Development of a Smart Helmet for Strategical BCI Applications

Transcutaneous and transcranial electrical stimulation for enhancing military performance: an update and systematic review

Introduction

Electrical stimulation (ES), including transcranial electrical stimulation (tES) and transcutaneous vagus nerve stimulation (tVNS), has shown potential for cognitive enhancement in military contexts. Various types of ES, such as transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), modulate neuronal membrane potentials and cortical excitability, potentially improving cognitive functions relevant to military training and operations.

Methods

This systematic review updates previous findings by examining studies published between 2019 and 2024 that investigated electrical stimulation effects on cognitive performance in military personnel and tasks. We focused on whether the studies addressed key questions about the generalizability of lab findings to military tasks, the frequency and intensity of adverse effects, the impact of repeated ES administration, and the ethical and regulatory considerations for its use in potentially vulnerable military populations.

Results

Eleven studies met the inclusion criteria; most demonstrated overall low to some concerns, however, two of these had overall high risk of bias. While tES and tVNS showed some promise for enhancing multitasking and visual search performance, the results were mixed, with no reliable effects on vigilance tasks.

Discussion

The reviewed studies highlight the need for a better understanding of ES mechanisms, optimal stimulation parameters, and individual differences in response to ES. They also highlight the importance of conducting high-powered research in military settings to evaluate the efficacy, safety, and ethical implications of ES. Future research should address the generalizability of lab-based results to real-world military tasks, monitor the frequency and intensity of adverse effects, and explore the long-term impacts of repeated administration. Furthermore, ethical and regulatory considerations are crucial for the responsible application of ES in military contexts, and a series of outstanding questions is posed to guide continuing research in this domain.

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

BACKGROUND

A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.

METHODS

78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.

RESULTS

The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.

CONCLUSIONS

This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD

Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.

Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography

Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.

Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm

Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks

Modern deep neural network training is based on mini-batch stochastic gradient optimization. While using extensive mini-batches improves the computational parallelism, the small batch training proved that it delivers improved generalization performance and allows a significantly smaller memory, which might also improve machine throughput. However, mini-batch size and characteristics, a key factor for training deep neural networks, has not been sufficiently investigated in training correlated group features and looping with highly complex ones. In addition, the unsupervised learning method clusters the data into different groups with similar properties to make the training process more stable and faster. Then, the supervised learning algorithm was applied with the cluster repeated mini-batch training (CRMT) methods. The CRMT algorithm changed the random minibatch characteristics in the training step into training in order of clusters. Specifically, the self-organizing maps (SOM) were used to cluster the information into n groups based on the dataset's labels Then, neural network models (ANN) were trained with each cluster using the cluster repeated mini-batch training method. Experiments conducted on EEG datasets demonstrate the survey of the proposed method and optimize it. In addition, the results in our research outperform other state-of-the-art methods.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review

BACKGROUND

As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety.

OBJECTIVE

This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors?

METHODS

We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility.

RESULTS

Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting.

CONCLUSIONS

A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Writing Blindly in Incomplete Locked-In Syndrome with A Custom-Made Switch-Operated Voice-Scanning Communicator—A Case Report

Background: Locked-In Syndrome (LIS) is a rare neurological condition in which patients’ ability to move, interact, and communicate is impaired despite their being conscious and awake. After assessing the patient’s needs, we developed a customized device for an LIS patient, as the commercial augmentative and alternative communication (AAC) devices could not be used. Methods: A 51-year-old woman with incomplete LIS for 15 years came to our laboratory seeking a communication tool. After excluding the available AAC devices, a careful evaluation led to the creation of a customized device (hardware + software). Two years later, we assessed the patient’s satisfaction with the device. Results: A switch-operated voice-scanning communicator, which the patient could control by residual movement of her thumb without seeing the computer screen, was implemented, together with postural strategies. The user and her family were generally satisfied with the customized device, with a top rating for its effectiveness: it fit well the patient’s communication needs. Conclusions: Using customized AAC and strategies provides greater opportunities for patients with LIS to resolve their communication problems. Moreover, listening to the patient’s and family’s needs can help increase the AAC’s potential. The presented switch-operated voice-scanning communicator is available for free on request to the authors.

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Incremental Validity of Multi-Method and Multi-Informant Evaluations in the Clinical Diagnosis of Preschool ADHD

OBJECTIVES

This study investigated the discriminative validity of various single or combined measurements of electroencephalogram (EEG) data, Conners' Kiddie Continuous Performance Test (K-CPT), and Disruptive Behavior Disorder Rating Scale (DBDRS) to differentiate preschool children with ADHD from those with typical development (TD).

METHOD

We recruited 70 preschoolers, of whom 38 were diagnosed with ADHD and 32 exhibited TD; all participants underwent the K-CPT and wireless EEG recording in different conditions (rest, slow-rate, and fast-rate task).

RESULTS

Slow-rate task-related central parietal delta (1-4 Hz) and central alpha (8-13 Hz) and beta (13-30 Hz) powers between groups with ADHD and TD were significantly distinct (p < .05). A combination of DBDRS, K-CPT, and specific EEG data provided the best probability scores (area under curve = 0.926, p < .001) and discriminative validity to identify preschool children with ADHD (overall correct classification rate = 85.71%).

CONCLUSIONS

Multi-method and multi-informant evaluations should be emphasized in clinical diagnosis of preschool ADHD.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

Atypical functional connectivity during rest and task-related dynamic alteration in young children with attention deficit hyperactivity disorder: An analysis using the phase-locking value

AIM

The study investigated the electroencephalography (EEG) functional connectivity (FC) profiles during rest and tasks of young children with attention deficit hyperactivity disorder (ADHD) and typical development (TD).

METHODS

In total, 78 children (aged 5-7 years) were enrolled in this study; 43 of them were diagnosed with ADHD and 35 exhibited TD. Four FC metrics, coherence, phase-locking value (PLV), pairwise phase consistency, and phase lag index, were computed for feature selection to discriminate ADHD from TD.

RESULTS

The support vector machine classifier trained by phase-locking value (PLV) features yielded the best performance to differentiate the ADHD from the TD group and was used for further analysis. In comparing PLVs with the TD group at rest, the ADHD group exhibited significantly lower values on left intrahemispheric long interelectrode lower-alpha and beta as well as frontal interhemispheric beta frequency bands. However, the ADHD group showed higher values of central interhemispheric PLVs on the theta, higher-alpha, and beta bands. Regarding PLV alterations within resting and task conditions, left intrahemispheric long interelectrode beta PLVs declined from rest to task in the TD group, but the alterations did not differ in the ADHD group. Negative correlations were observed between frontal interhemispheric beta PLVs and the Disruptive Behavior Disorder Rating Scale as rated by teachers.

CONCLUSIONS

These results, which complement the findings of other sparse studies that have investigated task-related brain FC dynamics, particularly in young children with ADHD, can provide clinicians with significant and interpretable neural biomarkers for facilitating the diagnosis of ADHD.

Task-Rate-Related Neural Dynamics Using Wireless EEG to Assist Diagnosis and Intervention Planning for Preschoolers with ADHD Exhibiting Heterogeneous Cognitive Proficiency

This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that pervasively interferes with the lives of individuals starting in childhood.

OBJECTIVE

To address the subjectivity of current diagnostic approaches, many studies have been dedicated to efforts to identify the differences between ADHD and neurotypical (NT) individuals using EEG and continuous performance tests (CPT).

APPROACH

In this study, we proposed EEG-based long short-term memory (LSTM) networks that utilize deep learning techniques with learning the cognitive state transition to discriminate between ADHD and NT children via EEG signal processing. A total of thirty neurotypical children and thirty ADHD children participated in CPT tests while being monitored with EEG. Several architectures of deep and machine learning were applied to three EEG data segments including resting state, cognitive execution, and a period containing a fusion of those.

MAIN RESULTS

The experimental results indicated that EEG-based LSTM networks produced the best performance with an average accuracy of 90.50 ± 0.81 % in comparison with the deep neural networks, the convolutional neural networks, and the support vector machines with learning the cognitive state transition of EEG data. Novel observations of individual neural markers showed that the beta power activity of the O1 and O2 sites contributed the most to the classifications, subjects exhibited decreased beta power in the ADHD group, and had larger decreases during cognitive execution.

SIGNIFICANCE

These findings showed that the proposed EEG-based LSTM networks are capable of extracting the varied temporal characteristics of high-resolution electrophysiological signals to differentiate between ADHD and NT children, and brought a new insight to facilitate the diagnosis of ADHD. The registration numbers of the institutional review boards are 16MMHIS021 and EC1070401-F.

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

Neural Dynamics for Facilitating ADHD Diagnosis in Preschoolers: Central and Parietal Delta Synchronization in the Kiddie Continuous Performance Test

The present study aimed to characterize children at risk of attention-deficit/hyperactivity disorder (ADHD) during preschool age and provide early intervention. The continuous performance test (CPT) and electroencephalography (EEG) can contribute additional valuable information to facilitate diagnosis. This study measured brain dynamics at slow and fast task rates in the CPT using a wireless wearable EEG and identified correlations between the EEG and CPT data in preschool children with ADHD. Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). The Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed simultaneously. Significant differences were observed between the groups with ADHD and TD in task-related EEG spectral powers (central as well as parietal delta, P < 0.01), which were distinct only in the slow-rate task condition. A shift from resting to the CPT task condition induced overall alpha powers decrease in the ADHD group. In the task condition, the delta powers were positively correlated with the CPT perseveration scores, whereas the alpha powers were negatively correlated with specific CPT scores mainly on perseveration and detectability (P < 0.05). These results, which complement the findings of other sparse studies that have investigated within-task-related brain dynamics, particularly in preschool children, can assist specialists working in early intervention to plan training and educational programs for preschoolers with ADHD.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Flexible graphene/GO electrode for gel-free EEG

Objective.Developments in electroencephalography (EEG) technology have allowed the use of the brain-computer interface (BCI) outside dedicated labratories. In order to achieve long-term monitoring and detection of EEG signals for BCI application, dry electrodes with good signal quality and high bio compatibility are essential. In 2016, we proposed a flexible dry electrode made of silicone gel and Ag flakes, which showed good signal quality and mechanical robustness. However, the Ag components used in our previous design made the electrode too expensive for commercial adaptation.Approach.In this study, we developed an affordable dry electrode made of silicone gel, metal flakes and graphene/GO based on our previous design. Two types of electrodes with different graphene/GO proportions were produced to explore how the amount of graphene/GO affects the electrode.Main results.During our tests, the electrodes showed low impedance and had good signal correlation to conventional wet electrodes in both the time and frequency domains. The graphene/GO electrode also showed good signal quality in eyes-open EEG recording. We also found that the electrode with more graphene/GO had an uneven surface and worse signal quality. This suggests that adding too much graphene/GO may reduce the electrods' performance. Furthermore, we tested the proposed dry electrodes' capability in detecting steady state visually evoked potential. We found that the dry electrodes can reliably detect evoked potential changes even in the hairy occipital area.Significance.Our results showed that the proposed electrode has good signal quality and is ready for BCI applications.

Classification of Lactate Level Using Resting-State EEG Measurements

The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI

Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming the impact of such user variability on BCI performance is an impending and inevitable challenge for routine applications of BCIs in the real world. To systematically explore the factors affecting BCI performance, this study embeds a Steady-State Visually Evoked Potential (SSVEP) based BCI into a "game with a purpose" (GWAP) to obtain data over significant lengths of time, under both high- and low-stress conditions. Ten healthy volunteers played a GWAP that resembles popular match-three games, such as Jewel Quest, Zoo Boom, or Candy Crush. We recorded the target search time, target search accuracy, and EEG signals during gameplay to investigate the impacts of stress on EEG signals and BCI performance. We used Canonical Correlation Analysis (CCA) to determine whether the subject had found and attended to the correct target. The experimental results show that SSVEP target-classification accuracy is reduced by stress. We also found a negative correlation between EEG spectra and the SNR of EEG in the frontal and occipital regions during gameplay, with a larger negative correlation for the high-stress conditions. Furthermore, CCA also showed that when the EEG alpha and theta power increased, the search accuracy decreased, and the spectral amplitude drop was more evident under the high-stress situation. These results provide new, valuable insights into research on how to improve the robustness of BCIs in real-world applications.

EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study

We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving exercises with different terrain complexities (low, medium, and high) while we recorded their electroencephalographic (EEG) activity. We focused on variations in the theta EEG power spectrum, a well-known index of mental workload. We also assessed performance and subjective ratings of task load. The theta EEG power spectrum in the frontal, temporal, and occipital areas were higher during the most complex scenarios. Performance (number of engine stops) and subjective data supported these findings. Our findings strengthen previous results found in civilians on the relationship between driver mental workload and the theta EEG power spectrum. This suggests that EEG activity can give relevant insight into mental workload variations in an objective, unbiased fashion, even during real training and/or operations. The continuous monitoring of the warfighter not only allows instantaneous detection of over/underload but also might provide online feedback to the system (either automated equipment or the crew) to take countermeasures and prevent fatal errors.