Channel Electroencephalography Research Articles

Motor imagery electroencephalography channel selection based on deep learning: A shallow convolutional neural network

Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much attention because of their potential to communicate with the surrounding environment in a specific way without the need for muscular and physical movement. Despite these advantages, these signals are difficult to detect due to their low signal to noise (SNR) rate and non-stationary and dynamic nature. Convolutional neural networks (CNN) can extract appropriate spatial and temporal features without the need for separate feature extraction and classification steps. Clean input data for CNN significantly improves its performance, but sometimes, common interference occurs between multi-channel signals. This challenge, along with the noisy EEG signals, degrades the performance of CNN networks. This paper presents a channel selection method based on convolutional neural networks to obviate these challenges. The proposed shallow convolutional neural network (SCNN) is designed with temporal convolution and pointwise convolution (using 1×1 convolution) to select the best channel with minimal computational load. Following the channel selection step, the multi-layer fusion CNN model was used to classify the signals. Choosing suitable and relevant EEG channels for motor imagery tasks makes it possible to create appropriate inputs for the classification model so that the model can be improved in the end. For High Gamma Dataset and Brian-Computer Interface (BCI) Competition IV-2a, the following accuracies were obtained: 81.15% and 72.01%, which are higher than when the other channel selections such as Mutual Information, Sequential Feature Forward Selection (SFFS) and wrapper methods are used.

EMPT: a sparsity Transformer for EEG-based motor imagery recognition.

Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm. We propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer. EMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods. The MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.

Analysis of Minimal Channel Electroencephalography for Wearable Brain–Computer Interface

Electroencephalography (EEG)-based brain—computer interface (BCI) is a non-invasive technology with potential in various healthcare applications, including stroke rehabilitation and neuro-feedback training. These applications typically require multi-channel EEG. However, setting up a multi-channel EEG headset is time-consuming, potentially resulting in patient reluctance to use the system despite its potential benefits. Therefore, we investigated the appropriate number of electrodes required for a successful BCI application in wearable devices using various numbers of EEG channels. EEG multi-frequency features were extracted using the “filter bank” feature extraction technique. A support vector machine (SVM) was used to classify a left/right-hand opening/closing motor imagery (MI) task. Nine electrodes around the center of the scalp (F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) provided high classification accuracy with a moderate setup time; hence, this system was selected as the minimal number of required channels. Spherical spline interpolation (SSI) was also applied to investigate the feasibility of generating EEG signals from limited channels on an EEG headset. We found classification accuracies of interpolated groups only, and combined interpolated and collected groups were significantly lower than the measured groups. The results indicate that SSI may not provide additional EEG data to improve classification accuracy of the collected minimal channels. The conclusion is that other techniques could be explored or a sufficient number of EEG channels must be collected without relying on generated data. Our proposed method, which uses a filter bank feature, session-dependent training, and the exploration of many groups of EEG channels, offers the possibility of developing a successful BCI application using minimal channels on an EEG device.

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement.

Significant progress has been made in linking measures of individual alpha frequency (IAF) and pain. A lower IAF has been associated with chronic neuropathic pain and with an increased sensitivity to pain in healthy young adults. However, the translation of these findings to chronic low back pain (cLBP) are sparse and inconsistent. To address this limitation, we assessed IAFs in a cohort of 70 individuals with cLBP, implemented 3 different IAF calculations, and separated cLBP subjects based on psychological variables. We hypothesized that a higher fear movement in cLBP is associated with a lower IAF at rest. A total of 10 minutes of resting data were collected from 128 electroencephalography channels. Our results offer 3 novel contributions to the literature. First, the high fear group had a significantly lower peak alpha frequency. The high fear group also reported higher pain and higher disability. Second, we calculated individual alpha frequency using 3 different but established methods; the effect of fear on individual alpha frequency was robust across all methods. Third, fear of movement, pain intensity, and disability highly correlated with each other and together significantly predicted IAF. Our findings are the first to show that individuals with cLBP and high fear have a lower peak alpha frequency.

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.

The Effectiveness of a Neurofeedback-Assisted Mindfulness Training Program Using a Mobile App on Stress Reduction in Employees: Randomized Controlled Trial.

Mindfulness-based training programs have consistently shown efficacy in stress reduction. However, questions regarding the optimal duration and most effective delivery methods remain. This research explores a 4-week neurofeedback-assisted mindfulness training for employees via a mobile app. The study's core query is whether incorporating neurofeedback can amplify the benefits on stress reduction and related metrics compared with conventional mindfulness training. A total of 92 full-time employees were randomized into 3 groups: group 1 received mobile mindfulness training with neurofeedback assistance (n=29, mean age 39.72 years); group 2 received mobile mindfulness training without neurofeedback (n=32, mean age 37.66 years); and group 3 were given self-learning paper materials on stress management during their first visit (n=31, mean age 38.65 years). The primary outcomes were perceived stress and resilience scales. The secondary outcomes were mindfulness awareness, emotional labor, occupational stress, insomnia, and depression. Heart rate variability and electroencephalography were measured for physiological outcomes. These measurements were collected at 3 different times, namely, at baseline, immediately after training, and at a 4-week follow-up. The generalized estimating equation model was used for data analysis. The 4-week program showed significant stress reduction (Wald χ22=107.167, P<.001) and improvements in psychological indices including resilience, emotional labor, insomnia, and depression. A significant interaction was observed in resilience (time × group, Wald χ42=10.846, P=.02). The post hoc analysis showed a statistically significant difference between groups 1 (least squares mean [LSM] 21.62, SE 0.55) and 3 (LSM 19.90, SE 0.61) at the posttraining assessment (P=.008). Group 1 showed a significant improvement (P<.001) at the posttraining assessment, with continued improvements through the 1-month follow-up assessment period (LSM 21.55, SE 0.61). Physiological indices were analyzed only for data of 67 participants (22 in group 1, 22 in group 2, and 23 in group 3) due to the data quality. The relaxation index (ratio of alpha to high beta power) from the right electroencephalography channel showed a significant interaction (time × group, Wald χ22=6.947, P=.03), with group 1 revealing the highest improvement (LSM 0.43, SE 0.15) compared with groups 2 (LSM -0.11, SE 0.10) and 3 (LSM 0.12, SE 0.10) at the 1-month follow-up assessment. The study demonstrated that the neurofeedback-assisted group achieved superior outcomes in resilience and relaxation during the 4-week mobile mindfulness program. Further research with larger samples and long-term follow-up is warranted. ClinicalTrials.gov NCT03787407; https://clinicaltrials.gov/ct2/show/NCT03787407.

Multistage Anxiety State Recognition based on EEG Signal using Safe-Level SMOTE

Anxiety is a complicated emotional condition that has a detrimental effect on people’s physical and mental health. It is critical to accurately recognize anxiety levels in early stage. The anxiety can be detected by pattern of brain signal using brain imaging tools. However, the common problem with dataset acquired from brain is imbalanced class distribution. Hence, the purpose of this work is to mitigate the imbalanced class distribution issue by removing data outlier and using improved Synthetic Minority Oversampling Technique (SMOTE) for improving the classification performance. This work used of the freely accessible Database for Anxious States based on Psychological stimulation (DASPS) that comprises of 14 channels electroencephalography (EEG) signal. It acquired from 23 subjects when they were exposed to psychological stimuli that elicited fear. The DASPS need to be processed for removing noises, extracting important features and sampling with Safe-level SMOTE method. Then, the processed DASPS was categorized into three types of model: Model A, Model B, and Model C. The feature Model C from enhanced DASPS class distribution obtained the precision of 89.7% and accuracy of 89.5% using optimized K-nearest neighbour (K-NN) algorithm. The proposed method showed outstanding classification performance than others existing methods in recognizing multistage anxiety.

Region-specific changes in brain activity and memory after continuous positive airway pressure therapy in obstructive sleep apnea: a pilot high-density electroencephalography study.

Limited channel electroencephalography (EEG) investigations in obstructive sleep apnea (OSA) have revealed deficits in slow wave activity (SWA) and spindles during sleep and increased EEG slowing during resting wakefulness. High-density EEG (Hd-EEG) has also detected local parietal deficits in SWA (delta power) during NREM. It is unclear whether effective continuous positive airway pressure (CPAP) treatment reverses regional SWA deficits, and other regional sleep and wake EEG abnormalities, and whether any recovery relates to improved overnight memory consolidation. A clinical sample of men with moderate-severe OSA underwent sleep and resting wake recordings with 256-channel Hd-EEG before and after 3 months of CPAP. Declarative and procedural memory tasks were administered pre- and post-sleep. Topographical spectral power maps and differences between baseline and treatment were compared using t-tests and statistical nonparametric mapping (SnPM). In 11 compliant CPAP users (5.2 ± 1.1 hours/night), total sleep time did not differ after CPAP but N1 and N2 sleep were lower and N3 was higher. Centro-parietal gamma power during N3 increased and fronto-central slow spindle activity during N2 decreased (SnPM < 0.05). No other significant differences in EEG power were observed. When averaged specifically within the parietal region, N3 delta power increased after CPAP (p = 0.0029) and was correlated with the change in overnight procedural memory consolidation (rho = 0.79, p = 0.03). During resting wakefulness, there were trends for reduced delta and theta power. Effective CPAP treatment of OSA may correct regional EEG abnormalities, and regional recovery of SWA may relate to procedural memory improvements in the short term.

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey

From August 19 to 21, 2022, the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing, China. Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI. This paper introduces the algorithms in the motor imagery (MI) classification area, describes the competition content and set, and summarizes the algorithms and results of the top five teams in the finals. First, the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced, followed by the introduction of the algorithms of the top five teams in the final step by step, including electroencephalography channel selection, data length selection, data preprocessing, data augmentation, classification network, training, and testing settings. Finally, the highlights and results of each algorithm are discussed.

Clinical Implication of Maumgyeol Basic Service-the 2 Channel Electroencephalography and a Photoplethysmogram-based Mental Health Evaluation Software.

Maumgyeol Basic service is a mental health evaluation and grade scoring software using the 2 channels EEG and photoplethysmogram (PPG). This service is supposed to assess potential at-risk groups with mental illness more easily, rapidly, and reliably. This study aimed to evaluate the clinical implication of the Maumgyeol Basic service. One hundred one healthy controls and 103 patients with a psychiatric disorder were recruited. Psychological evaluation (Mental Health Screening for Depressive Disorders [MHS-D], Mental Health Screening for Anxiety Disorders [MHS-A], cognitive stress response scale [CSRS], 12-item General Health Questionnaire [GHQ-12], Clinical Global Impression [CGI]) and digit symbol substitution test (DSST) were applied to all participants. Maumgyeol brain health score and Maumgyeol mind health score were calculated from 2 channel frontal EEG and PPG, respectively. Participants were divided into three groups: Maumgyeol Risky, Maumgyeol Good, and Maumgyeol Usual. The Maumgyeol mind health scores, but not brain health scores, were significantly lower in the patients group compared to healthy controls. Maumgyeol Risky group showed significantly lower psychological and cognitive ability evaluation scores than Maumgyeol Usual and Good groups. Maumgyel brain health score showed significant correlations with CSRS and DSST. Maumgyeol mind health score showed significant correlations with CGI and DSST. About 20.6% of individuals were classified as the No Insight group, who had mental health problems but were unaware of their illnesses. This study suggests that the Maumgyeol Basic service can provide important clinical information about mental health and be used as a meaningful digital mental healthcare monitoring solution to prevent symptom aggravation.

MI-based BCI with accurate real-time three-class classification processing and light control application.

The use of brain-computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.

Neuropsychiatric symptoms of Alzheimer’s dementia and their association with dorsolateral prefrontal cortex TMS evoked potentials

AbstractBackgroundAlzheimer’s Dementia (AD) is characterized by cognitive and neuropsychiatric symptoms (NPS). Recently, cognitive decline in AD has been linked to increased cortical excitability. However, the relationship between NPS and cortical excitability remains unclear. This study investigated the relationship between NPS and cortical excitability in the dorsolateral prefrontal cortex (DLPFC).Method24 participants with probable AD diagnosed using National Institute of Aging‐Alzheimer’s Association criteria (Mage (SD) = 74.1 (7.2) years) were recruited and followed for up to 6 months. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA). NPS were assessed using the Neuropsychiatric Inventory Questionnaire (NPI‐Q) total score and subdomain scores. Single pulse transcranial magnetic stimulation (TMS) was applied at the DLPFC (localized using MRI‐based neuronavigation) at a frequency of 0.1 Hz, at sufficient intensity; and collected using 62 channel electroencephalography (EEG) recorded from the scalp. TMS‐evoked potential (TEP) peak amplitudes for individual TEP peaks (P30, N45 and P60) were calculated.ResultNPI‐Q severity score was positively correlated with DLPFC TEP peak P30 (r (19) = .64, p = .003) and negatively correlated with DLPFC TEP peak N45 (r (22) = ‐.46, p = .033). These associations remained significant after controlling for age, sex and cognition (DLPFC TEP peak P30 (ß = .93, p = .001) and N45 (ß = ‐.47, p = .031)). Further, NPI‐Q severity score at 6 month follow up negatively correlated with baseline DLPFC TEP peak N45 (r (16) = ‐.56, p = .025). Among NPI‐Q subdomains, apathy was positively correlated with DLPFC TEP peak P30 (r (19) = .64, p = .003) and negatively correlated with DLPFC TEP peak N45 (r (22) = ‐.42, p = .049).ConclusionNPS burden in AD was positively associated with excitatory activity (i.e., P30) and negatively associated with inhibitory activity (i.e., N45) using combined TMS‐EEG over the DLPFC. These associations were largely driven by the apathy domain. These findings may have implications for developing neurophysiological biomarkers and targeted treatment interventions for NPS in AD.

Mental performance classification using fused multilevel feature generation with EEG signals

ABSTRACT Mental performance classification is a critical issue for brain-computer interfaces. Accurate and reliable classification of good or bad mental performance gives important clues for the preliminary diagnosis of some diseases and mental stress. In this work, we put forward an objective artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks. The proposed model consists of: (i) multilevel feature extraction based on statistical and texture analysis methods, (ii) feature ranking and selection with a Chi2 method, (iii) classification, and (iv) weightless majority voting classifier. The novelty of the presented model comes from multilevel fused feature generation. The presented model was developed using 20 channel electroencephalography data from 36 subjects. The signals were captured while the subjects were performing mental arithmetic tasks. The individual datasets were labeled as either good or bad, based on the task results. We have obtained an accuracy of 96.77% using O2 channel with a k-nearest neighbor classifier and reached 100.0% accuracy with the majority voting classifier. Our results indicate that it is possible to determine mental performance with artificial intelligence. That might be a steppingstone to establish objective measures for the clarity of thought during a wide range of mental tasks.

Changes in the cortical network during sleep stage transitions.

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Comparison Between Deltascan Single Channel Electroencephalography (EEG), Confusion Assessment Method-Intensive Care Unit (CAM-ICU) Score and Clinical Assessment in Diagnosing Delirium in Intubated Patients in the Intensive Care Unit.

BackgroundThe aim of this article is to assess the feasibility of using single-channel electroencephalography (EEG) measurement for detecting delirium in intubated Intensive care (ICU) patients and to assess the level of agreement between the EEG measurements, the CAM-ICU score and the clinical diagnosis of delirium.Materials and methodsThis study was an exploratory pilot between May 2021 and September 2021 including intubated patients in the ICU. For this study the Prolira® (Arnhem, The Netherlands) Deltascan single-channel EEG was used and compared with the Confusion Assessment Method (CAM)-ICU and the clinical diagnosis of delirium by ICU physicians.ResultsIn total 23 patients were found eligible for this study, of which 20 were included in the final analysis. The patients mean age was 63.0 ± 8.8 years, and the majority (thirteen) was male (65%). In total 17 of the 20 patients (85%) received the diagnosis delirium by the medical team. There were no statistically significant differences between the Deltascan and CAM-ICU measurements in diagnosing delirium per time point (p values respectively 0.21; 0.90; 0.34; 0.11; 0.056 and 0.091). AUCs for the agreement between the CAM-ICU and the Deltascan measurements were respectively: 0.676 ± 0.205; 0.333 ± 0.224; 0.402 ± 0.146; 0.488 ± 0.202; 0.06 ± 0.077 and 0.06 ± 0.109 (all p>0.05). AUCs for the level of agreement between the clinical diagnosis delirium and Deltascan were: 0.676 ± 0.152; 0.686 ± 0.146; 0.711 ± 0.132; 0.688 ± 0.136; 0.500 ± 0.158 and 0.700 ± 0.211 (all p>0.05).ConclusionIn this exploratory study, we showed that there is no statistical agreement between CAM-ICU and Delta scan measurements. Secondly, there is a higher agreement, although not statistically significant between the clinical diagnoses of a delirium (by a clinician) with the Deltascan measurements. Despite this small study we think that the Deltascan can be of additional value in intubated ICU patients and therefore larger studies are needed to substantiate our findings.

0261 Pre-Sleep Arousal Predicts Subsequent Night’s REM Frontal Theta Power in a Naturalistic Context

Abstract Introduction Decreased rapid eye movement (REM) frontal theta spectral activity during sleep has been identified following intense negative experiences in both naturalistic and experimental settings. It is unknown, however, whether this spectral marker a) also appears following lower intensity naturalistic negative affective experiences, and b) whether it is responsive to arousal, to negative valence, or to both. Methods In a community sample of adults (N = 50, age range 21-49 [M = 30.30, SD = 7.85]) with no mental health or sleep disorders except bruxism, we separately measured naturalistic experiences of negative valence and arousal 40-60 minutes prior to bedtime, then assessed at-home sleep using a single night of ambulatory polysomnography with 8 electroencephalography channels. REM frontal theta was calculated as the mean relative spectral power density between 4.9-8 Hz at F3 and F4 during REM. Affective experience was assessed with a 0-100 slider rating filled out on participants’ smartphones. Negative valence was assessed by asking, “How negative are you feeling right now?” Arousal was assessed using a composite of somatic and cognitive arousal that reverse scored and averaged two ratings, “How calm is your body?” and “How quiet is your mind?” Analyses were conducted with Bayesian general linear models using flat priors. Results Contrary to our hypothesis, pre-sleep arousal was linearly associated with increased REM frontal theta (ß = 2.06 [0.95, 3.20], pMAP &amp;lt; 0.001). A quadratic association fit the data better than the linear model (Bayes Factor = 250.47), such that, for low to moderate pre-sleep arousal, REM frontal theta power increased as arousal ratings increased, but for moderate to high pre-sleep arousal, REM frontal theta power decreased as arousal ratings increased (ß = -2.04 [-3.18, -0.90], pMAP &amp;lt; 0.001). Both associations remained significant even adjusting for negative valence. Contrary to expectations, pre-sleep negative valence was not associated with REM frontal theta. Conclusion Separately measuring affect dimensions revealed that REM frontal theta power was selectively responsive to naturalistic pre-sleep arousal. At higher arousal ratings, our results matched prior findings. However, at lower arousal ratings, the association was reversed, suggesting that spectral markers may vary across affective intensities and contexts. Support (If Any) National Science Foundation Graduate FellowshipR56, National Institute of Dental and Craniofacial Research, “Emotion dysregulation and sleep-time masticatory muscle activity in sleep bruxism,” DE025321

Evidence That Brain-Controlled Functional Electrical Stimulation Could Elicit Targeted Corticospinal Facilitation of Hand Muscles in Healthy Young Adults

ObjectivesBrain-computer interface (BCI)–controlled functional electrical stimulation (FES) has been used in rehabilitation for improving hand motor function. However, mechanisms of improvements are still not well understood. The objective of this study was to investigate how BCI-controlled FES affects hand muscle corticospinal excitability. Materials and MethodsA total of 12 healthy young adults were recruited in the study. During BCI calibration, a single electroencephalography channel from the motor cortex and a frequency band were chosen to detect event-related desynchronization (ERD) of cortical oscillatory activity during kinesthetic wrist motor imagery (MI). The MI-based BCI system was used to detect active states on the basis of ERD activity in real time and produce contralateral wrist extension movements through FES of the extensor carpi radialis (ECR) muscle. As a control condition, FES was used to generate wrist extension at random intervals. The two interventions were performed on separate days and lasted 25 minutes. Motor evoked potentials (MEPs) in ECR (intervention target) and flexor carpi radialis (FCR) muscles were elicited through single-pulse transcranial magnetic stimulation of the motor cortex to compare corticospinal excitability before (pre), immediately after (post0), and 30 minutes after (post30) the interventions. ResultsAfter the BCI-FES intervention, ECR muscle MEPs were significantly facilitated at post0 and post30 time points compared with before the intervention (pre), whereas there were no changes in the FCR muscle corticospinal excitability. Conversely, after the random FES intervention, both ECR and FCR muscle MEPs were unaffected compared with before the intervention (pre). ConclusionsOur results demonstrated evidence that BCI-FES intervention could elicit muscle-specific short-term corticospinal excitability facilitation of the intervention targeted (ECR) muscle only, whereas randomly applied FES was ineffective in eliciting any changes. Notably, these findings suggest that associative cortical and peripheral activations during BCI-FES can effectively elicit targeted muscle corticospinal excitability facilitation, implying possible rehabilitation mechanisms.

Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices.

Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operator's cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.