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Self-health monitoring and wearable neurotechnologies
Abstract
Brain-computer interfaces and wearable neurotechnologies are now used to measure real-time neural and physiologic signals from the human body and hold immense potential for advancements in medical diagnostics, prevention, and intervention. Given the future role that wearable neurotechnologies will likely serve in the health sector, a critical state-of-the-art assessment is necessary to gain a better understanding of their current strengths and limitations. In this chapter we present wearable electroencephalography systems that reflect groundbreaking innovations and improvements in real-time data collection and health monitoring. We focus on specifications reflecting technical advantages and disadvantages, discuss their use in fundamental and clinical research, their current applications, limitations, and future directions. While many methodological and ethical challenges remain, these systems host the potential to facilitate large-scale data collection far beyond the reach of traditional research laboratory settings.
... MI is usually measured with electroencephalography (EEG) to register brain activity on the scalp surface. Thus, assessment and interpretation of MI brain dynamics in the sensorimotor cortex may contribute to applications ranging from evaluation of pathological conditions and rehabilitation of motor functions [1,2], motor learning and performance [3], improving the learning of different abilities [4], among others. In education scenarios, the Media and Information Literacy methodology proposed by the UNESCO covers several competencies that are vital for people to be effectively engaged in all aspects of human development [5]. ...
... Brain Informatics *Correspondence: dfcollazosh@unal.edu.co 1 Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia Full list of author information is available at the end of the article neuroscience discovery [10]. Nevertheless, for applications in MI tasks, designing an available end-to-end CNN architecture remains a challenge due to several restrictions: their large number of hyperparameters to be learned increase the computational burden (being unsuitable for online processing [11]), and complicated multilayer integration to encode relevant features at every abstraction level of the input EEG data [12]. ...
... The authors declare that they have no competing interests. 1 Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia. 2 Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales, Colombia. ...
Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms μ and β.
... While the use of neurotechnology to allow brain control of devices out of the laboratory (e.g., videogames, domotics or even rehabilitation) might still require several years of research and innovation to become a reality, ambulatory brain monitoring is a promising near-term medical application, for instance, for patient follow-up, seizure detection or sleep assessment (Cannard et al., 2020;Tseghai et al., 2020). In sleep studies, it could be used as a relatively cheap triage tool for patients that require a full polysomnographic study (Rundo and Downey, 2019). ...
... In addition to clinical and research applications, brain monitoring with garments could be easily adopted for neuroscience applications in education, wellness, sports or industrial environments, incorporating these measurements to the range of bio-signals that are currently acquired by smart watches, rings, or chest-bands (Peake et al., 2018;Cannard et al., 2020;Di Pasquale et al., 2022). ...
This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.
... Early in 2021, telehealth usage was 38 times greater than it was before the COVID-19 pandemic [5]. The emergence of a vast array of wearable technology in the field of neuro-and biotechnology, which provides real-time and continuous monitoring of physiologic as well as neurological activities, has been a catalyst in this shift towards remote healthcare [6][7][8][9][10][11][12]. The development of these wearable devices and smartphone applications has been identified as a clear step in assisting epilepsy patients to monitor the progression of their condition [13]. ...
... The subject was instructed to keep his eyes closed before and after flashing followed by flashing in the eye open condition at a frequency of 1 Hz for 10 s. The procedure was repeated with different frequencies of 3 Hz, 6 ...
Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG’s signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.
... Wearable EEG technologies make the collection of large datasets of diversified and under-represented populations more feasible and offer promising new applications for both clinicians and researchers in the long term (Cannard et al., 2020). These applications include brain monitoring in naturalistic settings and in real-time (Hu et al., 2015;Jebelli et al., 2017), brain-computer interfaces (BCI; Park et al., 2020), neurofeedback interventions (Angelakis et al., 2007;Quaedflieg et al., 2016;Brandmeyer and Delorme, 2020a), neuromarketing (Cartocci et al., 2018;Ramsøy et al., 2018), or neuroaesthetics research (i.e., the science studying the biological underpinnings of aesthetic experience; Cheung et al., 2019;Cartocci et al., 2021). ...
... Finally, advancements in wearable technologies may allow care providers to monitor patients and apply neurofeedback or neuromodulation protocols at a low cost and remotely while patients are in the comfort of their homes (Cannard et al., 2020;Biondi et al., 2021). ...
Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22–80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8–13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8–10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.
... In addition, the Inner Balance app is unique in its potential to enhance moral qualities of emotional literacy, empathy and deep heart wisdom. HeartMath and related studies reputedly affirm the value of the coherent heart influence on the human brain, social relationships and wider ecology (Cannard et al., 2020;Childre et al., 2016;Fournie et al., 2020;McCraty et al., 2018). ...
... Although AI creativity is continually improving as evident in wearable self-health technologies, ongoing monitoring is vital to ensure general interests, ethics and quality of health of the public (Cannard et al., 2020). Haselager and Mecacci (2020) argue that human beings need super-ethics before superintelligence and that AI should develop knowledge and the tools to improve ethical behaviour. ...
This Artificial Intelligence (AI) assisted, case study type investigation consisted of a review of Fitbit and Inner Balance application (app) records before and during the initial five-week South African COVID 19 lockdown period. Physical health activity consisted of various stillness and movement forms, ranging from subtle energetic, soft styles such as yoga, Pilates, Chi Gung and Tai Chi, through hard style resistance training with and without weights, to vigorous running and swimming. These were typically followed by HeartMath Inner Balance meditation sessions of 5 to 10 minutes in duration, which yielded quantitative coherence and achievement data as well as qualitative experiential descriptions of exercise and meditation. In general, the Covid-19 lockdown appeared to have been associated with improved physical health specifically concerning improved activity, sleep, coherence, resistance and resilience. The AI devices assisted in providing objective evaluations into such perennial ethical healing exhortations as self-knowledge and healing. Findings and implications are discussed.
... Anticipation methods have become especially relevant to developing non-invasive brain monitoring technology, as the opportunities to collect data for health purposes and monitoring are envisioned (Cinel et al., 2019, p. 16;Coates McCall & Wexler, 2020). Although the technology might not have yet reached a stage where it dramatically alters perspectives on the brain, potential opportunities and applications for future applications are envisioned in the literature (Balcombe & De Leo, 2022;Blandford, 2019;Cannard et al., 2020). A critical-hermeneutic perspective on the future might be helpful for combating the reifying power of futures, and the purpose is to deconstruct the futures that colonise the present (Urueña, 2023). ...
... Implementing Artificial Intelligence in healthcare is a compelling concept that has the potential to lead to major advances in accomplishing real-time and tailored treatment at lower costs [31]. Even though these systems face numerous methodological and ethical issues, they have the potential to permit large-scale data collecting far beyond the scope of typical research laboratory settings [32]. From deep learning to control of health management systems its informatics-driven techniques and active physician guidance in treatment decisions cannot be ignored [33]. ...
Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare.
... Besides medical BMIs, cognitively enhancing BMIs can also create reliance, possibly leading to the deterioration of cognitive functions they enhance [8]. None of the reviewed articles, however, propose any solution to this issue. ...
With their rapid development and huge potential, brain-machine interfaces (BMIs) will become one of the most important technologies in human society within decades. However, the ethical framework around this technology is far from mature. This paper seeks to summarize, analyze, and provide solutions to ethical issues associated with a particularly transformative family of BMIs - cognitive BMIs. 18 articles were included in this review through a structured article selection process. The discussion of the ethics of cognitive BMIs is divided into six topics: (i) individual cost-benefit balance; (ii) privacy and cybersecurity; (iii) autonomy, authenticity, and responsibility; (iv) equality; (v) cultural issues; (vi) military dual use. Within each topic, ethical issues that appeared in the reviewed articles are discussed, and solutions or directions for approaching them are given.
... Implementing Artificial Intelligence in healthcare, according to Ahmed et al. (2020), is a compelling concept that has the potential to lead to major advances in accomplishing real-time and tailored treatment at lower costs. Despite the fact that these systems face numerous methodological and ethical issues, they have the potential to permit large-scale data collecting far beyond the scope of typical research laboratory settings (Cannard et al., 2020). From deep learning to control of health management systems (including electronic health records) its informatics-driven techniques and active physician guidance in treatment decisions cannot be neglected (Hamet, 2017). ...
The presented doctoral thesis is titled “Augmenting Psychological Well-being using Artificial Intelligence: Reflections on Workplace Productivity”. It offers insight into how technology can be used to support psychological health. This study makes use of Healthcare IoT (IoMT) and Artificial Intelligence (A.I.) to fulfill the same. The study, with its interdisciplinary approach, focuses on the augmentation of psychosomatic health using A.I. and considers its impact on an individual to extend reflections on organizational performance. Health is an essential component of life. We take care of physical health, but mental health is usually taken for granted where it must be given the same care and importance. Psychosomatic health is nothing but a holistic reflection of both the physical and mental health of an individual. As per the pilot study, the root causes of the same are related to events relating to workplaces, finances, and relationships. As studies indicate, stress, anxiety, and depression are the signs of degrading mental health; considering service research priorities, the presented research empirically explores the impact of positive emotions on psychological well-being. Observing the complexity of neural constructs, Artificial Intelligence is deployed to be able to gain valuable insights. At the same time, keeping a managerial point of view in research reflects on co-created value in an organization achieved through a person’s well-being. Literature suggests that seeking therapy may be the only option while dealing with psychological issues, but it could be a time-consuming, expensive process with limited access to society. We do have technologies that have advanced over the last few decades but are mainly focused on supporting physical health. The presented study offers insight into how it can be used to support psychological health in the form of Machine Learning. The study reflects on EEG retrieved in the form of brain signals. Based on the adaption of research design termed as ‘Sequential Mixed Method,’ the study extends its application from the personal to a professional arena for enhancing workplace productivity. Research design includes experiments with predictive analytics and drives discussions using Qualitative and Quantitative data. Based on the information retrieved from the subjects - captured through a BCI and a survey questionnaire, a Machine Learning (ML) model was developed. In this study, we hypothesize that such treatment protocol can accelerate treatments by therapists for the betterment of Psychosomatic health. Not only that, but the use of the ML model can also offer greater scalability in reaching out to the masses for greater access. The well-being achieved can positively reflect on the individual. Through a comprehensive view, it would support a person in improving their personal and professional life. Ultimately given study suggests that the well-being achieved could further impact organizations, enhancing their overall performance as validated in the presented thesis. The contribution of this study was the interlinking of interdisciplinary domains such as Management - Technology and Healthcare. Also, this study uniquely utilized Data science due to the large size of the dataset that is collected in this study.
... Implementing artificial intelligence in healthcare is a compelling concept that has the potential to lead to major advances in accomplishing real-time and tailored treatment at lower costs [37]. Even though these systems face numerous methodological and ethical issues, they have the potential to permit large-scale data collecting far beyond the scope of typical research laboratory settings [38]. From deep learning to control of health management systems, its informatics-driven techniques and active physician guidance in treatment decisions cannot be ignored [39]. ...
Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare.
... These tools are non-invasive, mobile, and affordable and allow easy data collection in real-world settings with recent advancements in wearable technologies (Cannard et al., 2020; Dunn et al., 2018;Patel et al., 2012;Steinhubl et al., 2015). The Muse headset (InteraXon Inc.) has been validated for collecting EEG signals comparable to state-of-the-art systems (Cannard et al., 2021a;Krigolson et al., 2017), including relevant EEG metrics such as the alpha asymmetry and the individual alpha frequency (IAF), described in more detail below. ...
Machine learning (ML) is revolutionizing the field of biosignals processing and classification. Feature-based and feature-free (deep learning) ML perform equally well on MEEG (electroencephalography and magnetoencephalography) data classification problems. Yet, feature-based ML approaches dominantly rely on traditional, linear MEEG measures such as power spectral density, potentially missing useful information about the neural time series. There is a need for more advanced features that can capture the nonlinearity, complexity, and irregularities of such signals. Entropy-based measures are promising techniques to fill this gap but are difficult to apply to MEEG signals because of the variety of algorithms and parameters available, while user-friendly tools and methodological guidelines are lacking. Hence, we have developed a set of MEEG tools to fill that gap, where we gathered multiple entropy measures, tailored them for MEEG signal, and packaged them in an open-source EEGLAB plugin with a graphical user interface (GUI). We hope this newly accessible framework will help popularize the use of entropy measures with MEEG data, their cross-comparison, and their use as a feature for ML models.
... Furthermore, although fNIRS cortical hemodynamics have relatively slow responses around 1 Hz, their superior spatial resolution makes hybrid EEG-fNIRS solutions attractive for multi-modal recordings [51]. The introduction of lightweight and wearable hybrid systems listed in [154] has resolved earlier concerns about long setup time in surgical environments [155]. Likewise, slow taskevoked pupillary response with 0.5-1 s resolution, recorded by eye-tracking glasses or lenses attached to microsurgical equipment [143], can decode stress and workload without causing any discomfort for surgeons [62]. ...
Real-time mental stress monitoring from surgeons and surgical staff in operating rooms may reduce surgical injuries, improve performance and quality of medical care, and accelerate implementation of stress-management strategies. Motivated by the increase in usage of objective and subjective metrics for cognitive monitoring and by the gap in reviews of experimental design setups and data analytics, a systematic review of 71 studies on mental stress and workload measurement in surgical settings, published in 2001-2020, is presented. Almost 61% of selected papers used both objective and subjective measures, followed by 25% that only administered subjective tools - mostly consisting of validated instruments and customized surveys. An overall increase in the total number of publications on intraoperative stress assessment was observed from mid-2010 s along with a momentum in the use of both subjective and real-time objective measures. Cardiac activity, including heart-rate variability metrics, stress hormones, and eye-tracking metrics were the most frequently and electroencephalography (EEG) was the least frequently used objective measures. Around 40% of selected papers collected at least two objective measures, 41% used wearable devices, 23% performed synchronization and annotation, and 76% conducted baseline or multi-point data acquisition. Furthermore, 93% used a variety of statistical techniques, 14% applied regression models, and only one study released a public, anonymized dataset. This review of data modalities, experimental setups, and analysis techniques for intraoperative stress monitoring highlights the initiatives of surgical data science and motivates research on computational techniques for mental and surgical skills assessment and cognition-guided surgery.
... The basic idea of the protocol is to use a fixed-size contention window and select an appropriate transmission probability distribution for nodes at different time slots, so that different nodes that detect the same event can be within the contention window to send messages without conflict in each time slot. Cannard et al. [16] found the problem of excessive energy consumption by the boundary nodes of MAC virtual clusters is brought forward by algorithm, which effectively improves the network life of boundary nodes. Researchers are concerned with synchronous periodic music tone listening/sleeping mechanism. ...
Aiming at the problem of adaptive change of auxiliary music tones, this paper proposes a MAC protocol with a common music tone listening/sleeping type based on a wireless music buzzer sensor. First of all, the new MAC protocol adopts network-wide synchronization, and all sensor nodes in the entire network use the same scheduling table, so that the entire network nodes enter the music tone listening period and the sleep period at the same time. Secondly, the node adaptively adjusts the duty cycle of the node according to the number of data packets in the sending queue, increases the node’s music tone listening time, reduces the end-to-end delay of data packets, and improves the throughput of the network. Then, the experiment adopts a new backoff strategy to adjust the contention window according to the backoff times and collision times of data packets sent by nodes in the last five working cycles, increase the backoff time of sending data packets under high network load, and reduce the appearance of data packets. We build four simulation experiments on the NS2 simulation platform: unassisted music tone adaptive network, single auxiliary music tone adaptive network, auxiliary music tone adaptive convergence network, and random deployment network, which will be based on the auxiliary music tone adaptive MAC protocol, and IEEE802.11 protocol and SMAC protocol are run in four simulation experiments, respectively, and the performance of the three protocols is analyzed according to the tracking files in the simulation experiment. The analysis results show that the simulation wireless sounding buzzer sensor network is adaptive to different auxiliary music tones and different topologies.
... The data quality of such devices has proven of sufficient quality to collect EEG in various conditions, including continuous EEG [1] and event-related potentials [2]. Some of these wearable systems, like the Muse, use active electrodes similar to the one available on research-grade high-density EEG systems [3]. While the number of channels of wearable EEG systems remains limited (4 channels for the Muse), because of volume conduction, most EEG channels record activity from the whole brain. ...
... While the acquisition of these measures has not been validated using low-cost wearable systems against research-grade ones, such systems have been used extensively over the past few years to measure FAA, suggesting this measure is well-suited for these technologies [2], [37]- [46]. Wearable systems, when reliable, can offer advantages for researchers through easeful EEG data collection over large samples, increased access to populations that are hard to study with conventional systems (e.g., children, elderly, patients), reduced hardware and software costs, and facilitated EEG research in real-world environments by increasing subjects' mobility and streaming the data wirelessly [47]. ...
EEG power spectral density (PSD), the individual
alpha frequency (IAF) and the frontal alpha asymmetry (FAA)
are all EEG spectral measures that have been widely used to
evaluate cognitive and attentional processes in experimental and
clinical settings, and that can be used for real-world applications
(e.g., remote EEG monitoring, brain-computer interfaces,
neurofeedback, neuromodulation, etc.). Potential applications
remain limited by the high cost, low mobility, and long
preparation times associated with high-density EEG recording
systems. Low-density wearable systems address these issues and
can increase access to larger and diversified samples. The
present study tested whether a low-cost, 4-channel wearable
EEG system (the MUSE) could be used to quickly measure
continuous EEG data, yielding similar frequency components
compared to a research-grade EEG system (the 64-channel
BIOSEMI Active Two). MUSE data can be live-streamed using
the Lab Stream Layer (LSL), and can therefore be implemented
into real-world EEG monitoring, brain-computer interfaces
(BCI), or neurofeedback applications. We compare the spectral
measures from MUSE EEG data referenced to mastoids to those
from BIOSEMI EEG data with two different references for
validation (mastoids and average reference). A minimal amount
of data was deliberately collected to test the feasibility for realworld
applications (EEG setup and data collection being
completed in under 5 min). We show that the MUSE can be used
to examine power spectral density (PSD) in all frequency bands,
the individual alpha frequency (IAF), and frontal alpha
asymmetry (FAA). Furthermore, we observed satisfying
internal consistency reliability in alpha power and asymmetry
measures recorded with the MUSE. However, estimating
asymmetry on the IAF did not yield significant advantages
relative to the traditional method (average over the 8-13 Hz
range). These findings should advance human
neurophysiological monitoring using easily accessible wearable
neurotechnologies in large samples and increase the feasibility
of their implementation in real-world settings.
... While the acquisition of these measures has not been validated using low-cost wearable systems against research-grade ones, such systems have been used extensively over the past few years to measure FAA, suggesting this measure is well-suited for these technologies [2], [37]- [46]. Wearable systems, when reliable, can offer advantages for researchers through easeful EEG data collection over large samples, increased access to populations that are hard to study with conventional systems (e.g., children, elderly, patients), reduced hardware and software costs, and facilitated EEG research in real-world environments by increasing subjects' mobility and streaming the data wirelessly [47]. ...
EEG power spectral density (PSD), the individual alpha frequency (IAF) and the frontal alpha asymmetry (FAA) are all EEG spectral measures that have been widely used to evaluate cognitive and attentional processes in experimental and clinical settings, and that can be used for real-world applications (e.g., remote EEG monitoring, brain-computer interfaces, neurofeedback, neuromodulation, etc.). Potential applications remain limited by the high cost, low mobility, and long preparation times associated with high-density EEG recording systems. Low-density wearable systems address these issues and can increase access to larger and diversified samples. The present study tested whether a low-cost, 4-channel wearable EEG system (the MUSE) could be used to quickly measure continuous EEG data, yielding similar frequency components compared to research a grade EEG system (the 64-channel BIOSEMI Active Two). We compare the spectral measures from MUSE EEG data referenced to mastoids to those from BIOSEMI EEG data with two different references for validation. A minimal amount of data was deliberately collected to test the feasibility for real-world applications (EEG setup and data collection being completed in under 5 min). We show that the MUSE can be used to examine power spectral density (PSD) in all frequency bands, the individual alpha frequency (IAF; i.e., peak alpha frequency and alpha center of gravity), and frontal alpha asymmetry. Furthermore, we observed satisfying internal consistency reliability in alpha power and asymmetry measures recorded with the MUSE. Estimating asymmetry on PAF and CoG frequencies did not yield significant advantages relative to the traditional method (whole alpha band). These findings should advance human neurophysiological monitoring using wearable neurotechnologies in large participant samples and increase the feasibility of their implementation in real-world settings.
... Global Coherence and Inner Balance electronic applications (apps), served to measure meditation sessions. Although wearable self-health technologies are improving continually, ongoing monitoring of such technology is vital to ensure general ethics and quality of health of the public (Cannard et al. 2020). ...
International lockdown and social distancing as a response to COVID-19 indicate planetary interconnectedness. This South African case study compared global coherence, healing meditations using HeartMath Global Coherence and Inner Balance electronic applications(apps) before and during a 3-week lockdown period. Methodology integrated quantitative and qualitative components. Findings revealed significant meditation coherence and achievement increases and significant correlational cluster patterns between meditation data and global coherence increases, magnetometer readings. Local and global healing phenomena, dynamics, mechanisms and implications are discussed.
... With the advent of the Internet of Things (IOT), wearable technology is rapidly evolving for uses such as physiological and biomechanical monitoring, combining data from multiple real-time sensors. Enhancing such technology with brain-computer interface (BCI) concepts has further potential for healthcare applications ranging from monitoring emotions, stress or other visuomotor tracking in real-time [1][2][3]. BCI applications already provide innovative solutions for various types of patients to overcome difficulties [4][5][6][7]. There are several non-invasive BCI modalities like P300, motor imagery (MI) and steady state visual evoked potential (SSVEP). ...
Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditionally, SSVEP data is collected from the occipital region using electrodes with or without gel, normally mounted on a head cap. In this experimental study, we develop an in-ear electrode to collect SSVEP data for four different flicker frequencies and compare against occipital scalp electrode data. Data from five participants demonstrates the feasibility of in-ear electrode based SSVEP, significantly enhancing the practicability of wearable BCI applications.
... Using the proposed architecture, we were able to regulate one's emotional state, specifically emotional valence levels, by implementing a fuzzy controller that acted on a state-space model of the human brain. With a similar approach, a WMI could, in the future, be used to recommend a specific music track for a person feeling down, advise a change in lighting for someone in a bad mental state, or even offer a cup of green tea if the user wants to maintain a desired level of well-being (Athavale and Krishnan, 2017;Cannard et al., 2020). While we used experimental data to design a closed-loop system for regulating an internal valence state in a simulation study, a future direction of this research would be designing human subject experiments to close the loop in real-world settings. ...
Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.
O monitoramento remoto por meio de biossensores acoplados ao corpo humano durante atividade na doutrina militar é fundamental para o acompanhamento do estado de sáude do soldado. Sendo assim, o presente estudo teve como objetivo analisar a utilização de sensores fisiológicos acoplados ao corpo humano. Para responder esta finalidade foi utilizado o método de revisão integrativa e de análise de prospecção tecnológica. Após esta averiguação foi identificado um aumento tanto nas publicações quanto nos depósitos de patentes a partir do século XX. Nestas inserções de patentes os Estados Unidos da América detém o maior número de depósitos e de publicações. Contudo, um crescente campo de estudo e de inovação está associado aos sensores neurofisiológicos aplicados em demandas militares operacionais.
Purpose
This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.
Design/methodology/approach
In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.
Findings
This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.
Originality/value
The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.
This paper presents the first garment capable of measuring EEG activity with accuracy comparable to state-of-the art dry EEG systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) fully implemented as a garment, using threads, fabrics and smart textiles, without relying on any metal or plastic materials. The garment is interfaced via a connector to a mobile EEG amplifier to complete the measurement system. The new EEG system (Garment-EEG) has been characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) electrophysiological measurements (spontaneous and evoked EEG activity); (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more prone to getting affected by artifacts in adverse recording conditions due to poorer contact impedances. Ergonomics and comfort favor the textile-based sensor layer with respect to its metal-based counterpart. User acceptance is the main obstacle for EEG systems to democratize neurotechnology and non-invasive brain-computer interfaces. EEG sensor layers encapsulated in wearables have the potential to enable neurotechnology that is naturally accepted by people in their daily lives. Furthermore, by supporting the EEG implementation in the textile industry it is manufactured with lower cost and much less pollution compared to the metal and plastic industries.
Education of students is associated with classroom environment in which consist of such as settlement order, airconditioning , furniture, size of classroom and lighting color. The latter effectiveness on attention and meditation of students may not be measured through a survey simultaneously. Nowadays, attention and meditation levels of a students can be extracted from their brainwaves using brainwave detectors. In this study, attention and meditation levels are extracted from the observed brainwaves of randomly selected two students when changing classroom lighting colors in the Department of Electrical and Energy Classroom of Uşak University. The result shows that effectiveness of different classroom lighting colors are measured analyzed and evaluated toward students' attention and meditation levels simultaneously.
Drowsy driving poses a dangerous threat to road safety. This study aims to compare and evaluate the accuracy afforded by different modalities in assessing drowsiness, including: electroencephalogram (EEG), eye tracker, photoplethysmogram (PPG), and video recording. The work also investigates the impact of two parameters in the driving tasks on subjects' level of alertness, presence of road-mark and frequency of lane-departure events. Ten healthy subjects without sleep deprivation participated in individual 1.5-hour experiments. A threshold of 80 th percentile of reaction time was taken as ground truth to label data as Drowsy or Non-Drowsy. Using supervised learning random forest algorithm and stratified 10-fold cross validation, the results suggest that EEG features achieved highest classification accuracy: 0.957 and 0.864 for individual and combined sessions respectively, followed by eye tracker (0.821, 0.755 respectively). The highest accuracy of all modalities fell in the section that has the longest reaction time. These experiments additionally show that an absence of road-marks does indeed increase subjects' reaction time, though they may not necessarily become drowsier. Further, low frequency of lane-departure events did make subjects drowsier as hypothesized. Since existing commercial products that claim to detect drowsiness are very expensive and target at vehicle transportation companies only, they are not available to daily private car users. As such, this type of study deserves attention so that drowsiness detection products could be made affordable and accessible to both professional drivers and daily private car users.
Objective:
To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.
Methods:
Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28-79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.
Results:
Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.
Conclusion:
The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.
Current HCI research overlooks an opportunity to create human-machine interaction within the unique cognition ongoing during dreams and drowsiness. During sleep onset, a window of opportunity arises in the form of Hypnagogia, a semi-lucid sleep state where we begin dreaming before we fall fully unconscious. To access this state, we developed Dormio, the first interactive interface for sleep, designed for use across levels of consciousness. Here we present evidence for a first use case, directing dream content to augment human creativity. The system enables future HCI research into Hypnagogia, extending interactive technology across levels of consciousness.
Background:
People with autism spectrum disorder (ASD) commonly experience symptoms related to attention-deficit/hyperactivity disorder (ADHD), including hyperactivity, inattention, and impulsivity. One-third of ASD cases may be complicated by the presence of ADHD. Individuals with dual diagnoses face greater barriers to accessing treatment for ADHD and respond less positively to primary pharmacologic interventions. Nonpharmacologic technology-aided tools for hyperactivity and inattention in people with ASD are being developed, although research into their efficacy and safety remains limited.
Objective:
The objective of this preliminary study was to describe the changes in ADHD-related symptoms in children, adolescents, and young adults with ASD immediately after use of the Empowered Brain system, a behavioral and social communication aid for ASD running on augmented reality smartglasses.
Methods:
We recruited 8 children, adolescents, and young adults with ASD (male to female ratio of 7:1, mean age 15 years, range 11.7-20.5 years) through a Web-based research signup form. The baseline score on the hyperactivity subscale of the Aberrant Behavioral Checklist (ABC-H), a measure of hyperactivity, inattention, and impulsivity, determined their classification into a high ADHD-related symptom group (n=4, ABC-H≥13) and a low ADHD-related symptom group (n=4, ABC-H<13). All participants received an intervention with Empowered Brain, where they used smartglasses-based social communication and behavioral modules while interacting with their caregiver. We then calculated caregiver-reported ABC-H scores at 24 and 48 hours after the session.
Results:
All 8 participants were able to complete the intervention session. Postintervention ABC-H scores were lower for most participants at 24 hours (n=6, 75%) and for all participants at 48 hours (n=8, 100%). At 24 hours after the session, average participant ABC-H scores decreased by 54.9% in the high ADHD symptom group and by 20% in the low ADHD symptom group. At 48 hours after the session, ABC-H scores compared with baseline decreased by 56.4% in the high ADHD symptom group and by 66.3% in the low ADHD symptom group.
Conclusions:
This study provides initial evidence for the possible potential of the Empowered Brain system to reduce ADHD-related symptoms, such as hyperactivity, inattention, and impulsivity, in school-aged children, adolescents, and young adults with ASD. This digital smartglasses intervention can potentially be targeted at a broader array of mental health conditions that exhibit transdiagnostic attentional and social communication deficits, including schizophrenia and bipolar disorder. Further research is required to understand the clinical importance of these observed changes and to conduct longitudinal studies on this intervention with control groups and larger sample sizes.
Recent research has shown that auditory closed-loop stimulation can enhance sleep slow oscillations (SO) to improve N3 sleep quality and cognition. Previous studies have been conducted in lab environments. The present study aimed to validate and assess the performance of a novel ambulatory wireless dry-EEG device (WDD), for auditory closed-loop stimulation of SO during N3 sleep at home. The performance of the WDD to detect N3 sleep automatically and to send auditory closed-loop stimulation on SO were tested on 20 young healthy subjects who slept with both the WDD and a miniaturized polysomnography (part 1) in both stimulated and sham nights within a double blind, randomized and crossover design. The effects of auditory closed-loop stimulation on delta power increase were assessed after one and 10 nights of stimulation on an observational pilot study in the home environment including 90 middle-aged subjects (part 2).The first part, aimed at assessing the quality of the WDD as compared to a polysomnograph, showed that the sensitivity and specificity to automatically detect N3 sleep in real-time were 0.70 and 0.90, respectively. The stimulation accuracy of the SO ascending-phase targeting was 45 ± 52°. The second part of the study, conducted in the home environment, showed that the stimulation protocol induced an increase of 43.9% of delta power in the 4 s window following the first stimulation (including evoked potentials and SO entrainment effect). The increase of SO response to auditory stimulation remained at the same level after 10 consecutive nights. The WDD shows good performances to automatically detect in real-time N3 sleep and to send auditory closed-loop stimulation on SO accurately. These stimulation increased the SO amplitude during N3 sleep without any adaptation effect after 10 consecutive nights. This tool provides new perspectives to figure out novel sleep EEG biomarkers in longitudinal studies and can be interesting to conduct broad studies on the effects of auditory stimulation during sleep.
Introduction
There is strong evidence suggesting detrimental effects of cortical spreading depolarization (CSD) in patients with acute ischemic stroke and severe traumatic brain injury. Previous studies implicated scalp electroencephalography (EEG) features to be correlates of CSD based on retrospective analysis of EEG epochs after having detected “CSD” in time aligned electrocorticography. We studied the feasibility of CSD detection in a prospective cohort study with continuous EEG in 18 patients with acute ischemic stroke and 18 with acute severe traumatic brain injury.
Methods
Full band EEG with 21 silver/silver chloride electrodes was started within 48 h since symptom onset. Five additional electrodes were used above the infarct. We visually analyzed all raw EEG data in epochs of 1 h. Inspection was directed at detection of the typical combination of CSD characteristics, i.e., (i) a large slow potential change (SPC) accompanied by a simultaneous amplitude depression of >1Hz activity, (ii) focal presentation, and (iii) spread reflected as appearance on neighboring electrodes with a delay.
Results
In 3,035 one-hour EEG epochs, infraslow activity (ISA) was present in half to three quarters of the registration time. Typically, activity was intermittent with amplitudes of 40–220 µV, approximately half was oscillatory. There was no specific spatial distribution. Relevant changes of ISA were always visible in multiple electrodes, and not focal, as expected in CSD. ISA appearing as “SPC” was mostly associated with an amplitude increase of faster activities, and never with suppression. In all patients, depressions of spontaneous brain activity occurred. However, these were not accompanied by simultaneous SPC, occurred simultaneously on all channels, and were not focal, let alone spread, as expected in CSD.
Conclusion
With full band scalp EEG in patients with cortical ischemic stroke or traumatic brain injury, we observed various ISA, probably modulating cortical excitability. However, we were unable to identify unambiguous characteristics of CSD.
We have conducted an observational study on persons participating passively in public lectures. During a lecture we were measuring the level of focus of listeners using the Muse EEG-headband as well as conducting an observational study of the usage of the device by experiment participants. The purpose was to understand to what extent commercially available portable EEG-devices can record synchronicity of experience among the audience. While we got some preliminary insights, we found that the usefulness in measuring EEG signal of consumer-grade devices such as Muse is extremely limited in non-laboratory conditions.
This research directly assesses older people’s neural activation in response to a changing urban environment while walking, as measured by electroencephalography (EEG). The study builds on previous research that shows changes in cortical activity while moving through different urban settings. The current study extends this methodology to explore previously unstudied outcomes in older people aged 65 years or more (n = 95). Participants were recruited to walk one of six scenarios pairing urban busy (a commercial street with traffic), urban quiet (a residential street) and urban green (a public park) spaces in a counterbalanced design, wearing a mobile Emotiv EEG headset to record real-time neural responses to place. Each walk lasted around 15 min and was undertaken at the pace of the participant. We report on the outputs for these responses derived from the Emotiv Affectiv Suite software, which creates emotional parameters (‘excitement’, ‘frustration’, ‘engagement’ and ‘meditation’) with a real-time value assigned to them. The six walking scenarios were compared using a form of high dimensional correlated component regression (CCR) on difference data, capturing the change between one setting and another. The results showed that levels of ‘engagement’ were higher in the urban green space compared to those of the urban busy and urban quiet spaces, whereas levels of ‘excitement’ were higher in the urban busy environment compared with those of the urban green space and quiet urban space. In both cases, this effect is shown regardless of the order of exposure to these different environments. These results suggest that there are neural signatures associated with the experience of different urban spaces which may reflect the older age of the sample as well as the condition of the spaces themselves. The urban green space appears to have a restorative effect on this group of older adults.
Objective:
Hearing threshold levels have been estimated successfully in the clinic using the objective EEG-based technique of auditory steady state response (ASSR). The recent method of ear-EEG could enable ASSR hearing tests to be made in everyday life, rather than in a specialized clinic, enabling cheaper and easier monitoring of audiometric thresholds over time. The objective of the current study was to evaluate the feasibility of ear-EEG in audiometric characterization of auditory sensitivity thresholds.
Methods:
An ear-EEG setup was used to estimate ASSR hearing threshold levels to CE-Chirp stimuli (with center frequencies 0.5, 1, 2 and 4 kHz) from 4 different electrode configurations including conventional scalp configuration, ear electrode with scalp reference, ear electrode with reference in the opposite ear and ear electrode with reference in the same ear. To evaluate the ear-EEG setup, ASSR thresholds estimated using ear-EEG were compared to ASSR thresholds estimated using standardized audiological equipment.
Results:
The SNRs of in-ear ear-EEG recordings were found to be on average 2.7 to 6.5 dB lower than SNRs of conventional scalp EEG. Thresholds estimated from in-ear referenced ear-EEG were on average 15.0±3.4, 9.1±4.4, 12.5±3.7, and 12.1±2.6 dB above scalp EEG thresholds for 0.5, 1, 2 and 4 kHz, respectively.
Conclusion:
We demonstrate that hearing threshold levels can be estimated from ear-EEG recordings made from electrodes placed in one ear.
Significance:
Objective hearing threshold estimation based on ear-EEG can be integrated into hearing aids, thereby allowing hearing assessment to be performed by the hearing instrument on a regular basis.
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders and is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). In this paper, we introduce the first end-to-end deep learning approach that performs automatic temporal sleep stage classification from multivariate and multimodal Polysomnography (PSG) signals. We build a general deep architecture which can extract information from EEG, EOG and EMG channels and pools the learnt representations into a final softmax classifier. The architecture is light enough to be distributed in time in order to learn from the temporal context of each sample, namely previous and following data segments. Our model, which is unique in its ability to learn a feature representation from multiple modalities, is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields state-of-the-art performance. Our study reveals a number of insights on the spatio-temporal distribution of the signal of interest: a good trade-off for optimal classification performance measured with balanced accuracy is to use 6 EEG with some EOG and EMG channels. Also exploiting one minute of data before and after each data segment to be classified offers the strongest improvement when a limited number of channels is available. Our approach aims to improve a key step in the study of sleep disorders. As sleep experts, our system exploits the multivariate and multimodal character of PSG signals to deliver state-of-the-art classification performance at a very low complexity cost.
Over the past ten years there has been a rapid increase in the number of portable electroencephalographic (EEG) systems available to researchers. However, to date, there has been little work validating these systems for event-related potential (ERP) research. Here we demonstrate that the MUSE portable EEG system can be used to quickly assess and quantify the ERP responses associated with visuospatial attention. Specifically, in the present experiment we had participants complete a standard “oddball” task wherein they saw a series of infrequently (targets) and frequently (control) appearing circles while EEG data was recorded from a MUSE headband. For task performance, participants were instructed to count the number of target circles that they saw. After the experiment, an analysis of the EEG data evoked by the target circles when contrasted with the EEG data evoked by the control circles revealed two ERP components – the N200 and the P300. The N200 is typically associated with stimulus/perceptual processing whereas the P300 is typically associated with a variety of cognitive processes including the allocation of visuospatial attention [1]. It is important to note that the physical manifestation of the N200 and P300 ERP components differed from reports using standard EEG systems; however, we have validated that this is due to the quantification of these ERP components at non-standard electrode locations. Importantly, our results demonstrate that a portable EEG system such as the MUSE can be used to examine the ERP responses associated with the allocation of visuospatial attention.
Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.
Today, due to technology development and aversive events of daily life, Human exposure to both radiofrequency and stress is unavoidable. This study investigated the co-exposure to repeated restraint stress and WiFi signal on cognitive function and oxidative stress in brain of male rats. Animals were divided into four groups: Control, WiFi-exposed, restrained and both WiFi-exposed and restrained groups. Each of WiFi exposure and restraint stress occurred 2 h (h)/day during 20 days. Subsequently, various tests were carried out for each group, such as anxiety in elevated plus maze, spatial learning abilities in the water maze, cerebral oxidative stress response and cholinesterase activity in brain and serum. Results showed that WiFi exposure and restraint stress, alone and especially if combined, induced an anxiety-like behavior without impairing spatial learning and memory abilities in rats. At cerebral level, we found an oxidative stress response triggered by WiFi and restraint, per se and especially when combined as well as WiFi-induced increase in acetylcholinesterase activity. Our results reveal that there is an impact of WiFi signal and restraint stress on the brain and cognitive processes especially in elevated plus maze task. In contrast, there are no synergistic effects between WiFi signal and restraint stress on the brain.
A novel musical instrument and biofeedback device was created using electroencephalogram (EEG) posterior dominant rhythm (PDR) or mu rhythm to control a synthesized piano, which we call the Encephalophone. Alpha-frequency (8–12 Hz) signal power from PDR in the visual cortex or from mu rhythm in the motor cortex was used to create a power scale which was then converted into a musical scale, which could be manipulated by the individual in real time. Subjects could then generate different notes of the scale by activation (event-related synchronization) or de-activation (event-related desynchronization) of the PDR or mu rhythms in visual or motor cortex, respectively. Fifteen novice normal subjects were tested in their ability to hit target notes presented within a 5-min trial period. All 15 subjects were able to perform more accurately (average of 27.4 hits, 67.1% accuracy for visual cortex/PDR signaling; average of 20.6 hits, 57.1% accuracy for mu signaling) than a random note generation (19.03% accuracy). Moreover, PDR control was significantly more accurate than mu control. This shows that novice healthy individuals can control music with better accuracy than random, with no prior training on the device, and that PDR control is more accurate than mu control for these novices. Individuals with more years of musical training showed a moderate positive correlation with more PDR accuracy, but not mu accuracy. The Encephalophone may have potential applications both as a novel musical instrument without requiring movement, as well as a potential therapeutic biofeedback device for patients suffering from motor deficits (e.g., amyotrophic lateral sclerosis (ALS), brainstem stroke, traumatic amputation).
Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG.
Neurophysiological correlates of affective experience could potentially provide continuous information about a person’s experience when cooking and tasting food, without explicitly verbalizing this. Such measures would be helpful to understand people’s implicit food preferences and choices. This study examined for the first time the relation between neurophysiological variables and affective experiences under real cooking and tasting circumstances, using ingredients that were a priori expected to evoke different affective reactions. 41 participants cooked and tasted two stir-fry dishes in random order following an identical, strictly timed protocol. Once the main ingredient was chicken and the other time mealworms. EEG, ECG and skin potential were recorded continuously. Participants scored subjective valence and arousal after each cooking and tasting session. Frontal EEG alpha asymmetry showed the expected effect throughout the whole cooking and tasting session, consistent with ‘approach’ motivation for chicken and ‘avoidance’ for mealworms. Skin potential effects differed between cooking intervals but were in the expected direction. ECG variables showed an interaction with order of cooking the different dishes. Based on EEG alpha asymmetry, ECG and skin potential variables, we can estimate with 82% accuracy whether a single participant is preparing a dish with mealworms or with chicken. Our study provides evidence that it is possible to estimate experienced emotion during real-life cooking and tasting. We argue that it is important to consider that different neurophysiological and subjective measures reflect different underlying affective processes, to map them out more precisely, and to take advantage of these differences.
There is a growing body of multidisciplinary research on how robotic systems can be deployed in education and training by providing personalized tutoring session to the user. Socially Assistive Robotics (SAR) is an efficient tool for educational and health-care purposes. In this work, we present our SAR system for personalized and adaptive cognitive training. More specifically, we present the sequence learning task that provides measures for executive function assessment, which may indicate learning or even behavior disabilities in children. This work outlines the designing and evaluation process of such a system, including data collection and analysis. The long-term goal of this research is to develop interactive machine learning methods towards the design of an adaptive SAR system that provides a personalized training session by adjusting the session parameters and the robot's behavior to maximize user engagement and performance.
In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system—one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t-tests of component existence (all p's < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts.
Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place.
There are concerns about mental wellbeing in later life in older people as the global population becomes older and more urbanised. Mobility in the built environment has a role to play in improving quality of life and wellbeing, as it facilitates independence and social interaction. Recent studies using neuroimaging methods in environmental psychology research have shown that different types of urban environments may be associated with distinctive patterns of brain activity, suggesting that we interact differently with varying environments. This paper reports on research that explores older people’s responses to urban places and their mobility in and around the built environment. The project aim was to understand how older people experience different urban environments using a mixed methods approach including electroencephalography (EEG), self-reported measures, and interview results. We found that older participants experience changing levels of “excitement”, “engagement” and “frustration” (as interpreted by proprietary EEG software) whilst walking between a busy built urban environment and an urban green space environment. These changes were further reflected in the qualitative themes that emerged from transcribed interviews undertaken one week post-walk. There has been no research to date that has directly assessed neural responses to an urban environment combined with qualitative interview analysis. A synergy of methods offers a deeper understanding of the changing moods of older people across time whilst walking in city settings.
The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the Electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency (SEF) and multi- scale fuzzy entropy (MSFE), a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5 % to 95.2 % for ear-EEG labels predicted from ear-EEG, and 76.8 % to 91.8 % for scalp-EEG labels predicted from ear-EEG. The corresponding kappa coefficients, which range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2, indicate a Substantial to Almost Perfect agreement, thus proving the feasibility of in-ear sensing for sleep monitoring in the community.
Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.
For decades, electroencephalography (EEG) has been a useful tool for investigating the neural mechanisms underlying human psychological processes. However, the amount of time needed to gather EEG data means that most laboratory studies use relatively small sample sizes. Using the Muse, a portable and wireless four-channel EEG headband, we obtained EEG recordings from 6029 subjects 18-88 years in age while they completed a category exemplar task followed by a meditation exercise. Here, we report age-related changes in EEG power at a fine chronological scale for δ, θ, α, and β bands, as well as peak α frequency and α asymmetry measures for both frontal and temporoparietal sites. We found that EEG power changed as a function of age, and that the age-related changes depended on sex and frequency band. We found an overall age-related shift in band power from lower to higher frequencies, especially for females. We also found a gradual, year-by-year slowing of the peak α frequency with increasing age. Finally, our analysis of α asymmetry revealed greater relative right frontal activity. Our results replicate several previous age-and sex-related findings and show how some previously observed changes during childhood extend throughout the lifespan. Unlike previous age-related EEG studies that were limited by sample size and restricted age ranges, our work highlights the advantage of using large, representative samples to address questions about developmental brain changes. We discuss our findings in terms of their relevance to attentional processes and brain-based models of emotional well-being and aging.
Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1–1 Hz or 1–3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.
One outstanding question in the contemplative science literature relates to the direct impact of meditation experience on the monitoring of internal states and its respective correspondence with neural activity. In particular, to what extent does meditation influence the awareness, duration and frequency of the tendency of the mind to wander. To assess the relation between mind wandering and meditation, we tested 2 groups of meditators, one with a moderate level of experience (non-expert) and those who are well advanced in their practice (expert). We designed a novel paradigm using self-reports of internal mental states based on an experiential sampling probe paradigm presented during ~1 h of seated concentration meditation to gain insight into the dynamic measures of electroencephalography (EEG) during absorption in meditation as compared to reported mind wandering episodes. Our results show that expert meditation practitioners report a greater depth and frequency of sustained meditation, whereas non-expert practitioners report a greater depth and frequency of mind wandering episodes. This is one of the first direct behavioral indices of meditation expertise and its associated impact on the reduced frequency of mind wandering, with corresponding EEG activations showing increased frontal midline theta and somatosensory alpha rhythms during meditation as compared to mind wandering in expert practitioners. Frontal midline theta and somatosensory alpha rhythms are often observed during executive functioning, cognitive control and the active monitoring of sensory information. Our study thus provides additional new evidence to support the hypothesis that the maintenance of both internal and external orientations of attention may be maintained by similar neural mechanisms and that these mechanisms may be modulated by meditation training.
A multimodal embodied interface for 3D navigation was designed as a modular wearable. The user is suspended with a harness controlled by a mechanical Motion Base. This allows both physical and virtual displacement within an immersive virtual environment. Through a combination of passive and active modalities, users are enabled to fly at their own will.
Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain in a not invasive manner, with low cost hardware, using wireless communication and with high temporal resolution. Serious Games (SG) have demonstrated their effectiveness as a therapeutic resource to deal with motor, sensory and cognitive disabilities. We have considered the combination of both techniques for attention assessment in children with cerebral palsy (CP). In this short paper we present a new approach for the development of a SG based on a Brain Computer Interface (BCI).
Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that affects 1 out of 5 Colombian children, converting into a real public health problem in the country. Conventional treatments such as medication and neuropsychological therapy have been proved to be insufficient in order to decrease high incidence levels of ADHD in the principal Colombian cities. This work presents a design and development of a videogame that uses a brain computer interface not only to serve as an input device but also as a tool to monitor neurophysiologic signal. The video game named “The Harvest Challenge” puts a cultural scene of a Colombian coffee grower in its context, where a player can use his/her avatar in three mini games created in order to reinforce four fundamental aspects: i) waiting ability, ii) planning ability, iii) ability to follow instructions and iv) ability to achieve objectives. The details of this collaborative designing process of the multimedia tool according to the exact clinic necessities and the description of interaction proposals are presented through the mental stages of attention and relaxation. The final videogame is presented as a tool for sustained attention training in children with ADHD using as an action mechanism the neuromodulation of Beta and Theta waves through an electrode located in the central part of the front lobe of the brain. The processing of an electroencephalographic signal is produced automatically inside the videogame allowing to generate a report of the theta/beta ratio evolution — a biological marker, which has been demonstrated to be a sufficient measure to discriminate of children with and without deficit.
One important aspect in non-invasive brain–computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.
Over the past ten years there has been a rapid increase in the numberof portable electroencephalographic (EEG) systems available to researchers.However, to date, there has been little work validating these systems forevent-related potential (ERP) research. Here we demonstrate that the MUSEportable EEG system can be used to quickly assess and quantify the ERPresponses associated with visuospatial attention. Specifically, in the presentexperiment we had participants complete a standard“oddball”task wherein theysaw a series of infrequently (targets) and frequently (control) appearing circleswhile EEG data was recorded from a MUSE headband. For task performance,participants were instructed to count the number of target circles that they saw.After the experiment, an analysis of the EEG data evoked by the target circleswhen contrasted with the EEG data evoked by the control circles revealed twoERP components–the N200 and the P300. The N200 is typically associatedwith stimulus/perceptual processing whereas the P300 is typically associatedwith a variety of cognitive processes including the allocation of visuospatialattention [1]. It is important to note that the physical manifestation of the N200and P300 ERP components differed from reports using standard EEG systems;however, we have validated that this is due to the quantification of these ERPcomponents at non-standard electrode locations. Importantly, our resultsdemonstrate that a portable EEG system such as the MUSE can be used toexamine the ERP responses associated with the allocation of visuospatialattention
Technology, when successfully integrated in a classroom environment can help redefine and facilitate the role of the teacher. Classroom orchestration is an approach to Technology Enhanced Learning that emphasizes attention to the challenges of classroom use of technology, with a particular focus on supporting teachers’ roles. The automatic detection of learners’ cognitive profiles is an important step towards adaptive learning, where the learning material are adapted to match that of the learners in order to enhance the learning outcome. Electroencephalogram (EEG) is a methodology that monitors the electric activity in the brain. It has been utilized in several applications including, for example, detecting the subject’s emotional and cognitive states. In this paper, an approach for detecting two basic cognitive skills that affect learning using EEG signals is proposed. These skills include focused attention and working memory. The proposed approach consists of the following main steps. First, subjects undergo a cognitive assessment test that stimulates and measures their full cognitive profiles while putting on a 14-channel wearable EEG headset. Second, only the scores of the two cognitive skills aforementioned above are extracted and used to encode the two targets for a classification problem. Third, the collected EEG data are analyzed and a number of time and frequency-domain features are extracted. Fourth, several classifiers were trained to be able to correctly classify and predict three levels (low, average, and high) of the measured cognitive skills. The classification accuracies that were obtained for the focused attention and working memory were 90% and 87%, respectively, which indicates the suitability of the proposed approach for the detection of these two skills. This could be used as a first step towards adaptive learning where adaptation is to be done according to the predicted levels of focused attention and working memory.
Modern cars can support their drivers by assessing and autonomously performing different driving maneuvers based on information gathered by in-car sensors. We propose that brain–machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this paper, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the drivers' intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation, and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single-trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-of-the-art BMIs. We foresee that neural fusion correlates with information extracted from other physiological measures, e.g., eye movements or electromyography as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus, keeping the user in the loop and allowing him to fully enjoy the driving experience.
Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects (p=0.31). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection, and could catalyze further explorations and developments of many other real-world BCI applications.
The field of sleep is in many ways ideally positioned to take full advantage of advancements in technology and analytics that is fueling the mobile health movement. Combining hardware and software advances with increasingly available big datasets that contain scored data obtained under gold standard sleep laboratory conditions completes the trifecta of this perfect storm. This review highlights recent developments in consumer and clinical devices for sleep, emphasizing the need for validation at multiple levels, with the ultimate goal of using personalized data and advanced algorithms to provide actionable information that will improve sleep health.
Traffic accidents remain one of the most critical issues in many countries. One of the major causes of traffic accidents is drowsiness while driving. Since drowsiness is related to human physiological conditions, drowsiness is hard to prevent. Several studies have been conducted in assessing drowsiness, especially in a driving environment. One of the common methods used is the electroencephalogram (EEG). It is known that drowsiness occurs in the central nervous system; thus, estimating drowsiness using EEG is the promising way to assess drowsiness accurately. In this study, we tried to estimate drowsiness using frequency-domain and time-domain analysis of EEG. To validate the physiological conditions of the subjects, the Karolinska sleepiness scale (KSS), a subject-based assessment of drowsiness condition; and an examiner-based assessment known as facial expression evaluation (FEE) were applied. Three categories were considered; alert (KSS <; 6; FEE <; 1), weak drowsiness (KSS 6-7; FEE 1-2) and strong drowsiness (KSS > 7; FEE > 2). The six parameters (absolute and relative power of alpha, ratio of β/α and (θ+α)/β, and Hjorth activity and mobility parameters) had statistically significant differences between the three drowsiness conditions (P <; 0.001). By using both KSS and FEE, these parameters showed high accuracy in detecting drowsiness (up to 92.9%). Taken together, we suggest that EEG parameters can be used in detecting the three drowsiness conditions in a simulated driving environment.
Fatigue is one of the causes of falling asleep at the wheel, which can result in fatal accidents. Thus, it is necessary to have practical fatigue detection solutions for drivers. In literature, electroencephalography (EEG) along with the surrogate measure of reaction time (RT) has been used to develop fatigue detection algorithms. However, these solutions are often based upon wet multi-channel EEG electrodes which are not feasible or practical for drivers. Using dry electrodes and headband like designs would be better. Hence, this study aims to investigate the correlation of EEG log bandpower against RT via a Muse headband which has dry frontal EEG electrodes. 31 subjects underwent an hour-long driving simulation experiment with car deviation events. Based on the video and EEG data, 5 `Sleepy' and 5 `Alert' subjects are identified and analyzed. A differential signal between Fp1 and Fp2 is computed so as to remove the effects of eye blinks, and is analyzed for correlation with RT. Significant positive correlation is found for log delta (1-4 Hz) bandpower, and significant negative correlations for log theta (4-8 Hz) and alpha (8-12 Hz) bandpowers, but the positive correlation of log beta (12-30 Hz) bandpower with RT is not significant. This is a good first step towards building a practical fatigue detection solution for drivers in the future.
Background: Neurofeedback, a type of biofeedback, is an operant conditioning treatment that has been studied for use in the treatment of traumatic brain injury (TBI) in both civilian and military populations. In this approach, users are able to see or hear representations of data related to their own physiologic responses to triggers, such as stress or distraction, in real time and, with practice, learn to alter these responses in order to reduce symptoms and/or improve performance. Objective: This article provides a brief overview of the use of biofeedback, focusing on neurofeedback, for symptoms related to TBI, with applications for both civilian and military populations, and describes a pilot study that is currently underway looking at the effects of a commercial neurofeedback device on patients with mild-to-moderate TBIs. Conclusions: Although more research, including blinded randomized controlled studies, is needed on the use of neurofeedback for TBI, the literature suggests that this approach shows promise for treating some symptoms of TBI with this modality. With further advances in technology, including at-home use of neurofeedback devices, preliminary data suggests that TBI survivors may benefit from improved motivation for treatment and some reduction of symptoms related to attention, mood, and mindfulness, with the addition of neurofeedback to treatment.
This study compared the performance of a low-cost wireless EEG system to a research-grade EEG system on an auditory oddball task designed to elicit N200 and P300 ERP components. Participants were 15 healthy adults (6 female) aged between 19 and 40 (M = 28.56; SD = 6.38). An auditory oddball task was presented comprising 1,200 presentations of a standard tone interspersed by 300 trials comprising a deviant tone. EEG was simultaneously recorded from a modified Emotiv EPOC and a NeuroScan SynAmps RT EEG system. The modifications made to the Emotiv system included attaching research grade electrodes to the Bluetooth transmitter. Additional modifications enabled the Emotiv system to connect to a portable impedance meter. The cost of these modifications and portable impedance meter approached the purchase value of the Emotiv system. Preliminary analyses revealed significantly more trials were rejected from data acquired by the modified Emotiv compared to the SynAmps system. However, the ERP waveforms captured by the Emotiv system were found to be highly similar to the corresponding waveform from the SynAmps system. The latency and peak amplitude of N200 and P300 components were also found to be similar between systems. Overall, the results indicate that, in the context of an oddball task, the ERP acquired by a low-cost wireless EEG system can be of comparable quality to research-grade EEG acquisition equipment.
Sleep is an important aspect of our health, but it is difficult for people to track manually because it is an unconscious activity. The ability to sense sleep has aimed to lower the barriers of tracking sleep. Although sleep sensors are widely available, their usefulness and potential to promote healthy sleep behaviors has not been fully realized. To understand people's perspectives on sleep sensing devices and their potential for promoting sleep health, we surveyed 87 and interviewed 12 people who currently use or have previously used sleep sensors, interviewed 5 sleep medical experts, and conducted an in-depth qualitative analysis of 6986 reviews of the most popular commercial sleep sensing technologies. We found that the feedback provided by current sleep sensing technologies affects users' perceptions of their sleep and encourages goals that are in tension with evidence-based methods for promoting good sleep health. Our research provides design recommendations for improving the feedback of sleep sensing technologies by bridging the gap between expert and user goals.
Increasingly, there is a trend to measure brain activity in more ecologically realistic scenarios. Normally, the confines of the laboratory and sedentary tasks mitigate sources of electrical noise on EEG measurement. Moving EEG outside of the lab requires understanding of the impact of complex movements and activities on traditional EEG and ERP measures. Here, we recorded EEG with active electrodes while participants were either riding or sitting on a stationary bike in an electrical and sound-attenuated chamber in the lab. Participants performed an auditory oddball task, pressing a button when they detected rare target tones in a series of standard frequent tones. We quantified both the levels of spectral, single-trial baseline, and ERP baseline noise, as well as classic MMN/N2b and P3 ERP components measured during both biking and sitting still. We observed slight increases in posterior high frequency noise in the spectra, and increased noise in the baseline period during biking. However, morphologically and topographically similar MMN/N2b and P3 components were measured reliably while both biking and sitting. A quantification of the power to reliably measure ERPs as a function of the number of trials revealed slight increases in the number of trials needed during biking to achieve the same level of power. Taken in sum, our results confirm that classic ERPs can be measured reliably during biking activities in the lab. Future directions will employ these techniques outside the lab in ecologically valid situations.
Virtual reality presents exciting new prospects for the delivery of educational materials to students. By combining this technology with biological sensors, a student in a virtual educational environment can be monitored for physiological markers of engagement or more cognitive states of learning. With this information, the virtual reality environment can be adaptively altered to reflect the student's state, essentially creating a closed-loop feedback system. This paper explores these concepts, and presents preliminary data on a combined EEG-VR working memory experiment as a first step toward a broader implementation of an intelligent adaptive learning system. This first-pass neural time-series and oscillatory data suggest that while an EEG-based neurofeedback system is feasible, more work on removing artifacts and identifying relevant and important features will lead to higher prediction accuracy.
Background:
Electroencephalography (EEG) experiments are typically performed in controlled laboratory settings to minimise noise and produce reliable measurements. These controlled conditions also reduce the applicability of the obtained results to more varied environments and may limit their relevance to everyday situations.
New method:
Advances in computer portability may increase the mobility and applicability of EEG results while decreasing costs. In this experiment we show that stimulus presentation using a Raspberry Pi 2 computer provides a low cost, reliable alternative to a traditional desktop PC in the administration of EEG experimental tasks.
Results:
Significant and reliable MMN and P3 activity, typical event-related potentials (ERPs) associated with an auditory oddball paradigm, were measured while experiments were administered using the Raspberry Pi 2. While latency differences in ERP triggering were observed between systems, these differences reduced power only marginally, likely due to the reduced processing power of the Raspberry Pi 2.
Comparison with existing method:
An auditory oddball task administered using the Raspberry Pi 2 produced similar ERPs to those derived from a desktop PC in a laboratory setting. Despite temporal differences and slight increases in trials needed for similar statistical power, the Raspberry Pi 2 can be used to design and present auditory experiments comparable to a PC.
Results:
Our results show that the Raspberry Pi 2 is a low cost alternative to the desktop PC when administering EEG experiments and, due to its small size and low power consumption, will enable mobile EEG experiments unconstrained by a traditional laboratory setting.
Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli. Furthermore, the technology has not yet been tested and validated for ecologically relevant auditory stimuli such as speech. In this study, Ear-EEG and conventional scalp EEG were recorded simultaneously in a discrete-tone as well as a continuous-speech design. The discrete stimuli were applied in a dichotic oddball paradigm, while continuous stimuli were presented diotically as two simultaneous talkers. Cross-correlation of stimulus envelope and Ear-EEG was assessed as a measure of ongoing neural tracking. The extracted ERPs from Ear-EEG revealed typical auditory components yet depended critically on the reference electrode chosen. Reliable neural-tracking responses were extracted from the Ear-EEG for both paradigms, albeit weaker in amplitude than from scalp EEG. In conclusion, this study shows the feasibility of extracting relevant neural features from ear-canal-recorded "Ear-EEG", which might augment future hearing technology.