Portable Headband Electroencephalogram in the Detection of Absence Epilepsy

The accuracy of headband electroencephalogram (EEG) was compared to traditional EEG in pediatric patients with absence epilepsy. This study enrolled 10 patients with previously diagnosed absence epilepsy and examined the concordance of headband EEG and traditional EEG in the follow-up EEG of treated absence epilepsy. The study found a concordant result in 80% of cases providing a signal that absence epilepsy is an effective target for headband EEG. The study showcases a need for further research in headband EEG technology and continued improvements in technology.

Exploratory Validation of Sleep-Tracking Devices in Patients with Psychiatric Disorders

Purpose

Sleep-tracking devices have performed well in recent studies that evaluated their use in healthy adults by comparing them with the gold standard sleep assessment technique, polysomnography (PSG). These devices have not been validated for use in patients with psychiatric disorders. Therefore, we tested the performance of three sleep-tracking devices against PSG in patients with psychiatric disorders.

Patients and methods

In total, 52 patients (32 women; 48.1 ± 17.2 years, mean ± SD; 18 patients diagnosed with schizophrenia, 19 with depressive disorder, 3 with bipolar disorder, and 12 with sleep disorder cases) were tested in a sleep laboratory with PSG, along with portable electroencephalography (EEG) device (Sleepgraph), actigraphy (MTN-220/221) and consumer sleep-tracking device (Fitbit Sense).

Results

Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Sleepgraph (0.95, 0.76), Fitbit Sense (0.95, 0.45) and MTN-220/221 (0.93, 0.40). Portable EEG (Sleepgraph) had the best sleep stage-tracking performance. Sleep-wake summary metrics demonstrated lower performance on poor sleep (ice, shorter total sleep time, lower sleep efficiency, longer sleep latency, longer wake after sleep onset).

Conclusion

Devices demonstrated similar sleep-wake detecting performance as compared with previous studies that evaluated sleep in healthy adults. Consumer sleep device may exhibit poor sleep stage-tracking performance in patients with psychiatric disorders due to factors that affect the sleep determination algorithm, such as changes in autonomic nervous system activity. However, Sleepgraph, a portable EEG device, demonstrated higher performance in mental disorders than the Fitbit Sense and actigraphy.

The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities

Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.

Feasibility of assessing brain activity using mobile, in-home collection of electroencephalography: methods and analysis

The last decade has seen increased availability of mobile electroencephalography (EEG). These mobile systems enable researchers to conduct data collection "in-context," reducing participant burden and potentially increasing diversity and representation of research samples. Our research team completed in-home data collection from more than 400 twelve-month-old infants from low-income backgrounds using a mobile EEG system. In this paper, we provide methodological and analytic guidance for collecting high-quality, mobile EEG in infants. Specifically, we offer insights and recommendations for equipment selection, data collection, and data analysis, highlighting important considerations for selecting a mobile EEG system. Examples include the size of the recording equipment, electrode type, reference types, and available montages. We also highlight important recommendations surrounding preparing a nonstandardized recording environment for EEG collection, obtaining informed consent from parents, instructions for parents during capping and recording, stimuli and task design, training researchers, and monitoring data as it comes in. Additionally, we provide access to the analysis code and demonstrate the robustness of the data from a recent study using this approach, in which 20 artifact-free epochs achieve good internal consistency reliability. Finally, we provide recommendations and publicly available resources for future studies aiming to collect mobile EEG.

Morning brain: real-world neural evidence that high school class times matter

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep-wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students' resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents' brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn.

Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom

The human brain has evolved for group living [1]. Yet we know so little about how it supports dynamic group interactions that the study of real-world social exchanges has been dubbed the "dark matter of social neuroscience" [2]. Recently, various studies have begun to approach this question by comparing brain responses of multiple individuals during a variety of (semi-naturalistic) tasks [3-15]. These experiments reveal how stimulus properties [13], individual differences [14], and contextual factors [15] may underpin similarities and differences in neural activity across people. However, most studies to date suffer from various limitations: they often lack direct face-to-face interaction between participants, are typically limited to dyads, do not investigate social dynamics across time, and, crucially, they rarely study social behavior under naturalistic circumstances. Here we extend such experimentation drastically, beyond dyads and beyond laboratory walls, to identify neural markers of group engagement during dynamic real-world group interactions. We used portable electroencephalogram (EEG) to simultaneously record brain activity from a class of 12 high school students over the course of a semester (11 classes) during regular classroom activities (Figures 1A-1C; Supplemental Experimental Procedures, section S1). A novel analysis technique to assess group-based neural coherence demonstrates that the extent to which brain activity is synchronized across students predicts both student class engagement and social dynamics. This suggests that brain-to-brain synchrony is a possible neural marker for dynamic social interactions, likely driven by shared attention mechanisms. This study validates a promising new method to investigate the neuroscience of group interactions in ecologically natural settings.