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Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review
Abstract
Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.
Universiti Sains Malaysia has started the Big Brain Data Initiative project since the last two years as brain mapping techniques have proven to be important in understanding the molecular, cellular and functional mechanisms of the brain. This Big Brain Data Initiative can be a platform for neurophysicians and neurosurgeons, psychiatrists, psychologists, cognitive neuroscientists, neurotechnologists and other researchers to improve brain mapping techniques. Data collection from a cohort of multiracial population in Malaysia is important for present and future research and finding cure for neurological and mental illness. Malaysia is one of the participant of the Global Brain Consortium (GBC) supported by the World Health Organization. This project is a part of its contribution via the third GBC goal which is influencing the policy process within and between high-income countries and low- and middle-income countries, such as pathways for fair data-sharing of multi-modal imaging data, starting with electroencephalographic data.
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.
Electroencephalogram(EEG) is done in several ways, which are referred to as brainwaves, which scientists interpret as an electromagnetic phenomenon that reflects the activity in the human brain, this study is used to diagnose brain diseases such as schizophrenia, epilepsy, Parkinson's, Alzheimer's, etc. It is also used in brain machine interfaces and in brain computers. In these applications wireless recording is necessary for these waves. What we need today is Authentication? Authentication is obtained from several techniques, in this paper we will check the efficiency of these techniques such as password and pin. There are also biometrics techniques used to obtain authentication such as heart rate, fingerprint, eye mesh and sound, these techniques give acceptable authentication. If we want to get a technology that gives us integrated and efficient authentication, we use brain wave recording. The aim of the technique in our proposed paper is to improve the efficiency of the reception of radio waves in the brain and to provide authentication.
A military lifestyle can have profound impacts on an individual’s health and wellbeing. Increasingly, new technologies such as the creation of Virtual Reality (VR) are being explored as bridging mecha-nisms to provide ‘space’ and to aid with other therapies. The overarching research programme investi-gates the therapeutic and social qualities of landscape and how these can be translated into an immersive virtual environment. There is a specific focus regarding immersive VR environments and how these could be used as a tool to promote positive health, wellbeing, and social connection within the New Zealand Defence Force (NZDF).
Measuring brain activity outside the laboratory is of great importance for investigating human behavior under naturalistic conditions (e.g., in cognition and movement research, application of brain-computer interfaces). To measure neuronal activity in moving subjects, only modified NIRS and EEG systems are applicable. Because conventional EEG systems are too sensitive to movement artifacts, artifact sources should be eliminated beforehand to improve signal quality. Four different approaches for EEG/ERP measures with moving subjects were tested in case studies: (i) a purpose-built head-mounted preamplifier, (ii) a laboratory system with active electrodes, (iii)+(iv) a wireless headset combined with (iii) passive or (iv) active electrodes. A standard visual oddball task was applied during rest (without movement) and during ergometer rowing. All 14 measures revealed very similiar (within subjects) visual evoked potentials for rowing and rest. The small intraindividual differences between rowing and rest, in comparison to the typically larger interindividual differences in the ERP waveforms revealed that ERPs can be measured reliably even in an athletic movement like rowing. The expected modulation of the motor-related activity by force output, on the other hand, was largely affected by movement artifacts. Therefore, for a successful application of ERP measures in movement research, further developments to differentiate between movement-related neuronal activity and movement-related artifacts are required. However, it cannot be excluded that activities with small magnitudes related to motor learning and motor control cannot be detected because they are superimposed by the very large motor potential which increases with force output.
Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.
The modern developments in neural science like neuroimaging and neuro sensing technologies have increase the size of neurological data, rate of neurological data generation, and variation in neuroscience data. These are vital role players for “Neuroscience Big data”. For statistically informative datasets in terms of size, with greater time scale and colossal number of attributes, the Neuroscience community for research can develop varied type of experiments using such data [1].With the development of many data driven research techniques, the understanding for complex neurological disorder can be advance. It will also help in the field of brain networks with model long term effects of brain injury. Tools for neuroinformatics data processing and analysing are available but they are not capable to bring about huge volume of neuroscience data, which makes it hard for researchers to advance their work due to lack of capably control over this available data. So in this paper we have analysed mainly three big data techniques like map reduce, spark and pig to check their most suitability in the field of neuroscience test. Analysis shows that out of this three methods NeuroPigPen method explained in [1] is the best to deal with the challenges raised by large-scale electrophysiological signal data.
A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.
In recent years, the virtual reality technology has been greatly developed and applied to many fields, its application extends to product design, education and training, military and aerospace, entertainment and leisure fields. This article summarizes the current developments in hardware and software of virtual reality technology and its application in the military field, and then analyzes the future development trends of the application of virtual reality in military.
Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.
The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing metal electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. This chapter overviews the fundamental basis of the EEG, the typical signals that are produced and how they are collected and analysed. Significant attention is given to reviewing the state of the art in EEG collection in both electrode designs and instrumentation hardware. In particular, recent developments in ear-EEG and in conformal tattoo electrodes for very long-term monitoring are highlighted. The chapter concludes by overviewing the applications of EEG technology in medical and non-medical domains, demonstrating the emergence of “consumer neuroscience” applications as EEG devices become more available and more readily useable by non-specialist operators.
The discovery of electroencephalography (EEG) over a century ago has changed the way we understand brain structure and function, in terms of both clinical and research applications. This paper starts with a short description of EEG and then focuses on the event-related brain potentials (ERPs), and their use in experimental settings. It describes the typical set-up of an ERP experiment. A description of a number of ERP components typically involved in language research is presented. Finally, the advantages and disadvantages of using ERPs in language research are discussed. EEG has an extensive use in today’s world, including medical, psychology, or linguistic research. The excellent temporal resolution of EEG information allows one to track a brain response in milliseconds and therefore makes it uniquely suited to research concerning language processing.
Brain Computer Interface (BCI) systems are the devices which are proposed to help the disabled, people who are incapable of making motor response to communicate with computer using brain signal. The aim of BCI is to interpret brain activity into digital form which acts as a command for a computer. One key challenge in current BCI research is how to extract features of random time-varying EEG signals and its classification as accurately as possible. Feature extraction techniques are used to extract the features which represent a unique property obtained from pattern of brain signal. Earlier EEG analysis was restricted to visual inspection only. The visual inspection of the signal is very subjective and hardly allows any standardization or statistical analysis. Hence, several different techniques were intended in order to quantify the information of the brain signal. Many linear and non-linear methods for feature extraction exist. The purpose of this paper is to give a brief introduction to the EEG signals and BCI system. The paper also includes a review on the conventional methods that are used for feature extraction of the signal.
Adolf Beck, born in 1863 in Kraków (Poland), joined the department of Physiology of the Jagiel-lonian University in 1889, to work directly under the prominent professor in physiology napoleon cybulski. Following his suggestion, Beck started studies on the electrical brain activity of animals. he recorded negative electrical potentials in several brain areas evoked by peripheral sensory im-pulses. Using this technique, Beck localised various centres in the brain of several animal species. in doing this, he discovered continuous electrical oscillations in the electrical brain activity and noted that these oscillations ceased after sensory stimulation. this was the first description of de-synchronisation in electrical brain potentials. he published these findings in 1890 in the german Centralblatt für Physiologie. immediately, an intense discussion arose under physiologists on the question who could claim being the founder of electroencephalography. Ultimately, richard ca-ton from liverpool showed that he had performed similar experiments in monkeys years earlier. nevertheless, Beck added several new elements to the nature of electrical brain activity, such as evoked potentials and desynchronisation. in looking back, Adolf Beck can be regarded, next to richard caton and together with hans Berger (who later introduced the electrical brain recording method to humans), as one of the founders of electroencephalography
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Previous studies that involve measuring EEG, or electroencephalograms, have mainly been experimentally-driven projects; for instance, EEG has long been used in research to help identify and elucidate our understanding of many neuroscientific, cognitive, and clinical issues (e.g., sleep, seizures, memory). However, advances in technology have made EEG more accessible to the population. This opens up lines for EEG to provide more information about brain activity in everyday life, rather than in a laboratory setting. To take advantage of the technological advances that have allowed for this, we introduce the Brain-EE system, a method for evaluating user engaged enjoyment that uses a commercially available EEG tool (Muse). During testing, fifteen participants engaged in two tasks (playing two different video games via tablet), and their EEG data were recorded. The Brain-EE system supported much of the previous literature on enjoyment; increases in frontal theta activity strongly and reliably predicted which game each individual participant preferred. We hope to develop the Brain-EE system further in order to contribute to a wide variety of applications (e.g., usability testing, clinical or experimental applications, evaluation methods, etc.).
Although much progress has been made in relating brain activations to choice behavior, evidence that neural measures could actually be useful for predicting the success of marketing actions remains limited. To be of added value, neural measures should significantly increase predictive power, beyond conventional measures. In the present study, the authors obtain both stated preference measures and neural measures (electroencephalography; EEG) in response to advertisements for commercially released movies (i.e., movie trailers) to probe their potential to provide insight into participants' individual preferences as well as movie sales in the general population. The results show that EEG measures (beta and gamma oscillations), beyond stated preference measures, provide unique information regarding individual and population-wide preference and can thus, in principle, be used as a neural marker for commercial success. As such, these results provide the first evidence that EEG measures are related to real-world outcomes and that these neural measures can significantly add to models predicting choice behavior relative to models that include only stated preference measures.
The first decade of consumer neuroscience research has produced groundbreaking work in identifying the basic neural processes underlying human judgment and decision making, with the majority of such studies published in neuroscience journals and influencing models of brain function. Yet for the field of consumer neuroscience to thrive in the next decade, the current emphasis on basic science research must be extended into marketing theory and practice. The authors suggest five concrete ways that neuroscientific methods can be fruitfully applied to marketing. They then outline three fundamental challenges facing consumer neuroscientists and offer potential solutions for addressing them. The authors conclude by describing how consumer neuroscience can become an important complement to research and practice in marketing.
Advanced noninvasive neuroimaging techniques such as EEG and fMRI allow researchers to directly observe brain activities while subjects perform various perceptual, motor, and/or cognitive tasks. By combining functional brain imaging with sophisticated experimental designs and data analysis methods, functions of brain regions and their interactions can be examined. A nascent field called neuroeconomics has recently emerged as a result of the enormous success of applications of functional brain imaging techniques in the study of human decision-making. In this article, we first provide an overview of brain imaging techniques, focusing on the recent developments in multivariate analysis and multi-modal data integration. We then present several studies on risky decision making, intertemporal choice, and social decision making, to illustrate how neuroimaging techniques can be used to advance our knowledge on decision making. Finally, we discuss challenges and future directions in neuroeconomics.
The discovery of electroencephalography (EEG) in 1929 by the German psychiatrist Hans Berger was a historical breakthrough providing a new neurologic and psychiatric diagnostic tool at the time, especially considering the lack of all those now available in daily practice (EP, CT, MRI, DSA, etc.) whithout which the making of neurologic diagnosis and planning neurosurgical operative procedures would now be unconceivable. There are no recent reports on the topic in the Croatian medical literature. The methods used in the study included search through previous reports, bibliographic notes, Internet sources, and analysis of continuous scientific attempts made through centuries to discover the real nature and meaning of electrical activity. Galvani's accidental discovery of "biological electricity" led to Volta's discovery of the battery (voltaic pile). Using it, Rolando was the first to stimulate cerebral surface. Thus, enabling Fritsch and Hitzig and Ferrier to develop the idea of cerebral localization (Jackson, Gowers, Gotch and Horsley). It was understandable that brain electrical stimulation produces contralateral motor response, but it was unknown whether there was a spontaneous (intrinsic) brain electrical current that could be recorded. Caton was the first to report on the "current in the brain gray substances onto open brain. Based on Caton's discovery and of those of Beck, Danilevsky, Prawdicz-Neminsky and others, Berger made the first EEG (electrocorticogram) recording on July 6, 1924, during a neurosurgical operation on a 17-year-old boy, performed by the neurosurgeon Nikolai Guleke. He reported on the topic in 1929, using the terms alpha and beta waves. The "spike and waves" (Spitzenwellen) were described shortly thereafter by the American group of EEG pioneers (H. and P. Davies, F. and E. Gibbs, Lenox and Jasper), although Berger had also observed them but considered them artifacts. The discovery of electroencephalography was a milestone for the advancement of neuroscience and of neurologic and neurosurgical everyday practice, especially for patients with seizures. The real nature of the disease and its management (anticonvulsants, excision of brain scars, tumors, etc.) were unkonown at that time. Berger's persistent, hardworking and steady personal style overcame all technical and other obstacles during the experiments. Unfortunately, he gained neither acceptance nor recognition, among his fellow contemporaries from abroad. Political turmoils at the dawn of World War II, in the country of Nazi's ideology and finally the outbreak of war, along with the complete ban of any further work on EEG after his forced retirement, led him to an uneasy professional and personal end. In the era when lumbar puncture, pneumoencephalography and ventriculography were the only diagnostic tools to detect and localize "sick sites" in the brain, EEG revolutionized daily neurologic and neurosurgic procedures, and bridged a time period of about 40 years (1930-1970) until the advent of computer tomography. Nowadays its importance is not as great as it was before, but it still has its place in the diagnostic work-up of seizures, brain tumors, degenerative brain changes, and other diseases.
Brain-computer interfaces (BCI) use electrophysiological measures of brain functions to send inputs to a computer from a new non-muscular channel. In this entry, the use of electroencephalography (EEG) is introduced as a BCI input for virtual reality (VR). A VR minigame is developed to showcase the use of EEG as an input in VR. With EEG, using the predefined brainwave patterns that are defined via EEG as a set of commands, users can interact with their environments in VR dynamically, through the changes in their emotional mood and concentration. This does not only introduce an alternative input for VR, in which the physical body’s integration is somewhat restricted due to the technological limitations, but also enables authentic digital realm experiences which would be impossible in the physical world.
Purpose:
It is unknown which patient education strategy before epilepsy surgery or stereotactic electrode implantation is best for patients. This prospective and randomized clinical study investigates whether the use of the mixed reality tool "VSI Patient Education" (VSI PE) running on HoloLens® glasses is superior to the use of a rubber brain model as a 3-dimensional tool for patient education before epilepsy surgery and stereotactic electrode implantation.
Material and methods:
17 patients with indication for epilepsy surgery or stereotactic electrode implantation were included in the study and randomized into two groups. All patients were informed with both comparative tools VSI PE (apoQlar®) and a rubber brain model (3B Scientific®) in a chronological order depending on group assignment. Afterwards, the patient and, if present, a relative (12) each filled out a questionnaire. For statistical analysis, Wilcoxon rank-sum tests were performed.
Results:
Patients found their patient education highly significantly more comprehensible (p = 0.001**, r = 0.84) and almost significantly more imaginable (p=0.020, r = 0.57), when their doctor used VSI PE compared to the rubber brain model. The patients felt significantly less anxious as a result of VSI PE (p = 0.008*, r = 0.64). Highly significantly more patients chose VSI PE as the preferred patient education tool (p < 0.001**, r = 0.91), and almost significantly more patients decided VSI PE to be the future standard tool (p = 0.020, r = 0.56). Significantly more relatives chose VSI PE as the preferred patient education tool (p = 0.004*, r = 0.83), and significantly more relatives decided VSI PE to be the future standard tool (p = 0.002*, r = 0.91).
Conclusion:
VSI Patient Education is a promising new mixed reality tool for informing patients before epileptic surgery or stereotactic electrode implantation in order to enhance comprehension and imagination and reduce fear and worries. It might strengthen patient commitment and have a positive influence on patients' decisions in favor of medically indicated surgical operations.
Purpose
This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of electroencephalogram (EEG) in consumer neuroscience.
Design/methodology/approach
In addition to the review of papers using EEG to study consumer cognitive processes, the authors also discuss relevant decisions and considerations in conducting event-related potential (ERP) studies. Further, a framework proposed by Plassmann et al. (2015) was used to discuss the applications of EEG in marketing research from papers reviewed.
Findings
This paper successfully used Plassmann et al.’s (2015) framework to discuss five applications of neuroscience to marketing research. A review of growing EEG studies in the field of marketing and other interdisciplinary fields reveals the advantages and potential of using EEG in combination with other methods. This calls for more research using such methods.
Research limitations/implications
A technical overview of ERP-related terminology provides researchers with a background for understanding and reviewing ERP studies. A discussion of method-related considerations and decisions provides marketing researchers with an introduction to the method and refers readers to relevant literature.
Practical implications
The marketing industry has been quick to adopt cutting edge technology, including EEG, to understand and predict consumer behavior for the purpose of improving marketing practices. This paper connects the academic and practitioner spheres by presenting past and potential EEG research that can be translatable to the marketing industry.
Originality/value
The authors review past literature on the use of EEG to study consumer-related topics in marketing and interdisciplinary fields, to demonstrate its advantages over-traditional methods in studying consumer-relevant behaviors. To foster increasing use of EEG in consumer neuroscience research, the authors further provide technical and marketing-specific considerations for both academic and market researchers. This paper is one of the first to review past EEG papers and provide methodological background insights for marketing researchers.
The electroencephalogram (EEG) signals are often mixed with several sources such as electrooculogram and electromyogram signals. Independent component analysis (ICA) is often used to separate the sources from the multichannel EEG signals. Recently, the use of portable EEG devices has gained much attention due to their low power consumption and the ability to record the EEG signals in home environment. However, these systems are equipped with single or few EEG channels, and hence the direct application of ICA is not possible. In this paper, we proposed an efficient technique to separate the sources from single-channel EEG signals by combining the singular spectrum analysis (SSA) and ICA techniques. In this technique, the single-channel EEG data are first decomposed into multivariate data using SSA. Later, ICA is applied on the multivariate data to extract the source signals. In order to validate the performance of the proposed technique, we carried out the simulation on synthetic and real life EEG signals. In addition, we have also studied the performance of the proposed technique for detecting the seizures. The performance measures of a seizure detection classifier such as receiver operating curve, true positive rate, and false positive rate are obtained and found that the proposed technique exhibits better performance compared with the existing methods.
Skilled advertisers often cause a diverse set of consumers to feel similarly about their product. We present a method for measuring neural data to assess the degree of similarity between multiple brains experiencing the same advertisements, and we demonstrate that this similarity can predict important marketing outcomes. Since neural data can be sampled continuously throughout an experience and without effort and conscious reporting biases, our method offers a useful complement to measures requiring active evaluations, such as subjective ratings and willingnessto- pay (WTP) scores. As a case study, we use portable electroencephalography (EEG) systems to record the brain activity of 58 moviegoers in a commercial theater and then calculate the relative levels of neural similarity, cross-brain correlation (CBC), throughout 13 movie trailers. Our initial evidence suggests that CBC predicts future free recall of the movie trailers and population-level sales of the corresponding movies. Additionally, since there are potentially other (i.e., non-neural) sources of physiological similarity (e.g., basic arousal), we illustrate how to use other passive measures, such as cardiac, respiratory, and electrodermal activity levels, to reject alternative hypotheses. Moreover, we show how CBC can be used in conjunction with empirical content analysis (e.g., levels of visual and semantic complexity).
Learning objectives After participating in this activity, learners should be better able to:
Abstract Virtual reality (VR) allows users to experience a sense of presence in a computer-generated, three-dimensional environment. Sensory information is delivered through a head-mounted display and specialized interface devices. These devices track head movements so that the movements and images change in a natural way with head motion, allowing for a sense of immersion. VR, which allows for controlled delivery of sensory stimulation via the therapist, is a convenient and cost-effective treatment. This review focuses on the available literature regarding the effectiveness of incorporating VR within the treatment of various psychiatric disorders, with particular attention to exposure-based intervention for anxiety disorders. A systematic literature search was conducted in order to identify studies implementing VR-based treatment for anxiety or other psychiatric disorders. This article reviews the history of the development of VR-based technology and its use within psychiatric treatment, the empirical evidence for VR-based treatment, and the benefits for using VR for psychiatric research and treatment. It also presents recommendations for how to incorporate VR into psychiatric care and discusses future directions for VR-based treatment and clinical research.
EEG is becoming increasingly important in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. The role of the EEG is to help physicians for establishing an accurate diagnosis. In neurology, a main diagnostic application of EEGs is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. In this chapter, we provide a brief discussion of various uses and the significance of EEGs in brain disorder diagnosis and also in brain-computer interface (BCI) systems. In this chapter, we also discuss why EEG signal analysis and classification are required for medical and health practice and research. Then, we provide the key concepts of EEG signal classification and a brief description of computer-aided diagnostic (CAD) systems.
In this paper, we proposed a method that classifies electroencephalography (EEG) from color imagination data using the Emotiv EPOC headset. For EEG measurement and the event-related potential (ERP) method, brain-computer interface (BCI) systems were used in the experiment. In the experiment, the subjects gaze at a non-flicker visual stimulus of color (i.e., red, green, blue, white, and yellow) and then proceed to imagine the color. To concentrate on the LED light, all experiments were performed in a dimly lit room. The flickered visual stimulus was made using an Arduino microcontroller board and LEDs with the purpose of prompting color imagination. As a result, we obtained significant EEG responses of thoughts related to certain colors. The EEG response is classified using classification algorithms including a support vector machine (SVM) with linear discriminant analysis (LDA), an artificial neural network (ANN) with LDA, and an ANN without LDA. In addition, five-fold cross validation was used to evaluate the performance. From the results, we found robust electrodes (T7 and F4). The technology developed in this paper can be used to assist paralyzed individuals and the elderly.
Continuous EEG monitoring: Principles and practice
- A M Husain
- S R Sinha
Husain, A. M., & Sinha, S. R. (2020). Continuous EEG monitoring: Principles and practice. Journal of Clinical
Neurophysiology, 37(3), 274-274. https://doi.org/10.1097/wnp.0000000000000571
Electroencephalography (EEG)
- G L Read
- I J Innis
Read, G. L., & Innis, I. J. (2017). Electroencephalography (EEG). In J. Matthes (Ed.), The international
encyclopedia of communication research methods (pp. 1-18). John Wiley & Sons, Inc. https://doi.
org/10.1002/9781118901731.iecrm0080