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Real-time Sensing and NeuroFeedback for Practicing Meditation Using simultaneous EEG and Eye Tracking
... We focus our attention on EEG-based brain-computer interfaces since they have a high temporal resolution, are relatively cheap, and are portable [30]. There are many EEG-based BCI paradigms such as the P300 [31], steady-state evoked potentials [32,33], and motor imagery/execution [34,35]. ...
... # Find index of band in frequency vector idx_band = np.logical_and(freqs >= low, freqs <= high) # Integral approximation of the spectrum using parabola (Simpson's rule) # Iterating over each subject for epoch in epochs:# Iterating over each song per subject[30] bands_video.append(bandpower_multitaper(input_brainwaves[k,:], sf=frequency, method='multitaper', band=[8,13], relative=False)) bands_video.append(bandpower_multitaper(input_brainwaves[k,:], ...
... sf=frequency, method='multitaper', band=[8,13], relative=False)) bands_video.append(bandpower_multitaper(input_brainwaves[k,:], sf=frequency, method='multitaper', band=[14,30], relative=False)) ...
Over 1 in 6 people around the world face significant disabilities, and
around half of them have limb disabilities. Brain-computer interfaces
using EEG signals can help us create paradigms for the rehabilitation
of patients with stroke, spinal cord injury, muscle degeneration, and so
on. This study aims to classify neural signatures of palm open vs. close
under varying hand positions. We chose this task as it is a fine motor
task, and loss of this functionality may impede one’s abilities to perform
activities of daily living (ADL). EEG data were collected on 15 participants,
sampled at 1000Hz. Participants were seated on a chair and
instructed to open or close either their left or right hand on cue from
PsychoPy. We used independent component activations as features for
the machine learning classifier and obtained significant within-subject
classification accuracy ranging from 67% to 79% for all subjects. We
trained one model for each of the components decomposed using ICA
and generated component maps of components with the highest accuracy.
Our findings reveal that components that originate from the
sensorimotor cortex are the best feature for the prediction of hand open
vs. close, which is consistent with the literature.
... State-of-the-art technology is beginning to address some of these challenges. For example, machine learning algorithms can be used to improve the quality of deciphering EEG and fNIRS signals by filtering out noise and identifying patterns that are relevant to mental health and wellbeing monitoring [4,5]. Newer EEG devices are also incorporating additional sensors, such as eye-tracking or heart rate monitors, to provide more comprehensive data and an inertial measurement unit (IMU) to measure and correct motion artifacts. ...
... The continued use of machine learning methods, for example, can be used to improve the quality of EEG and fNIRS data by filtering out noise and discovering patterns related to mental health. Newer EEG devices are also incorporating additional sensors, such as eye tracking [5] or heart rate monitors, to provide more comprehensive data. There are efforts currently underway to standardize data collection and analysis techniques, which will make it easier to compare results across research studies and create the best practices for clinical application. ...
... State-of-the-art technology is beginning to address some of these challenges. For example, machine learning algorithms can be used to improve the quality of deciphering EEG and fNIRS signals by filtering out noise and identifying patterns that are relevant to mental health and wellbeing monitoring [4,5]. Newer EEG devices are also incorporating additional sensors, such as eye-tracking or heart rate monitors, to provide more comprehensive data and an inertial measurement unit (IMU) to measure and correct motion artifacts. ...
... The continued use of machine learning methods, for example, can be used to improve the quality of EEG and fNIRS data by filtering out noise and discovering patterns related to mental health. Newer EEG devices are also incorporating additional sensors, such as eye tracking [5] or heart rate monitors, to provide more comprehensive data. There are efforts currently underway to standardize data collection and analysis techniques, which will make it easier to compare results across research studies and create the best practices for clinical application. ...
Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.
... Various non-invasive methods are used to record brain activity, such as EEG (Electroencephalography), fNIRS (functional near-infrared spectroscopy), etc. We prefer using EEG as they offer a high temporal resolution, are non-invasive, and are cost-effective [19,30]. EEG-BCI systems are used to classify MI signals, which are rhythmic oscillations of motor movement captured over the sensorimotor cortex within the mu and beta frequency bands [42]. ...
... Recently, there has been a surge in the development of machine learning models for meditation due to the availability of wearable EEG headsets for consumer use. Identifying differences between expert and non-expert have been in the rise of exploration using machine learning with signal processing techniques [10,[23][24][25][26][27]. Pre-and post-changes after a few weeks of practice are the quickest way to observe the effects with interpretability. ...
... Machine learning classifiers are trained as the most practical method for spotting differences due to their strong pattern learning capabilities. Moreover, there has been a surge in studies using machine learning to categorize meditation states in recent years Pandey et al., 2022;Pandey & Miyapuram, 2020;Pandey & Miyapuram, 2021a, 2021c. ...
Research into the similarities and differences between various forms of meditation practice is still in its early stages. Here, utilizing functional connectivity and graph measures, we present our work examining three meditation traditions: Himalayan Yoga (HT), Isha Shoonya (SNY), and Vipassana (VIP). EEG activity of the meditative block is used to build functional brain connections to exploit the resulting networks between various meditation traditions and a control group. Support vector machine is employed for binary classification, and models are built with features generated via graph theory measures. We obtain maximum accuracy of 84.76% with gamma1, 90% with alpha, and 84.76% with theta in HT, SNY, and VIP, respectively. Our key findings involve (a) higher delta connectivity in Vipassana meditators, (b) synchronization of theta networks in the left hemisphere inspected to be stronger in the anterior frontal area across meditators, (c) greater involvement of gamma2 processing observed among Himalayan and Vipassana meditators, (d) increased left frontal activity contribution for all meditators in theta and gamma bands, and (e) modularity engaged extensively in gamma processing across all meditation traditions. Furthermore, we discuss the implication of this research for neurotechnology products to enable guided meditation among naive practitioners.
In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchi's fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.
In this study, we examined EEG analysis techniques across a variety of meditation traditions in order to identify reliable metrics that could be applied in meditation research, revealing how each tradition interprets eeg signals distinctly. The study’s potential is in its future neurotechnological innovations, to increase usage of meditation (by enriching one’s internal environment for better equilibration) among users. EEG's high temporal resolution may result in misinterpretation or erroneous correlation if the analyzing technique is unreliable and weak. Overall, investigation of this may provide insight into the complex
nature of meditative practice and its impact on the brain. Methods: Neuroscientific studies demonstrate several methods from simple to complex, including spectral power, entropy (sample, lempel-ziv), and fractal dimensions (higuchi) etc. Distinguishing expert and naive practitioners involve large-scale dynamics of distinct cognitive and resource allocation across different meditation types. Meditation research includes a comparison of expert meditators with controls, Intervariability across meditation practitioners (Himalayan yoga, Vipassana, etc) with controls. There have been several studies conducted on the
effects of mind-wandering on brain rhythms and meditation practice among expert meditators and controls, using thought probes, instructed mind-wandering conditions. Results: Meditation-related EEG studies have shown power increases in theta and alpha bands and overall frequency slowing, with the occasional increase in gamma power. Large scale decrease in entropy (Lempel Ziv-Complexity) during meditation states was observed. Contrary to this finding, a study by Vivot et al., 2020 observed increased alpha and gamma power sample entropy across vipassana meditators. Decreased number of transient alphatheta 2:1 harmonic ratio during meditation conditions among 43 experienced meditators, was revealed. In one study, after Integrated Body-Mind Training (IBMT) of 24 participants, functional connectivity measures were performed. The IBMT group
exhibited a larger clustering coefficient, global and local efficiency, and shorter average path length at midline electrodes when compared to controls. Discussion: The generation of a neurophysiological marker that is reliable when a person is progressing
from one level of meditation to another is still in its infancy. Depending on how different meditation styles are studied and analyzed there might be both similarities and differences in the results, but rigorous scrutiny (preprocessing, experimental design, meditation type, feature extraction, etc.) will provide a better insight into generalizations.
Mindfulness meditation is a form of self-regulatory training for the mind and the body. The relationship between mindfulness meditation and musical aesthetic emotion processing (MAEP) remains unclear. This study aimed to explore the effect of temporary mindfulness meditation on MAEP while listening to Chinese classical folk instrumental musical works. A 2 [(groups: mindfulness meditation group (MMG); control group (CG)] × 3 (music emotions: calm music, happy music, and sad music) mixed experimental design and a convenience sample of university students were used to verify our hypotheses, which were based on the premise that temporary mindfulness meditation may affect MAEP (MMG vs. CG). Sixty-seven non-musically trained participants (65.7% female, age range: 18–22 years) were randomly assigned to two groups (MMG or CG). Participants in MMG were given a single 10-min recorded mindfulness meditation training before and when listening to music. The instruments for psychological measurement comprised of the Five Facet Mindfulness Questionnaire (FFMQ) and the Positive and Negative Affect Schedule (PANAS). Self-report results showed no significant between-group differences for PANAS and for the scores of four subscales of the FFMQ (p > 0.05 throughout), except for the non-judging of inner experience subscale. Results showed that temporary mindfulness meditation training decreased the negative emotional experiences of happy and sad music and the positive emotional experiences of calm music during recognition and experience and promoted beautiful musical experiences in individuals with no musical training. Maintaining a state of mindfulness while listening to music enhanced body awareness and led to experiencing a faster passage of musical time. In addition, it was found that Chinese classical folk instrumental musical works effectively induced aesthetic emotion and produced multidimensional aesthetic experiences among non-musically trained adults. This study provides new insights into the relationship between mindfulness and music emotion.
Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.
Objectives
EEG neurofeedback has potential to increase the effectiveness of mobile meditation applications by providing synchronous performance feedback to meditators. This crossover trial aimed to evaluate the effects of auditory EEG neurofeedback on state mindfulness during focused attention meditation—a putative mediator of mental health benefits—relative to no feedback.
Methods
Adult participants (N = 68, Mage = 22.66, SDage = 7.35) completed a task-based measure of state mindfulness while meditating with and without auditory feedback from a consumer-grade EEG headband. Participants rated subjective meditation experiences in each condition. A subgroup (n = 29) completed 14 days of home practice with the device and responded to open-ended questions about their experience.
Results
Auditory feedback was associated with greater state mindfulness (RR = 1.15, 95% CI [1.00, 1.29]). Device-measured mind wandering was lower when feedback was present (d = − 0.22 [− 0.07, − 0.37]), but there was a negligible effect on device-measured recoveries from mind wandering episodes (d = − 0.11 [− 0.30, 0.08]). Feedback was associated with quantitative differences in subjective experiences consistent with heightened arousal. Thematic analysis revealed helpful (active, guiding) and unhelpful (stressful, distracting, incongruent with subjective experience) aspects of feedback.
Conclusions
EEG neurofeedback appears to increase state mindfulness in adults during a brief meditation. These results support feedback as an effective adjunct to meditation. Psychoeducation regarding feedback and the meditative experience may help to maximise the beneficial effects. Replication of these findings in clinical populations is warranted.
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.
We address the hypothesis that the entropy of neural dynamics indexes the intensity and quality of conscious content. Previous work established that serotonergic psychedelics can have a dysregulating effect on brain activity, leading to subjective effects that present a considerable overlap with the phenomenology of certain meditative states. Here we propose that the prolonged practice of meditation results in endogenous increased entropy of brain oscillatory activity. We estimated the entropy of band-specific oscillations during the meditative state of traditions classified as 'focused attention' (Himalayan Yoga), 'open monitoring' (Vipassana), and 'open awareness' (Isha Shoonya Yoga). Among all traditions, Vipassana resulted in the highest entropy increases, predominantly in the alpha and low/high gamma bands. In agreement with previous studies, all meditation traditions increased the global coherence in the gamma band, but also stabilized gamma-range dynamics by lowering the metastability. Finally, machine learning classifiers could successfully generalize between certain pairs of meditation traditions based on the scalp distribution of gamma band entropies. Our results extend previous findings on the spectral changes observed during meditation, showing how long-term practice can lead to the capacity for achieving brain states of high entropy. This constitutes an example of an endogenous, self-induced high entropy state.
This paper proposes an unobtrusive and calibration-free framework towards eye gaze tracking based interactive directional control interface for desktop environment using simple webcam under unconstrained settings. The proposed eye gaze tracking involved hybrid approach designed by combining two different techniques based upon both supervised and unsupervised methods wherein the unsupervised image gradients method computes the iris centers over the eye regions extracted by the supervised regression based algorithm. Experiments performed by the proposed hybrid approach to detect eye regions along with iris centers over challenging face image datasets exhibited exciting results. Similar approach for eye gaze tracking worked well in real-time by using a simple web camera. Further, PC based interactive directional control interface based upon iris position has been designed that works without needing any prior calibrations unlike other Infrared illumination based eye trackers.
The proposed work may be useful to the people with full body motor disabilities, who need interactive and unobtrusive eye gaze control based applications to live independently.
Mindfulness meditation consists of focused attention meditation (FAM) and open monitoring meditation (OMM), both of which reduce activation of the default mode network (DMN) and mind-wandering. Although it is known that FAM requires intentional focused attention, the mechanisms of OMM remain largely unknown. To investigate this, we examined striatal functional connectivity in 17 experienced meditators (mean total practice hours = 920.6) during pre-resting, meditation, and post-resting states comparing OMM with FAM, using functional magnetic resonance imaging. Both FAM and OMM reduced functional connectivity between the striatum and posterior cingulate cortex, which is a core hub region of the DMN. Furthermore, OMM reduced functional connectivity of the ventral striatum with both the visual cortex related to intentional focused attention in the attentional network and retrosplenial cortex related to memory function in the DMN. In contrast, FAM increased functional connectivity in these regions. Our findings suggest that OMM reduces intentional focused attention and increases detachment from autobiographical memory. This detachment may play an important role in non-judgmental and non-reactive attitude during OMM. These findings provide new insights into the mechanisms underlying the contribution of OMM to well-being and happiness.
Mindfulness meditation is at present deemed also as form of mental training that may allow for empowering focusing, attention regulation, and executive control skills. Nonetheless, the potential of traditional mindfulness practice for improving cognitive and neural efficiency is affected by two critical requirements—intensity of exercise and perseverance to practice—which represent a known limitation of accessibility to meditation practices. It has been suggested that the impact of such limitations might be reduced thanks to the support of external devices. The present study aims at testing the efficacy of an intensive technology-mediated intervention based on mindful practices and supported by a brain-sensing device to optimize cognitive performance and neural efficiency. Forty participants took part in the study and were randomly divided in an active control and an experimental group. Both groups were involved in a structured intervention, which lasted 4 weeks and was constituted by brief daily activities. The experimental group, differently from the active control, underwent mindfulness-based practices with the support of a dedicated device. Analyses highlighted increased electrophysiological responsiveness indices at rest and frequency profiles consistent with a relaxed mindset in the experimental group. Participants in the experimental group also showed improved electrophysiological markers of attention regulation and improved cognitive performance, as measured by a complex reaction times task. Findings hint at the potential of the investigated technology-mediated mindfulness practice for enhancing cognitive performance and for inducing consistent modulations of neural efficiency markers.
Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.
Regular mindfulness practice benefits people both mentally and physically, but many populations who could benefit do not practice mindfulness. Virtual Reality (VR) is a new technology that helps capture participants’ attention and gives users the illusion of “being there” in the 3D computer generated environment, facilitating sense of presence. By limiting distractions from the real world, increasing sense of presence and giving people an interesting place to go to practice mindfulness, Virtual Reality may facilitate mindfulness practice. Traditional Dialectical Behavioral Therapy (DBT®) mindfulness skills training was specifically designed for clinical treatment of people who have trouble focusing attention, however severe patients often show difficulties or lack of motivation to practice mindfulness during the training. The present pilot study explored whether a sample of mindfulness experts would find useful and recommend a new VR Dialectical Behavioral Therapy (DBT®) mindfulness skills training technique and whether they would show any benefit. Forty four participants attending a mindfulness conference put on an Oculus Rift DK2 Virtual Reality helmet and floated down a calm 3D computer generated virtual river while listening to digitized DBT® mindfulness skills training instructions. On subjective questionnaires completed by the participants before and after the VR DBT® mindfulness skills training session, participants reported increases/improvements in state of mindfulness, and reductions in negative emotional states. After VR, participants reported significantly less sadness, anger, and anxiety, and reported being significantly more relaxed. Participants reported a moderate to strong illusion of going inside the 3D computer generated world (i.e., moderate to high “presence” in VR) and showed high acceptance of VR as a technique to practice mindfulness. These results show encouraging preliminary evidence of the feasibility and acceptability of using VR to practice mindfulness based on clinical expert feedback. VR is a technology with potential to increase computerized dissemination of DBT® skills training modules. Future research is warranted.
Despite decades of research, effects of different types of meditation on electroencephalographic (EEG) activity are still being defined. We compared practitioners of three different meditation traditions (Vipassana, Himalayan Yoga and Isha Shoonya) with a control group during a meditative and instructed mind-wandering (IMW) block. All meditators showed higher parieto-occipital 60–110 Hz gamma amplitude than control subjects as a trait effect observed during meditation and when considering meditation and IMW periods together. Moreover, this gamma power was positively correlated with participants meditation experience. Independent component analysis was used to show that gamma activity did not originate in eye or muscle artifacts. In addition, we observed higher 7–11 Hz alpha activity in the Vipassana group compared to all the other groups during both meditation and instructed mind wandering and lower 10–11 Hz activity in the Himalayan yoga group during meditation only. We showed that meditation practice is correlated to changes in the EEG gamma frequency range that are common to a variety of meditation practices.
While it has been suggested that loving-kindness meditation (LKM) is an effective practice for promoting positive emotions, the empirical evidence in the literature remains unclear. Here, we provide a systematic review of 24 empirical studies (N = 1759) on LKM with self-reported positive emotions. The effect of LKM on positive emotions was estimated with meta-analysis, and the influence of variations across LKM interventions was further explored with subgroup analysis and meta-regression. The meta-analysis showed that (1) medium effect sizes for LKM interventions on daily positive emotions in both wait-list controlled RCTs and non-RCT studies; and (2) small to large effect sizes for the on-going practice of LKM on immediate positive emotions across different comparisons. Further analysis showed that (1) interventions focused on loving-kindness had medium effect size, but interventions focused on compassion showed small effect sizes; (2) the length of interventions and the time spent on meditation did not influence the effect sizes, but the studies without didactic components in interventions had small effect sizes. A few individual studies reported that the nature of positive emotions and individual differences also influenced the results. In sum, LKM practice and interventions are effective in enhancing positive emotions, but more studies are needed to identify the active components of the interventions, to compare different psychological operations, and to explore the applicability in clinical populations.
Mind wandering is a ubiquitous phenomenon where attention involuntarily shifts from task-related thoughts to internal task-unrelated thoughts. Mind wandering can have negative effects on performance; hence, intelligent interfaces that detect mind wandering can improve performance by intervening and restoring attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudorandomly probed to report mind wandering while an eye tracker recorded their gaze during the reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from eye gaze and context features in a user-independent fashion. Mind wandering was detected with an accuracy of 72 % (expected accuracy by chance was 60 %) when probed at the end of a page and an accuracy of 67 % (chance was 59 %) when probed in the midst of reading a page. Global gaze features (gaze patterns independent of content, such as fixation durations) were more effective than content-specific local gaze features. An analysis of the features revealed diagnostic patterns of eye gaze behavior during mind wandering: (1) certain types of fixations were longer; (2) reading times were longer than expected; (3) more words were skipped; and (4) there was a larger variability in pupil diameter. Finally, the automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity. Possible improvements to the detector and applications that utilize the detector are discussed.
Meditation is becoming increasingly popular as a topic for scientific research and theories on meditation are becoming ever more specific. We distinguish between what is called focused Attention meditation, open Monitoring meditation, and loving kindness (or compassion) meditation. Research suggests that these meditations have differential, dissociable effects on a wide range of cognitive (control) processes, such as attentional selection, conflict monitoring, divergent, and convergent thinking. Although research on exactly how the various meditations operate on these processes is still missing, different kinds of meditations are associated with different neural structures and different patterns of electroencephalographic activity. In this review we discuss recent findings on meditation and suggest how the different meditations may affect cognitive processes, and we give suggestions for directions of future research.
A significant body of literature supports the contention that pupil size varies depending on cognitive load, affective state, and level of drowsiness. Here we assessed whether oculometric measures such as gaze position, blink frequency and pupil size were correlated with the occurrence and time course of self-reported mind-wandering episodes. We recorded the pupil size of two subjects engaged in a monotonous breath counting task while keeping their eyes on a fixation cross. This task is conducive to producing mind-wandering episodes. Each subject performed ten 20-min sessions, for total duration of about 4 h. Subjects were instructed to report spontaneous mind-wandering episodes by pressing a button when they lost count of their breath. After each button press, subjects filled in a short questionnaire describing the characteristics of their mind-wandering episode. We observed larger pupil size during the breath-focusing period compared to the mind-wandering period (p < 0.01 for both subjects). Our findings contradict previous research showing a higher baseline pupil size during mind wandering episodes in visual tasks. We discuss possible explanations for this discrepancy. We also analyzed nine other oculometric measures including blink rate, blink duration and gaze position. We built a support vector machine (SVM) classifier and showed that mean pupil size was the most reliable predictor of mind wandering in both subjects. The classification accuracy of mind wandering data segments vs. breath-focusing data segments was 81% for the first subject and 77% for the second subject. Additionally, we analyzed oculometric measures in light of the phenomenological data collected in the questionnaires. We showed that how well subjects remembered their thoughts while mind wandering was positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.05). Feelings of well being were also positively correlated with pupil size (subject 1, p < 0.001; subject 2, p < 0.001). Our results suggest that oculometric data could be used as a neurocognitive marker of mind-wandering episodes.
This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.
Multiple measures exist that examine the attentional aspects of meditation practice, but measurement of the compassion component
is relatively understudied. This paper describes the development and initial validation of a scale designed to measure application
of the four immeasurable qualities at the heart of Buddhist teachings: loving kindness, compassion, joy and acceptance toward
both self and others. Our analyses suggest four distinct subscales: positive qualities toward self, positive qualities toward
others, negative qualities toward self and negative qualities toward others. Initial examination of reliability and validity
showed high internal consistency for the subscales as well as strong concurrent, discriminant, and construct validity. We
believe the Self-Other Four Immeasurables (SOFI) scale has broad utility for research on mindfulness, positive psychology,
and social psychology.
A three-stimulus auditory oddball series was presented to experienced Vipassana meditators during meditation and a control thought period to elicit event-related brain potentials (ERPs) in the two different mental states. The stimuli consisted of a frequent standard tone (500 Hz), an infrequent oddball tone (1000 Hz), and an infrequent distracter (white noise), with all stimuli passively presented through headphones and no task imposed. The strongest meditation compared to control state effects occurred for the distracter stimuli: N1 amplitude from the distracter was reduced frontally during meditation; P2 amplitude from both the distracter and oddball stimuli were somewhat reduced during meditation; P3a amplitude from the distracter was reduced during meditation. The meditation-induced reduction in P3a amplitude was strongest in participants reporting more hours of daily meditation practice and was not evident in participants reporting drowsiness during their experimental meditative session. The findings suggest that meditation state can decrease the amplitude of neurophysiologic processes that subserve attentional engagement elicited by unexpected and distracting stimuli. Consistent with the aim of Vipassana meditation to reduce cognitive and emotional reactivity, the state effect of reduced P3a amplitude to distracting stimuli reflects decreased automated reactivity and evaluative processing of task irrelevant attention-demanding stimuli.
Distressing imagery may inhibit health communications by inducing audiences to reduce distress by avoiding attention to persuasive messages.
This study used eye-tracking methods to compare gaze time allocated to a persuasive textual message, accompanied by either distressing high-resolution color images or less distressing two-color images with degraded outline and detail.
Participants in the distressing images condition showed lower intentions to reduce drinking in the following 3 months, which may have been mediated by lower gaze time to textual elements of the message. The effect was stronger in participants who both scored lower on dispositional mental disengagement and were more vulnerable to alcohol-related problems.
These findings suggest that distressing imagery may inhibit persuasion by reducing audience attention to message components. Implications for message design are discussed.
Mindfulness is an attribute of consciousness long believed to promote well-being. This research provides a theoretical and empirical examination of the role of mindfulness in psychological well-being. The development and psychometric properties of the dispositional Mindful Attention Awareness Scale (MAAS) are described. Correlational, quasi-experimental, and laboratory studies then show that the MAAS measures a unique quality of consciousness that is related to a variety of well-being constructs, that differentiates mindfulness practitioners from others, and that is associated with enhanced self-awareness. An experience-sampling study shows that both dispositional and state mindfulness predict self-regulated behavior and positive emotional states. Finally, a clinical intervention study with cancer patients demonstrates that increases in mindfulness over time relate to declines in mood disturbance and stress.
Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to overcome errors caused by microsaccade eye motions that cause the exact gaze point to be uncertain. Second, we design an image enhancement model to refine visual details and remove motion blur effects of input eye images. Finally, we apply a calibration scheme to correct for differences between the perceived and actual gaze points caused by individual Kappa angle differences. With all these improvements, iMon achieves a person-independent per-frame tracking error of 1.49 cm (on smartphones) and 1.94 cm (on tablets) when tested with the GazeCapture dataset and 2.01 cm with the TabletGaze dataset. This outperforms the previous state-of-the-art solutions by ~22% to 28%. By averaging multiple per-frame estimations that belong to the same fixation point and applying personal calibration, the tracking error is further reduced to 1.11 cm (smartphones) and 1.59 cm (tablets). Finally, we built implementations that run on an iPhone 12 Pro and show that our mobile implementation of iMon can run at up to 60 frames per second - thus making gaze-based control of applications possible.
Today’s fast paced life reports so much stress among people that it may lead to various psychological and physical illnesses. Yoga and meditation are the best strategies to reduce the effect of stress on physical and mental level without any side-effects. In this study, combined yoga and Sudarshan Kriya (SK) has been used as an alternative and complementary therapy for the management of stress. The aim of the study is to find a method to classify the meditator and non-meditator states with the best accuracy. The 50 subjects have been participating in this study and divided into two groups, i.e. study and control group. The subjects with regular practice of Yoga and SK are known as meditators and the ones without any practice of yoga and meditation were known as non-meditators. Electroencephalogram (EEG) signals were acquired from these both groups before and after 3 months. The statistical parameters were computed from these acquired EEG signals using Discrete Wavelet Transform (DWT). These extracted statistical parameters were given as input to the classifiers. The decision tree, discriminant analysis, logistic regression, Support Vector Machine (SVM), Weighted K- Nearest Neighbour (KNN) and ensemble classifiers were used for classification of meditator and non- meditator states from the acquired EEG signals. The results have demonstrated that the SVM method gives the highest classification accuracy as compared to other classifiers. The proposed method can be used as a diagnosis system in clinical practices.
Biased attention for emotional information is associated with the emotional disorders. Trait mindfulness is associated with lower depression and anxiety and with improved attentional control. Mindfulness is also related to lower levels of brooding rumination. The current study examined the association between trait mindfulness, brooding rumination, depressed and anxious state moods, and attention to emotional visual stimuli utilizing eye tracking methodology. Participants were 158 undergraduates. Trait mindfulness was negatively associated with attention to sad and threatening stimuli, but was not associated with attention to positive or neutral stimuli. There was an indirect effect of mindfulness on attention to sad stimuli through brooding rumination. Data are cross sectional but provide initial evidence that mindfulness may partially exert its effects on depression and anxiety by lessening attention to negatively-valenced stimuli.
Recent developments in neurotechnology effectively utilize the decades of neuroscientific findings of multiple meditation techniques. Meditation is linked to higher-order cognitive processes, which may function as a scaffold for cognitive control. In line with these developments, we analyze oscillatory brain activities of expert and non-expert meditators from the Himalayan Yoga tradition. We exploit four dimensions (Temporal, Spectral, Spatial and Pattern) of EEG data and present an analysis pipeline employing machine learning techniques. We discuss the significance of different frequency bands in relation with distinct primary 5 scalp brain regions. Functional connectivity networks (PLV) are utilized to generate features for classification between expert and non-expert meditators. We find (a) higher frequency β and γ oscillations generate maximum discrimination over the parietal region whereas lower frequency θ and α oscillations dominant over the frontal region; (b) maximum accuracy of over 90% utilizing features from all regions; (c) Quadratic Discriminant Analysis surpasses other classifiers by learning distribution for classification. Overall, this paper contributes a pipeline to analyze EEG data utilizing various properties and suggests potential neural markers for an expert meditative state. We discuss the implications of our research for the advancement of personalized headset design that rely on feedback on depth of meditation by learning from expert meditators.
Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.
Objectives
The present investigation is to study the impact of yoga and meditation on Brain waves concerning physical and mental health. There are mainly three stages (steps) in the brain wave classification:(i) preprocessing, ii) feature extraction, and iii) classification. This work provides a review of interpretation methods of Brain signals (Electroencephalogram (EEG)) EEG during yoga and meditation. Past research has revealed significant mental and physical advantages with yoga and meditation (1).
Methods
The research topic reviewed focused on the machine learning strategies applied for the interpretation of brain waves. In addressing the research questions highlighted earlier in the general introduction, we conducted a systematic search of articles from targeted scientific and journal online databases that included PubMed, Web of Science, IEEE Xplore Digital Library (IEEE), and Arxiv databases based on their relevance to the research questions and domain topic. The survey topic is relatively nascent, and therefore, the scope of the search period was limited to the 20-year timeline that was deemed representative of the research topic under investigation. The literature search was based on the keywords “EEG”, “yoga*” and “meditation*”. The key phrases were concatenated using Boolean expressions and applied to search through the selected online databases yielding a total of 120 articles. The online databases were selected based on the relevancy of content with the research title, research questions, and the domain application. The literature review search, process, and classification were carefully conducted guided by two defined measures; 1.) Inclusion criteria; and 2.) Exclusion criteria. These measures define the criteria for searching and extracting relevant articles relating to the research title and domain of interest.
Results
Our literature search and review indicate a broad spectrum of neural mechanics under a variety of meditation styles have been investigated. A detailed analysis of various mental states using Zen, CHAN, mindfulness, TM, Rajayoga, Kundalini, Yoga, and other meditation styles have been described by means of EEG bands. Classification of mental states using KNN, SVM, Random forest, Fuzzy logic, neural networks, Convolutional Neural Networks has been described. Superior research is still required to classify the EEG signatures corresponding to different mental states.
Conclusions
Yoga practice may be an effective adjunctive treatment for a clinical and aging population. Advanced research can examine the effects of specific branches of yoga on a designated clinical grouping. Yoga and meditation increased overall healthy brain activity (2).
Trait mindfulness pertains to one’s ability to non-judgmentally attend to experiences. While attention regulation represents a core component of mindfulness, the relation between trait mindfulness and visual attention is unclear. Further, despite established associations between mindfulness and emotion regulation, few studies have examined whether trait mindfulness may be related to attention to emotionally valenced content. Thus, the present study used an eye-tracking paradigm to assess relations between trait mindfulness, emotion regulation and selective visual attention to valenced stimuli. Participants (N = 123; 75.6% female; 87% Caucasian; Mage = 19.14 years) completed measures of trait mindfulness, emotion regulation, and engaged in an eye-tracking paradigm in which they viewed sad, threatening, neutral, and happy images simultaneously. Dwell times on images (all categories combined), black space on screen, and each image category were calculated. Bivariate correlations were assessed to determine the relations among mindfulness, emotion regulation, and visual attention, controlling for mood. Trait mindfulness was associated with longer dwell time on images overall, but specifically longer dwell time on threatening and happy images. Although trait mindfulness and emotion regulation were positively associated, emotion regulation was not significantly associated with visual attention. These results suggest that trait mindfulness is associated with visual attention to valenced stimuli, particularly happy and threatening images, and emotion regulation does not account for these relations. These findings add to our understanding of the cognitive mechanisms underlying trait mindfulness.
EEG oscillatory correlates of expert meditators have been studied in the time-frequency domain. Machine Learning techniques are required to expand the understanding of oscillatory signatures. In this work, we propose a methodological pipeline to develop machine learning models for the classification between expert and nonexpert meditative state. We carried out this study utilizing the online repository consisting of EEG dataset of 24 meditators that categorized as 12 experts and 12 nonexperts meditators. The pipeline consists of four stages that include feature engineering, machine learning classifiers, feature selection, and visualization. We decomposed signals using five wavelet families consisting of Haar, Biorthogonal(1.3-6.8), Daubechies( orders 2-10), Coiflet(orders 1-5), and Symlet(2-8), followed by feature extraction using relative entropy and power. We classified the meditative state between expert and non-expert meditators employing twelve classifiers to build machine learning models. Wavelet coefficients d8 shows the maximum classification accuracy in all the wavelet families. Wavelet orders Bior3.5 and Coif3 produce the maximum classification performance with the detail coefficient d8 using relative power. We have successfully classified the meditative state between expert and non-expert with 100% accuracy using d5,d6,d7,d8,a8 coefficients. Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy. We have figured out the most discriminating channels during classification and reported 20 channels involving frontal, central and parietal regions. We plot the high dimensional structure of data by utilizing two feature reduction techniques PCA and t-SNE.
To determine whether childhood intermittent exotropia (IXT) affects distance divergence and performance in block-building tasks within a virtual reality (VR) environment.
Thirty-nine children with IXT, aged 6–12 years, who underwent muscle surgery and 37 normal controls were enrolled. Children were instructed to watch the target moving away and perform a block-building task while fitted with a VR head-mounted display equipped with eye- and hand-movement tracking systems. The change in inter-ocular distance with binocular distance viewing, time to stack five cube blocks of different sizes in order, and distance disparities between the largest and farthest cubes were assessed. All children were evaluated at baseline and 3-month time points.
The patients with IXT exhibited a larger distance divergence than did controls (p = 0.024), which was associated with greater distance angle of deviation and poorer distance control (r = 0.350, p = 0.001 and r = 0.349, p = 0.004). At baseline, the patients with IXT showed larger distance disparities in the block-building task than did controls in terms of the horizontal, vertical, and 3-dimensional (3-D) measurements (all ps < 0.050). Larger horizontal disparity was associated with greater distance angle of deviation (r = 0.383, p = 0.037). Three months after surgery, the horizontal and 3-D disparities in the patients with IXT improved significantly and were not comparably different compared with controls.
These preliminary findings suggest that VR-based block-building task may be useful in testing possible deficits in visuo-motor skills associated with childhood IXT.
Advent of computationally efficient smartphones, inexpensive high‐resolution cameras, drones, and robotic sensors has brought a new era of next‐generation intelligent monitoring systems for civil infrastructure. Vibration‐based condition assessment has garnered as a prominent method of evaluating the health of large‐scale infrastructure. The use of contact‐based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next‐generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so‐called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next‐generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.
The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.
Repeated exposure to stressors, even if mild, may alter the efficiency of optimal stress responses and hinder emotion regulation skills. Mindfulness meditation, by strengthening self-regulation and awareness, may optimize the efficiency of physiological, cognitive, and behavioral reactions to stressful events but typically requires notable commitment to practice, which often leads to disengagement. Recent research suggested that such practices may be made more accessible and that the potential for self-enhancement and stress management of meditation might be improved by supporting mental training with wearable neurofeedback devices able to inform the practicer on ongoing modulation of bodily and brain activity. This study aimed at testing the effect of such novel training approach based on the integration of mental training with brain-sensing wearable devices on physiological (heart rate and variability) and subjective markers of stress (perceived stress, anxiety, and mood states). Participants (N = 55) have been randomly divided into an active control (CONTg) and an experimental group (EXPg). Both groups completed a four-week training constituted by brief daily activities based on mindfulness practices. Experimental participants practiced with the support of dedicated brain-sensing devices. By analyzing pre- and post-training assessments, we observed relevantly decreased stress and anxiety measures in EXPg, as well as relevantly decreased mental fatigue and increased vigor. EXPg also showed improved physiological markers of vagal tone both at rest and during exposure to a cognitive stressor. Reported findings add to the limited available literature on potential effects of technology-supported mental training protocols for promoting subjective well-being and enhancing self-regulation skills.
The term health care has a very wide scope that ranges from lifestyle and wellness to care for acute conditions. With the availability of digital accessories for monitoring basic biological functions, the potential for obtaining detailed data on the lifestyle, habits, and behavior of an individual exists. Such data can enable the diagnosis of the causes for a condition with higher accuracy. Recently, a large number of devices have become available on the market that can monitor various aspects of lifestyle and biological functions. Such data provide feedback to an individual for compliance with healthy guidelines as well as contributing information to the health-care provider for use in the diagnosis of an ailment. In this article, we identify the various aspects of care that can benefit from consumer-grade health-monitoring devices and present the overall landscape in the context of self-care. We qualify the term consumer health care, assigning the context to it and identifying the services available in that context.
Mindfulness practices have been shown to improve various health related aspects of patient lifestyles including the reduction of depressive relapse in those who suffer from depression [4] and reduction of perceived pain in chronic pain patients [3]. Mindfulness meditation has also been shown to reduce stress and encourage relaxation [10] which is naturally beneficial for many demographics, including those with low life satisfaction [1]. This paper outlines an attempt to translate learning outcomes of mindfulness practice with gamification into educational software. The software provides immersive virtual environments and guided meditation tracks to catalyze mindfulness learning practices. It also supports electroencephalography (EEG) data collection to monitor the affective states of participants, which allows the software to provide visual feedback in real-time. Its design is heavily influenced by gamification strategies and contemporary game design practices in order to encourage persistent training behaviors in participants over longer periods of time.
Electroencephalogram (EEG) is a non-invasive test that measures electrical activity in the brain. The source of EEG activity is the voltage differences within neurons of the brain. Therefore, it is a reflection of the synchronous activity of neurons. EEG activity shows oscillations at a variety of frequencies. This rhythmic activity is divided into bands by frequency and usually associated with different states of brain functioning. EEG is a valuable tool for clinical and research uses in many scientific fields. However, traditional devices are usually cost thousands of dollars and the preparation process is time-consuming. In recent years, newer EEG devices are introduced for consumer use and currently available on the market. The devices use dry electrodes and send signal via wireless, thus easier to use and more comfortable to wear. They are also considerably cheaper, cost around a few hundred dollars. In this paper, we used a consumer EEG device to record the brainwave of Buddhist monks during meditation and other activities. We then analyzed the recordings and demonstrated that an inexpensive device has enough features and can also be used as a tool for research as well. Muse from InteraXon Inc. was chosen as a consumer EEG device for our experiment. The device has a total of seven EEG sensors capable of reading four channels of data with active noise suppression. It also provides additional information, such as eye blink and jaw clench, for further analysis. The preliminary results show that the device can effectively record an EEG signal and could potentially be used as a research tool.
Insufficient attention to tasks can result in slips of action as automatic, unintended action sequences are triggered inappropriately. Such slips arise in part from deficits in sustained attention, which are particularly likely to happen following frontal lobe and white matter damage in traumatic brain injury (TBI). We present a reliable laboratory paradigm that elicits such slips of action and demonstrates high correlations between the severity of brain damage and relative-reported everyday attention failures in a group of 34 TBI patients. We also demonstrate significant correlations between self-and informant-reported everyday attentional failures and performance on this paradigm in a group of 75 normal controls. The paradigm (the Sustained Attention to Response Task—SART) involves the withholding of key presses to rare (one in nine) targets. Performance on the SART correlates significantly with performance on tests of sustained attention, but not other types of attention, supporting the view that this is indeed a measure of sustained attention. We also show that errors (false presses) on the SART can be predicted by a significant shortening of reaction times in the immediately preceding responses, supporting the view that these errors are a result of `drift' of controlled processing into automatic responding consequent on impaired sustained attention to task. We also report a highly significant correlation of −0.58 between SART performance and Glasgow Coma Scale Scores in the TBI group.