EEG System Research Articles

Fully differential fifth-order dual-notch low-pass filter for portable EEG system

This paper presents a new fully differential fifth-order dual-notch low-pass filter based on multiple-input multiple-output operational transconductance amplifiers (MIMO OTA). This work shows that MIMO OTA-based fifth-order dual-notch filter can reduce the number of used OTAs, resulting in simplified realization and low-power consumption. The multiple-input OTA is obtained by using the multiple-input dynamic threshold MOS (DTMOS) technique without additional differential pairs. A simple common-mode feedback technique has been used; thus, fully differential OTA can be easily obtained. The proposed dual-notch low-pass filter can be applied to electroencephalogram (EEG) detection system to reject the 50 Hz powerline interference and the third harmonic 150 Hz. The proposed filter has been simulated using Cadence environment with 0.18 µm TSMC CMOS process. The power supply of 0.5 V is used and the total power consumption of the MIMO OTA is 17.5 nW. The proposed filter provides 49.7 dB dynamic range for 1% total harmonic distortion (THD) for a sine input signal of 250 mVpp at 10 Hz. At 50 Hz and 150 Hz the notch depths of attenuation are respectively −37.2 dB and −47.4 dB. The pre-layout simulation results are in good agreement with the theory.

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG.

Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.

Exploratory Study on the Clinical use of EEG for the People with Chronic Stroke and Their Correlation with the Neuropsychological Outcome.

Objective. To measure the EEG signals of the people with chronic stroke in eyes-closed and eyes-open condition and study their relationship with the cognitive function and mental wellbeing. Methods. The investigators would conduct cognitive and mental wellbeing tests on recruited subjects. Their EEG signal was acquired by the 16-channel EEG system. The absolute power under different frequency bands and EEG indices (delta alpha ratio and pairwise derived brain symmetry index) in different eye conditions was calculated. Pearson's correlation was conducted to investigate the association between the clinical tests and the EEG index. Results. 32 subjects were recruited for the study. There was a significant correlation between the pairwise derived brain symmetry index (pdBSI) in eyes-open condition with the Stroop Test (p = .002), Paced Auditory Serial Addition Test-3 s (p = .008)/2 s (p = .002) and WHO-5 well-being scale (p = .023). Conclusions. There is a significant correlation between the brain symmetry index and the cognitive and wellbeing assessment. Brain symmetry index over the delta frequency has been found to be the most useful parameter relating to the clinical score.Significance:It is recommended to use EEG as an adjunctive neuropsychological assessment in clinics for people with chronic stroke, especially for clients who could not undertake conventional assessments (eg aphasia, attention problem).Highlights: There is a significant correlation between the EEG index and the clinical neuropsychological assessmentPairwise Derived Brain Symmetry index in delta frequency range correlated with most of the neuropsychological outcome.It is feasible for us to adopt EEG as an adjunctive assessment in clinical settings.

Expert Perspective: Who May Benefit Most From the New Ultra Long-Term Subcutaneous EEG Monitoring?

Today's modalities for short-term monitoring of EEG are primarily meant for supporting clinical diagnosis of epilepsy or classifying seizures and interictal epileptiform discharges while long-term EEG adds the value of differential diagnosis investigation or pre-surgical evaluation. However, longitudinal epilepsy care relies on patient diaries, which is known to be unreliable for most patients and especially those with focal impaired awareness or nocturnal seizures. The subcutaneous ultra long-term EEG (ULT-EEG) systems alleviate those issue by enabling objective, continuous EEG monitoring for days, weeks, months, or years. Albeit a great advance in continuous EEG over extended periods, it comes with the caveat of limited spatial resolution of two channels. Therefore, the new subcutaneous EEG modality may be especially suited for a selected group of patients. We convened a panel of experienced epileptologists to consider the utility of a subcutaneous, two-channel ULT-EEG device with the goal of developing a consensus-based expert recommendation on selecting the optimal patient types for this investigative technique. The ideal patients to select for this type of monitoring would have focal impaired awareness seizures without predominant motor features and seizures with medium to high voltage patterns. As this technology matures and we learn more about its limitations and benefits we might find a wider array of use case scenarios as it is believed that the benefits for many patients are most likely to outweigh the risks and cost.

Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator.

Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.

Relationship Between Changes in EEG and Autonomic Nervous System Activity During Task Execution by a Simple EEG

Relationship Between Changes in EEG and Autonomic Nervous System Activity During Task Execution by a Simple EEG

Самооценка влияния спелеоклиматотерапии на организм человека

The influence of speleoclimatotherapy on the human body was investigated using questionnaire data. The study involved 29 volunteer 2nd year of Voronezh State Medical University named after N. N. Burdenko. The course of speleoclimatotherapy consisted of 10 sessionsof 60 minutes each and was carried out in a stationary surface speleological chamber at a temperature of 18–22 °C. The subjects were offered a questionnaire to assess their condition during speleoclimatotherapy (each specific day) and the overall results of the treatment. The analysis of the data obtained was carried out using the Excel version 16. According to the results, despite the alarming symptoms the subjects experienced during the speleoclimatotherapy treatment itself, 85% of them noted positive changes in their functional state, while 15% did not notice any changes. None of the patients reported a negative effect of speleoclimatotherapy on their well-being or functional state, despite our previous findings indicating a mixed effect of speleoclimate on the EEG, immune and cardiovascular system of the person undergoing the treatment. This is a key motivation for further study into the subtle internal mechanisms of the impact of speleoclimate on human health, since the subjective assessment of one’s state of health does not always correspond to the changes occurring in human systems, organs and tissues.

Self-Applied Electrode Set Provides a Clinically Feasible Solution Enabling EEG Recording in Home Sleep Apnea Testing

Home sleep apnea testing (HSAT) without electroencephalography (EEG) recordings is increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of obstructive sleep apnea (OSA). However, without EEG, electrooculography (EOG), and chin electromyography (EMG) recordings, the OSA severity may be significantly underestimated. Although several ambulatory EEG systems have been recently introduced, no patient-applied systems including EEG, EOG, and chin EMG suitable for home polysomnography are currently in clinical use. We have recently developed and pre-clinically tested a self-applied ambulatory electrode set (AES), consisting of frontal EEG, EOG, and EMG, in subjects with possible sleep bruxism. Now, in this clinical feasibility study, we investigated the signal scorability and usability of the AES as a self-administered sleep assessment approach supplementing the conventional HSAT device. We also investigated how the diagnostic parameters and OSA severity changed when utilizing the AES. Thirty-eight patients (61 % male, 25-78 years) with a clinical suspicion of OSA conducted a single-night, self-administered HSAT with a portable polysomnography device (Nox A1, Nox Medical, Reykjavik, Iceland) supplemented with AES. Only one AES recording failed. The use of AES signals in data analysis significantly affected the median AHI, increasing it from 9.4 to 12.7 events/h (p<0.001) compared to the conventional HSAT. Also, in eight patients, the OSA severity class changed to one class worse. Perceived ease of use was well in line with that previously found among healthy volunteers. These results suggest that the AES provides an easy, clinically feasible solution to record EEG as a part of conventional HSAT.

EEG normal variants: A prospective study using the SCORE system

ObjectiveTo determine the prevalence and characteristics of normal variants in EEG recordings in a large cohort, and provide readers with typical examples of all normal variants for educational purposes. MethodsUsing the SCORE EEG system (Standardized Computer-Based Organized Reporting of EEG), we prospectively extracted EEG features in consecutive patients. In this dataset, we analyzed 3050 recordings from 2319 patients (mean age 38.5 years; range: 1–89 years). ResultsThe distribution of the normal variants was as follows: sharp transients 19.21% (including wicket spikes), rhythmic temporal theta of drowsiness 6.03%, temporal slowing of the old 2.89%, slow fused transients 2.59%, 14-and 6-Hz bursts 1.83%, breach rhythm 1.25%, small sharp spikes 1.05%, 6-Hz spike and slow wave 0.69% and SREDA 0.03%. ConclusionsThe most prevalent normal variants are the sharp transients, which must not be over-read as epileptiform discharges. SignificanceEEG readers must be familiar with the normal variants to avoid misdiagnosis and misclassification of patients referred to clinical EEG recordings.

Implementing the SCORE system improves the quality of clinical EEG reading

ObjectiveTo assess whether implementing the freeware version of the SCORE EEG system (Standardized Computer-based Organized Reporting of EEG) leads to improvement in the quality of clinical EEG reading, and whether EEG reports in SCORE EEG are understood and accepted by the referring physicians. MethodsWe generated EEG reports in the conventional, free-text style and then using SCORE EEG, in consecutive patients referred to routine EEG. We used the Georgian translation in the SCORE EEG Free Edition. We pre-defined quality indicators consisting of a list of 24 key features that need to be addressed in EEG reports. We compared these quality indicators in free-text reports with SCORE EEG. In addition, EEG reports in SCORE EEG format were assessed by ten referring physicians, who evaluated their usability on a 7-point Likert scale. ResultsWe included and evaluated EEG reports from 157 patients (80 female; age: 1–75 years; median: 28 years). Fourteen features were reported exclusively in SCORE EEG, four were reported significantly more often in SCORE EEG than in free-text format, and six features were reported equally often in SCORE EEG and in free-text format. Usability aspects of SCORE EEG were highly rated by the referring physicians (median 6–7 on the 7-point Likert scale). ConclusionsThe structured system of EEG reporting in SCORE EEG helped the experts reading clinical EEG to cover the important aspects and increase the quality of clinical EEG reports. SignificanceImplementing the freeware version of SCORE EEG in underprivileged areas will help improving management of patients with epilepsy.

Carl Wilhelm Sem-Jacobsen: Aerospace Neurophysiology and Deep Brain Stimulation Pioneer.

The Norwegian physician Carl Wilhelm Sem-Jacobsen (1912–1991) was a pioneer in deep brain stimulation and aerospace neurophysiology, but for several reasons, his story has remained untold. During WW2, he collaborated with a renowned military underground resistance group against the Nazi occupants and then had to flee to neutral Sweden. He returned to participate in the liberation of Northern Norway as a Captain in the US Special Forces also working with the OSS (Office of Strategic Services—precursor for CIA) and received a citation from General Eisenhower for his contributions. Sem-Jacobsen then spent several years in the US training in psychiatry and clinical neurophysiology at the Mayo Clinic. He constructed his own medical technical devices, was among the first to develop deep brain stimulation, and made the smallest EEG and EKG recording systems yet produced, also used by the American astronauts walking on the Moon. But he was more an inventor than a researcher, and few of his observations were published in peer-reviewed medical journals. He built his own neurophysiologic institute for neurosurgery, deep brain recordings, and deep brain stimulation in Oslo's main psychiatric hospital, but was sponsored by US military forces and NASA. He knew CIA Director William E. Colby personally, and rumors soon flourished that Sem-Jacobsen conducted secret mind control experiments for American authorities and the CIA. These accusations were investigated, and long after his death, he was officially absolved by a Hearing Committee appointed by the Norwegian Government. Nevertheless, all his personal files were burnt by his family who was still harassed by investigative journalists. Sem-Jacobsen also documented some of his work on film, but the whereabouts of these films have remained unknown. I searched for them for several years and recently discovered numerous films and photographs in an old barn in rural Norway. These films and photographs document in-action neurophysiology recordings in divers, pilots, and astronauts, and they show how Sem-Jacobsen in collaboration with experienced neurosurgeons in Oslo conducted the very first trials with deep brain stimulation in patients with Parkinson disease. He apparently even tried subthalamic stimulation as early as in the 1950s.

Intra‐ and inter‐rater reliability of brain's activation area measurement on imagej: An easy and low‐cost method for cognitive and neurophysiological studies

AbstractBackgroundNeuroimage is an important resource in cognitive neuroscience1 but its analysis is complex and requires reliable and affordable procedures. The present study sought to determine the intra‐ and inter‐rater reliability of brain´s activation area of neuroimages obtained derived from an integrated EEG system equipment.MethodThe study follows the Declaration of Helsinki. Fifteen cerebral cortex images were obtained through Neurometry v6 (Bioevolution®, Brazilian National Health Surveillance Agency registration 81403519002)2,3 from 15 individuals above 60 years, who consented to participate of the study. This device consists of a 19‐channel EEG (10‐20 system). Amplitude of brain waves (delta, theta, alpha, beta, and gamma) are automatically processed, filtered and analyzed with Math Library MtxVec, LAPACK and Math Kernel BLAS with codes Streaming SIMD (SSE2, SSE3 and SSE4)2. The neuroimages display different colors according to the amplitude of brain waves: brown/blue (low activation), green/yellow (moderate activation), and orange/red (high activation)2. Images retrieved were exported and analyzed with the ImageJ software (National Institutes of Health, Bethesda, Maryland, USA) in 3 phases: 1st An experienced researcher (RSMJ) standardized Broadmann´s area as the region of interest (ROI); 2nd The researcher specified the brain´s activation area in accordance with colored regions (brown, blue, green, yellow, orange, and red) in the Broadmann´s area 10 (BA10) covering 1,391 pixels. Colors were covered using 2 ImageJ tools (polygon and freehand selections). In order to calculate the colored area of interest, pixels quantification was performed with a 400% magnification filter (Figure 1). The procedure was repeated 24h later for the intra‐rater reliability; 3rd An inexperienced assessor (IRDS) independently performed the same procedure of the 2nd phase. Intraclass Correlation Coefficients (ICC3,1 and ICC3,k)4 were calculated to determine the intra‐ and inter‐rater reproducibility. Typical Error of Measurement (TEM=SD∑difference between measures÷√2)5 was also calculated to quantify typical deviations from the right measure.ResultsIntra‐ and inter‐rater reliability were excellent (ICC=0.99, p<0.01) and good (ICC=0.82, p<0.01), respectively (Table 1). TEM varied from 4 to 17%.ConclusionQuantification of the brain´s activation area of neuroimages derived from Neurometry v6 using the ImageJ ROI is highly reliable, independently of the assessor´s experience.

DeepADNet: A CNN‐LSTM model for the multi‐class classification of Alzheimer’s disease using multichannel EEG

AbstractBackgroundEarly and accurate diagnosis of Alzheimer’s disease (AD) – an incurable and progressive brain disorder, along with a sharp upward trend in the incidence rate, is significantly important for patients to take prevention and appropriate treatments. However, the pathophysiological mechanism at the levels of AD severity is still poorly understood in spite of the worldwide financial and research efforts. Using an inexpensive and noninvasive modality, such as Electroencephalography (EEG) coupled with Deep Learning (DL)‐derived diagnostic tool to retain the accuracy and versatility, therefore, has gained much attention in AD multi‐class classification.MethodsThe present study proposes DL model, named DeepADNet, for the AD multi‐class classification using multichannel EEG. First, we extracted the event‐related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands relative to a specific experimental event. Second, the hybrid deep Convolutional Neural Network (CNN) ‐ Long Short‐Term Memory Network (LSTM) was built to exploit the wide range of ERSP patterns on a time‐frequency domain and to generate discriminant features to classify healthy controls (HC) with two AD subject groups. To tackle the imbalance problem and improve the classification accuracy, we also applied different over‐sampling techniquesResultsThe EEG data were collected from the Oddball – a cognitive ability test with 63 subjects (23 HC, 17 Pre‐symptomatic AD (aAD) and 23 Prodromal AD (pAD) subjects) and the N‐back – a memory ability test with 36 subjects (13 HC, 11 aAD and 12 pAD subjects). We demonstrated that the ERSP patterns displayed significant differences among three subject groups during the two experimental stages. Classification results also reveal that a CNN‐LSTM model could overcome the existing methods utilizing the hand‐engineered features which demand on prior knowledge of AD analysis and achieved the highest accuracy during the Oddball (71.95% ± 0.019 and 75.95% ± 0.017) and during the N‐back (69.40% ± 0.003 and 73.70% ± 0.010) corresponding to original and SVMSMOTE‐based ERSP features.ConclusionsThese findings demonstrate the capability of the EEG systems to better underlie the AD progression through spatiotemporal‐dynamic regions in the brain and the potential of DL‐based models for further AD classification studies.

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors.

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.

Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment

Gait training in a virtual reality (VR) environment is promising for children affected by different disorders. However, the efficacy of VR therapy is still under debate, and more research is needed to clarify its effects on clinical conditions. The combination of VR with neuroimaging methods, such as the electroencephalography (EEG), might help in answering this need. The aim of the present work was to set up and test a system for the multimodal analysis of the gait pattern during VR gait training of pediatric populations by analyzing the EEG correlates as well as the kinematic and kinetic parameters of the gait. An EEG system was integrated with the Gait Real-time Analysis Interactive Lab (GRAIL). We developed and validated, with healthy adults (n = 5) and children (n = 4, healthy or affected by cerebral palsy (CP)), the hardware and software integration of the two systems, which allowed the synchronization of the acquired signals and a reliable identification of the initial contact (IC) of each gait cycle, showing good sensitivity and critical success index values. Moreover, we tested the multimodal acquisition by successfully analyzing EEG data and kinematic and kinetic parameters of one healthy child and one child with CP. This system gives the possibility of monitoring the effect of the VR therapy and studying the neural correlates of gait.

Near-zero phase-lag hyperscanning in a novel wireless EEG system

Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with electroencephalography (EEG) is increasingly popular owing to its mobility and its ability to allow studying social interactions in naturalistic settings at the millisecond scale. Approach. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposes a clock-synchronized method that uses a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional radio frequency channels to keep the counter of their receiving dongles updated, which guarantees that binding event markers received by the dongle with the EEG time series have the correct timestamp. Main results. The results of two simulation experiments and one video gaming experiment reveal that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase lag and negligible amplitude difference between the signals. Significance. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.

Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g., single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first open-source algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts, if present, gives the opportunity for future extraction of heart rate features without ECG measurement.

Efficient Artifact Removal from Low-Density Wearable EEG using Artifacts Subspace Reconstruction.

Light-weight, minimally-obtrusive mobile EEG systems with a small number of electrodes (i.e., low-density) allow for convenient monitoring of the brain activity in out-of-the-lab conditions. However, they pose a higher risk for signal contamination with non-stereotypical artifacts due to hardware limitations and the challenging environment where signals are collected. A promising solution is Artifacts Subspace Reconstruction (ASR), a component-based approach that can automatically remove non-stationary transient-like artifacts in EEG data. Since ASR has only been validated with high-density systems, it is unclear whether it is equally efficient on low-density portable EEG. This paper presents a complete analysis of ASR performance based on clean and contaminated datasets acquired with BioWolf, an Ultra-Low-Power system featuring only eight channels, during SSVEP sessions recorded from six adults. Empirical results show that even with such few channels, ASR efficiently corrects artifacts, enabling an overall enhancement of up to 40% in SSVEP response. Furthermore, by choosing the optimal ASR parameters on a single-subject basis, SSVEP response can be further increased to more than 45%. These results suggest that ASR is a viable and robust method for online automatic artifact correction with low-density BCI systems in real-life scenarios.

Frontal and parietal EEG alpha asymmetry: a large-scale investigation of short-term reliability on distinct EEG systems

EEG resting-state alpha asymmetry is one of the most widely investigated forms of functional hemispheric asymmetries in both basic and clinical neuroscience. However, studies yield inconsistent results. One crucial prerequisite to obtain reproducible results is the reliability of the index of interest. There is a body of research suggesting a moderate-to-good reliability of EEG resting-state alpha asymmetry, but unfortunately sample sizes in these studies are typically small. This study presents the first large-scale short-term reliability study of frontal and parietal EEG resting-state alpha asymmetry. We used the Dortmund Vital Study data set containing 370 participants. In each participant, EEG resting state was recorded eight times, twice with their eyes opened, twice with their eyes-closed, each on two different EEG systems. We found good reliability of EEG alpha power and alpha asymmetry on both systems for electrode pairs. We also found that alpha power asymmetry reliability is higher in the eyes-closed condition than in the eyes-open condition. The frontomedial electrode pair showed weaker reliability than the frontolateral and parietal electrode pairs. Interestingly, we found no population-level alpha asymmetry in frontal electrodes, one of the most investigated electrode sites in alpha asymmetry research. In conclusion, our results suggest that while EEG alpha asymmetry is an overall reliable measure, frontal alpha asymmetry should be assessed using multiple electrode pairs.

Wearable Reduced-Channel EEG System for Remote Seizure Monitoring.

Epitel has developed Epilog, a miniature, wireless, wearable electroencephalography (EEG) sensor. Four Epilog sensors are combined as part of Epitel's Remote EEG Monitoring platform (REMI) to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog sensor EEG in the 10-channel “REMI montage,” with and without seizure detection support software. Three board certified epileptologists reviewed the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive. Blinded consensus review of unannotated Epilog EEG in the REMI montage detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate (FDR) of 0.002 false positives per hour (FP/h) of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in FDR (0.005 FP/h). As seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/h. This is the first study showing epileptologists' ability to blindly review EEG from four Epilog sensors in the REMI montage, and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.