Two-channel EEG Research Articles

Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment

BackgroundThe worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer’s Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device.MethodsIn this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI.ResultsOur results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors.DiscussionThe unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

An Open-Source Mouse Chronic EEG Array System with High-Density MXene-Based Skull Surface Electrodes.

Electroencephalography (EEG) is an indispensable tool in epilepsy, sleep, and behavioral research. In rodents, EEG recordings are typically performed with metal electrodes that traverse the skull into the epidural space. In addition to requiring major surgery, intracranial EEG is difficult to perform for more than a few electrodes, is time-intensive, and confounds experiments studying traumatic brain injury. Here, we describe an open-source cost-effective refinement of this technique for chronic mouse EEG recording. Our alternative two-channel (EEG2) and sixteen-channel high-density EEG (HdEEG) arrays use electrodes made of the novel, flexible 2D nanomaterial titanium carbide (Ti3C2T x ) MXene. The MXene electrodes are placed on the surface of the intact skull and establish an electrical connection without conductive gel or paste. Fabrication and implantation times of MXene EEG electrodes are significantly shorter than the standard approach, and recorded resting baseline and epileptiform EEG waveforms are similar to those obtained with traditional epidural electrodes. Applying HdEEG to a mild traumatic brain injury (mTBI) model in mice of both sexes revealed that mTBI significantly increased spike-wave discharge (SWD) preictal network connectivity with frequencies of interest in the β-spectral band (12-30 Hz). These findings indicate that the fabrication of MXene electrode arrays is a cost-effective, efficient technology for multichannel EEG recording in mice that obviates the need for skull-penetrating surgery. Moreover, increased preictal β-frequency network connectivity may contribute to the development of early post-mTBI SWDs.

Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot.

Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment. Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences. Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks. The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.

Prefrontal event-related potential markers in association with mild cognitive impairment.

Alzheimer's disease (AD) is among the leading contributors of dementia globally with approximately 60-70% of its cases. Current research is focused on the mild cognitive impairment (MCI), which is associated with cognitive decline but does not disrupt routine activities. Event-related potential (ERP) research is essential in screening patients with MCI. Low-density channel electroencephalography (EEG) is frequently used due to its convenience, portability, and affordability, making it suitable for resource-constrained environments. Despite extensive research on neural biomarkers for cognitive impairment, there is a considerable gap in understanding the effects on early stages of cognitive processes, particularly when combining physiological and cognitive markers using portable devices. The present study aimed to examine cognitive shortfalls and behavioral changes in patients with MCI using prefrontal selective attention ERP recorded from a prefrontal two-channel EEG device. We assessed cognitive decline using the Mini-Mental State Examination (MMSE) and the Seoul Neuropsychological Screening Battery (SNSB). We administered auditory selective attention tasks to 598 elderly participants, including those with MCI (160) and cognitively normal (CN) individuals (407). We conducted statistical analyses such as independent t-tests, Pearson's correlations, and univariate and multiple logistic regression analyses to assess group differences and associations between neuropsychological tests, ERP measures, behavioral measures, and MCI prevalence. Our findings revealed that patients with MCI demonstrated slower information-processing abilities, and exhibited poorer task execution, characterized by reduced accuracy, increased errors, and higher variability in response time, compared to CN adults. Multiple logistic regression analyses confirmed the association between some ERP and behavioral measures with MCI prevalence, independent of demographic and neuropsychological factors. A relationship was observed between neuropsychological scores, ERP, and behavioral measures. The slower information processing abilities, and poor task execution in the MCI group compared to the CN individuals suggests flawed neurological changes and reduced attentional maintenance during cognitive processing, respectively. Hence, the utilization of portable EEG devices to capture prefrontal selective attention ERPs, in combination with behavioral assessments, holds promise for the identification of mild cognitive deficits and neural alterations in individuals with MCI. This approach could potentially augment the traditional neuropsychological tests during clinical screening for MCI.

Electroencephalography Quality and Application Times in a Pediatric Emergency Department Setting: A Feasibility Study

BackgroundStatus epilepticus is the most common neurological emergency presenting to pediatric emergency departments. Nonconvulsive status epilepticus can be extremely challenging to diagnose, however, requiring electroencephalographic (EEG) confirmation for definitive diagnosis. We aimed to determine the feasibility of achieving a good-quality pediatric EEG recording within 20 minutes of presentation to the emergency department. MethodsSingle-center prospective feasibility study in Cork University Hospital, Ireland, between July 2021 and June 2022. Two-channel continuous EEG was recorded from children (1) aged <16 years and (2) with Glasgow Coma Scale <11 or a reduction in baseline Glasgow Coma Scale in the case of a child with a neurodisability. ResultsTwenty patients were included. The median age at presentation was 65.8 months (interquartile range, 23.2 to 119.0); 50% had a background diagnosis of epilepsy. The most common reason for EEG monitoring was status epilepticus (85%) followed by suspected nonconvulsive status (10%) and reduced consciousness of unknown etiology (5%). The mean length of recording was 93.1 minutes (S.D. 47.4). The mean time to application was 41.3 minutes (S.D. 11.7). The mean percent of artifact in all recordings was 19.3% (S.D. 15.9). Thirteen (65%) EEGs had <25% artifact. Artifact was higher in cases in which active airway management was ongoing. ConclusionsEEG monitoring can be achieved in a pediatric emergency department setting within one hour of presentation. Overall, artifact percentage was low outside of periods of airway manipulation. Future studies are required to determine its use in early seizure detection and its support role in clinical decision-making in these patients.

0187 Hypnograms for 365 Nights Predicts Subjective Sleep Quality in Healthy Adults: Results from the Ultra Long-Term Sleep (ULTS) Study

Abstract Introduction Sleep quality is fundamental to our somatic and mental health. However, the relationship between subjective sleep quality and sleep architecture remains poorly understood. New wearable or minimally invasive technologies facilitate the recording of electroencephalography (EEG) with lower spatial resolution than standard EEG but much greater longitudinal dispersion. This enables investigation of day-to-day variation in sleep measured directly with EEG. This study will compare EEG-derived sleep parameters with covariates such as sustained attention and subjective sleep quality. Methods Twenty-five healthy adults were implanted with a two-channel subcutaneous EEG (sqEEG) lead. Twenty subjects completed the 1-year protocol (average 32±13 years of age). Their sqEEG signals were recorded each night for 1 year alongside a morning 3-minute Psychomotor Vigilance Task (PVT) and self-reported sleep quality, which included Karolinska Sleepiness Scale (KSS). A deep learning model, U-Sleep, was fine-tuned on sqEEG with synchronized gold standard polysomnography used as ground truth. Hypnograms and sleep parameters were thus automatically calculated. Results Subjective sleep quality measured by KSS revealed a moderate negative correlation with rapid-eye-movement (REM) duration (r=-0.31, 95% CI=(-0.31, -0.31)), and total sleep time (TST) (r=-0.31, 95% CI=(-0.31, -0.31)). There was a moderate correlation between KSS and mean PVT reaction time (r=0.21, 95% CI=(0.21, 0.22)). There was a low negative correlation between PVT and TST (r=-0.1). Preliminary results indicate a moderate correlation between sleep parameters and subjective sleep quality. The correlations with PVT were lower, which suggests that 3-minute PVT is not sensitive to TST in normal sleep. However, the correlation between PVT and KSS suggests that PVT does predict subjective sleep quality, but to a smaller degree than standard sleep parameters. Conclusion Measuring day-to-day variation in high-quality EEG-based sleep recordings has the potential of creating a new branch in sleep medicine. Patients can be evaluated not only by findings in a single recording but the stability and variation of all findings can be analyzed. Preliminary results suggest that subjective sleep quality can be predicted directly from sqEEG and potentially be explained by behavioral factors in a subsequent cause-effect analysis. Support (if any) The project is supported by Innovation Fund Denmark, UNEEG medical, and T&amp;W Engineering.

Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system – the “Patch” – could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

SIMPLE EEG ASSESS WORKING MEMORY PROCESS IN DIFFERENCE GENDER

Terminology working memory often used to describe the whole process of structure in memory, including executive function, which all information retains and uses in temporary period. Simple EEG, like Headband Muse Electroencephalography (EEG) which used 4 sensors EEG is one of the tools in neuroscience research to investigate neuronal process in different condition, as well as working memory process in simple and easiest way. Results in this study hopefully provide baseline data for another neurofeedback research in cognitive process. Material &amp; Methods using the Muse, simple EEG as portable low cost and wireless two-channel EEG headband, we record 23 students at 4th grade elementary school in Jatinangor. The ? brain waves bands were recorded while they get memory span task, which had sequences base 1, read 1, interphase1, recall 1, base 2, read 2 interphase2, recall 2. EEG recorded result difference between gender. Oscillation ? brain wave will loaded into MATHLAB. In this study, we report sex related changes ? trends bands in EEG power as peak frequency for both frontal and temporoparietal sites, in several condition task in working memory process. In male participants increasing trends for ? in working memory process higher than female students. The result also stated asymetri frontal cortex and decreasing power of ? wave between TP and AF. We discuss our finding probability sex related chages their attentional and emotional in working memory process. Encoding process and retrieval also probability influence.

The relationship between interhemispheric synchrony, morphine and microstructural development of the corpus callosum in extremely preterm infants.

The primary aim of this study is to examine whether bursting interhemispheric synchrony (bIHS) in the first week of life of infants born extremely preterm, is associated with microstructural development of the corpus callosum (CC) on term equivalent age magnetic resonance imaging scans. The secondary aim is to address the effects of analgesics such as morphine, on bIHS in extremely preterm infants. A total of 25 extremely preterm infants (gestational age [GA] < 28 weeks) were monitored with the continuous two‐channel EEG during the first 72 h and after 1 week from birth. bIHS was analyzed using the activation synchrony index (ASI) algorithm. Microstructural development of the CC was assessed at ~ 30 and ~ 40 weeks of postmenstrual age (PMA) using fractional anisotropy (FA) measurements. Multivariable regression analyses were used to assess the primary and secondary aim. Analyses were adjusted for important clinical confounders: morphine, birth weight z‐score, and white matter injury score. Due to the reduced sample size, only the most relevant variables, according to literature, were included. ASI was not significantly associated with FA of the CC at 30 weeks PMA and at 40 weeks PMA (p > .5). ASI was positively associated with the administration of morphine (p < .05). Early cortical synchrony may be affected by morphine and is not associated with the microstructural development of the CC. More studies are needed to evaluate the long‐term effects of neonatal morphine treatment to optimize sedation in this high‐risk population.

Seizure detection with deep neural networks for review of two-channel electroencephalogram.

Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10-20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.

Development of an Integrated EEG/fNIRS Brain Function Monitoring System.

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer’s disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person’s home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

An effective multi-model fusion method for EEG-based sleep stage classification

Stage 1 (S1) and REM sleep are the two key stages in EEG-based sleep stage classification, which are of great significance to the study of neurocognitive ability and sleep diseases. Recently, various methods have been widely studied, and achieved good classification performance, however, most existing studies have a common problem of the low detection rate of S1 and REM sleep. In this paper, we focus on improving the detection performance of S1 and REM sleep and present an effective multi-model fusion method by using hybrid-channel EEG signals, which consists of two parts: the detection of merged stage of S1 and REM sleep and the classification between these two stages. First, we detect S1 and REM sleep by distinguishing the merged stage from other sleep stages using C-SVM model and single-channel EEG signals. To overcome the influence caused by class imbalance, a one-class OC-SVM model of the merged stage is established to correct S1 and REM sleep from the misclassified negative samples. Then, through analyzing the EEG characteristic between S1 and REM sleep and extracting the classification features of multiple sub-bands, we classify S1 and REM sleep using two-channel EEG signals. Finally, the proposed method is tested and analyzed for commonly used dataset of Sleep-EDFX. The results show that this method can effectively detect S1 and REM sleep and promote the application of sleep quality evaluation, fatigue detection, sleep disease diagnosis.

Implementation of Wireless Biosensor for Continuous EEG Monitoring in Neurological Intensive Care.

Seizure is a common complication in a neurological intensive care unit (NICU) and it requires continuous electroencephalograms (EEG) monitoring. Implementation of EEG for each bed in a NICU is very expensive and require labor work for interpretation of EEG. To provide an affordable device of EEG in NICU, we developed a low-cost wireless biosensor, which utilized the current standard of the internet of things technology (IoT). In this study, we implement a wireless biosensor for continuous EEG monitoring in NICU and discuss its feasibility. To provide a low-cost EEG device, we embraced Bluetooth and mobile phone technology, which is convenient for implementation. We build a two-channel EEG biosensor, which utilizes Bluetooth to transmit the signal to mobile phones. Then, mobile phones use Wi-Fi technology to send data to the server. Additionally, we also developed a registry to organize the patient's EEG data. In six months research period, we have 65.8% of patients collected successfully. Using 2 channel-biosensor in NCU is feasible. It also develops a neuromedical database by collecting and monitoring physiological signals to develop future neuromedical research.

Bispectral EEG (BSEEG) to assess arousal after electro-convulsive therapy (ECT)

ObjectivesPostictal confusion is encountered among most patients following electro-convulsive therapy (ECT). This study aimed to test the capabilities of a point-of-care electroencephalography (EEG) method to quantitatively measure and monitor postictal confusion immediately following ECT. We evaluated whether a two-channel frontal EEG device may provide a purely quantitative measure of the postictal state that could aid in the continuous, clinical monitoring of patients following ECT. Methods50 patients receiving ECT at the University of Iowa Hospitals and Clinics were recruited for this study. Subsequently, we obtained 5 min of frontal bispectral EEG (BSEEG) recording from a hand-held EEG device at baseline and 10–20 min following ECT. We performed power spectral density analysis to yield a “BSEEG” score and to capture the difference between patients at baseline and after ECT. ResultsThe BSEEG score was demonstrated to be a significant indicator of postictal confusion compared to baseline. For 5 patients, we also obtained continuous EEG recordings following ECT to determine the time course required for a patient's BSEEG score to return to baseline. In this subset of patients, it took between 2 and 3 h in duration for the BSEEG score to return to the baseline range. ConclusionsIn this pilot study, we showed that BSEEG score was able to distinguish between baseline condition and postictal confusion in patients treated with ECT, and assess the duration for recovery from postictal confusion following ECT. BSEEG may provide a more sensitive measure of arousal in patients following ECT compared to traditional survey-based methods.

Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy

Background The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients.Method Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested.Results An average Cohen’s kappa of kappa = 0.78pm 0.02 was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake–sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min.Conclusion This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at ClinicalTrial.gov on 19 October 2016 (ID:NCT02946151).

Universal Brain-Machine Interfaces Enabled By Flexible Scalp Electronics and Deep Convolutional Neural Networks

Electroencephalography (EEG) allows non-invasive monitoring of brain electrical activity at a fine temporal resolution. However, it is difficult to consistently capture high-quality biopotential signals due to natural variations in scalp and skin conditions. Conventional EEG systems suffer from inconsistent signal quality extensive preparation time, and uncomfortable and bulky electrode setups from long, inflexible wires and rigid electrodes. Improvements in analysis and machine learning techniques have allowed researchers to extract more information from EEG with fewer electrodes on the scalp with certain EEG paradigms. However, further hardware improvements would be required before these systems could be comfortably used daily. Here, we introduce skin-like hybrid electronics (SKINTRONICS), a fully portable, wireless, flexible biomonitoring electronics platform incorporating a soft elastomeric membrane for maximum conformability and wearability on the skin. This concept allows for a comfortable and ergonomic wearable BMI concept that can be used long-term without discomfort and is optimized for use in a rehabilitation setting for patients with brainstem injury or other motor impairment. Analytical and computational studies establish the fundamental design criteria of the SKINTRONICS, enabling seamless portable EEG recording with significantly enhanced signal quality over commercial systems. Analysis in the time domain and frequency domain are completed, with deep convolutional neural networks providing appreciable improvements over conventional techniques such as SVM. The real-time time-domain analysis with deep convolutional neural networks allows for highly accurate classification and fine machine control with high information transfer rates from only two channels. With six human subjects this wireless interface achieves a high accuracy (94.54% ± 0.90%) for a corresponding information transfer rate of 122.1 ± 3.53 bits per minute, demonstrated using a wireless wheelchair, motorized vehicle, and keyboard-less presentation using a two-channel EEG. This demonstrates great potential for this platform, which can accommodate a wide range of assistive technologies as well as different EEG paradigms like motor imagery (MI).

Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.

Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.

Convolutional neural networks for sleep stage scoring on a two-channel EEG signal

Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time-consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as the benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67\% and a Cohen's Kappa value over 0.84 compared to human experts.