Review on portable EEG technology in educational research

Could the Suboccipital Release Technique Result in a Generalized Relaxation and Self-Perceived Improvement? A Repeated Measure Study Design

Background: Musculoskeletal disorders such as cervicogenic headaches present with suboccipital muscle hypertonicity and trigger points. One manual therapy intervention commonly used to target the suboccipital muscles is the suboccipital release technique, previously related to positive systemic effects. Therefore, this study aimed to determine the immediate and short-term effects of the Suboccipital Release Technique (SRT) on brainwave activity in a subgroup of healthy individuals. Methods: Data were collected from 37 subjects (20 females and 17 males, with a mean age of 24.5). While supine, the subjects underwent a head hold followed by suboccipital release. A total of four 15 s electroencephalogram (EEG) measurements were taken and a Global Rating of Change Scale was used to assess self-perception. Results: There was a statistically significant difference (p < 0.005) in various band waves under the following electrodes: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, and FC6. An 8-point range in the Global Rating of Change Scores with a mean score of 1.649 (SD = 1.719 and SE = 0.283) supported the hypothesis of a self-perceived benefit from the intervention. Conclusions: The results of this study indicate that the suboccipital release technique significantly affects brain wave activity throughout different brain regions. This change is likely not the result of any placebo effect and correlates highly with the subject's self-perception of a change following the intervention. These findings support the clinical use of the suboccipital release technique when a centralized effect is desired.

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.

Exploring Embodied Cognition and Brain Dynamics Under Multi-Tasks Target Detection in Immerse Projector-Based Augmented Reality (IPAR) Scenarios

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.

The moving wave: Applications of the mobile EEG approach to study human attention

Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.

Wireless ear EEG to monitor drowsiness

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

The convergence of naturalistic paradigms and cognitive neuroscience methods to investigate memory and its development

Studies that involve lab-based stimuli (e.g., words, pictures) are fundamental in the memory literature. At the same time, there is growing acknowledgment that memory processes assessed in the lab may not be analogous to how memory operates in the real world. Naturalistic paradigms can bridge this gap and over the decades a growing proportion of memory research has involved more naturalistic events. However, there is significant variation in the types of naturalistic studies used to study memory and its development, each with various advantages and limitations. Further, there are notable gaps in how often different types of naturalistic approaches have been combined with cognitive neuroscience methods (e.g., fMRI, EEG) to elucidate the neural processes and substrates involved in memory encoding and retrieval in the real world. Here we summarize and discuss what we identify as progressively more naturalistic methodologies used in the memory literature (movie, virtual reality, staged-events inside and outside of the lab, photo-taking, and naturally occurring event studies). Our goal is to describe each approach's benefits (e.g., naturalistic quality, feasibility), limitations (e.g., viability of neuroimaging method for event encoding versus event retrieval), and discuss possible future directions with each approach. We focus on child studies, when available, but also highlight past adult studies. Although there is a growing body of child memory research, naturalistic approaches combined with cognitive neuroscience methodologies in this domain remain sparse. Overall, this viewpoint article reviews how we can study memory through the lens of developmental cognitive neuroscience, while utilizing naturalistic and real-world events.

A scoping review on the use of consumer-grade EEG devices for research

BACKGROUND

Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.

PURPOSE

The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.

METHODS

We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.

RESULTS

We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.

CONCLUSIONS

Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.

Analyzing neural activity under prolonged mask usage through EEG

In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans' neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG

Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.

Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students' Attention and the Estimation of Academic Performance in Secondary School

The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students' attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students' attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12-30 Hz) and students' academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments.

Exploring the potential of eye tracking on personalized learning and real-time feedback in modern education

Eye tracking is one of the techniques used to investigate cognitive mechanisms involved in the school context, such as joint attention and visual perception. Eye tracker has portability, straightforward application, cost-effectiveness, and infant-friendly neuroimaging measures of cognitive processes such as attention, engagement, and learning. Furthermore, the ongoing software enhancements coupled with the implementation of artificial intelligence algorithms have improved the precision of collecting eye movement data and simplified the calibration process. These characteristics make it plausible to consider eye-tracking technology a promising tool to assist the teaching-learning process in school routines. However, eye tracking needs to be explored more as an educational instrument for real-time classroom activities and teachers' feedback. This perspective article briefly presents the fundamentals of the eye-tracking technique and four illustrative examples of employing this method in everyday school life. The first application shows how eye tracker information may contribute to teacher assessment of students' computational thinking in coding classes. In the second and third illustrations, we discuss the additional information provided by the eye-tracker to the teacher assessing the student's strategies to solve fraction problems and chart interpretation. The last illustration demonstrates the potential of eye tracking to provide Real-time feedback on learning difficulties/disabilities. Thus, we highlight the potential of the eye tracker as a complementary tool to promote personalized education and discuss future perspectives. In conclusion, we suggest that an eye-tracking system could be helpful by providing real-time student gaze leading to immediate teacher interventions and metacognition strategies.

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.

Inter-brain coupling reflects disciplinary differences in real-world classroom learning

The classroom is the primary site for learning. A vital feature of classroom learning is the division of educational content into various disciplines. While disciplinary differences could substantially influence the learning process toward success, little is known about the neural mechanism underlying successful disciplinary learning. In the present study, wearable EEG devices were used to record a group of high school students during their classes of a soft (Chinese) and a hard (Math) discipline throughout one semester. Inter-brain coupling analysis was conducted to characterize students' classroom learning process. The students with higher scores in the Math final exam were found to have stronger inter-brain couplings to the class (i.e., all the other classmates), whereas the students with higher scores in Chinese were found to have stronger inter-brain couplings to the top students in the class. These differences in inter-brain couplings were also reflected in distinct dominant frequencies for the two disciplines. Our results illustrate disciplinary differences in the classroom learning from an inter-brain perspective, suggesting that an individual's inter-brain coupling to the class and to the top students could serve as potential neural correlates for successful learning in hard and soft disciplines correspondingly.

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Objective.Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science.Approach.First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network) and end-to-end (Long Short Term Memory, Residual Network, and EEGNet) machine learning methods.Main results.Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states.Significance.The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.

Multimodal Fast–Slow Neural Network for learning engagement evaluation

Purpose

Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.

Design/methodology/approach

The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.

Findings

Experimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.

Originality/value

This study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing a continuous brain rate, an index of cognitive activation, and does not require human declarative knowledge. The aim is to induce models automatically from data, supporting replicability, generalisability and applicability across fields and contexts. This specific method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data, aimed at fitting a novel brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had, on average, a test Mean Absolute Percentage Error of around 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. These findings point to the existence of quasi-stable blocks of automatically learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Additionally, across-subject models, induced with data from an increasing number of participants, thus trained with data containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable and does not rely on ad hoc human crafted models.

Deep Learning Adapted to Differential Neural Networks Used as Pattern Classification of Electrophysiological Signals

This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic convergence of the classification error and the weights of the DDNN. Then, an extension to include an arbitrary number of hidden layers in the DDNN is analyzed. The learning laws for this general form of the DDNN offer a contribution to the deep learning framework for signal classification with biological nature and dynamic structures. The DDNN is used to classify electroencephalographic signals from volunteers that perform an identification graphical test. The classification results show exponential growth in the signal classification accuracy from 82 percent with one layer to 100 percent with three hidden layers. Working with DDNN instead of static deep neural networks (SDNN) represents a set of advantages, such as processing time and training period reduction up to almost 100 times, and the increment of the classification accuracy while working with less hidden layers than working with SDNN, which are highly dependent on their topology and the number of neurons in each layer. The DDNN employed fewer neurons due to the induced feedback characteristic.

Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals

The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer's multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.

Miniaturization for wearable EEG systems: recording hardware and data processing

As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.

EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children

Advances in mobile electroencephalography (EEG) technology have made it possible to examine covert cognitive processes in real-world settings such as student attention in the classroom. Here, we outline research using wired and wireless EEG technology to examine attention in elementary school children across increasingly naturalistic paradigms in schools, ranging from a lab-based paradigm where children met one-on-one with an experimenter in a field laboratory to mobile EEG testing conducted in the same school during semi-naturalistic classroom lessons. Despite an increase of data loss with the classroom-based paradigm, we demonstrate that it is feasible to collect quality data in classroom settings with young children. We also provide a test case for how robust EEG signals, such as alpha oscillations, can be used to identify measurable differences in covert processes like attention in classrooms. We end with pragmatic suggestions for researchers interested in employing naturalistic EEG methods in real-world, multisensory contexts.

Intelligenzdiagnostik der Zukunft

Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.

Applying Interleaving Strategy of Learning Materials and Perceptual Modality to Address Secondary Students' Need to Restore Cognitive Capacity

Online courses are prevalent around the world, especially during the COVID-19 pandemic. Long hours of highly demanding online learning can lead to mental fatigue and cognitive depletion. According to Attention Restoration Theory, 'being away' or a mental shift could be an important strategy to allow a person to recover from the cognitive overload. The present study aimed to test the interleaving strategy as a mental shift method to help sustain students' online learning attention and to improve learning outcomes. A total of 81 seventh-grade Chinese students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). We collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data during the 40 min of online learning, and test results to assess learning outcomes. The results showed that the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning online classes.

Factors influencing students' adoption intention of brain–computer interfaces in a game-learning context

Purpose

A research framework that explains adoption intention in students with regard to brain–computer interface (BCI) games in the learning context was proposed and empirically examined.

Design/methodology/approach

In this study, an approach integrating the decomposed theory of planned behavior, perceived playfulness, risk and the task–technology fit (TTF) concept was used to assess data collected using a post-experiment questionnaire from a student sample in Taiwan. The research model was tested using the partial least-squares structural equation modeling (PLS-SEM) technique.

Findings

Attitude, subjective norms and TTF were shown to impact intention to play the BCI game significantly, while perceived behavioral control did not show a significant impact. The influence of superiors and peers was found to positively predict subjective norms. With the exception of perceived ease of use, all of the proposed antecedents were found to impact attitude toward BCI games. Technology facilitating conditions and BCI technology characteristics were shown to positively determine perceived behavior control and TTF, respectively. However, the other proposed factors did not significantly influence the latter two dependents.

Originality/value

This research contributes to the nascent literature on BCI games in the context of learning by highlighting the influence of belief-related psychological factors on user acceptance of BCI games. Moreover, this study highlights the important, respective influences of perceived playfulness, risk and TTF on users' perceptions of a game, body monitoring and technology implementation, each of which is known to influence willingness to play.

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals

Teaching is an activity that requires understanding the class's reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel's model, we grouped the most important Ekman's facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students' level of attention for in-presence lectures.

Implementation of the Modern Immersive Learning Model CPLM

The digitalization of industrial processes is being driven forward worldwide. In parallel, the education system must also be transformed. Currently, education does not follow the opportunities and development of technologies. We can ask ourselves how we can integrate technologies into a traditional learning process or how we can adapt the learning process to these technologies. We focused on robotics education in secondary vocational education. The paper contains research results from a modern learning model that addresses student problem-solving using cyber–physical systems. We proposed a reference model for industrial robotics education in the 21st century based on an innovative cyber-physical didactic model (CPLM). We conducted procedure time measurements, questionnaire evaluations, and EEG evaluations. We could use VR to influence the improvement of spatial and visual memory. The more intense representation of the given information influences multiple centers in the brain and, thus, the formation of multiple neural connections. We can influence knowledge, learning more effectively with short-term training in the virtual world than with classical learning methods. From the studied resources, we can conclude that the newer approach to teaching robotics is not yet available in this form. The emerging modern technologies and the possibility of developing training in this area should be investigated further.

A Modified LSTM Framework for Analyzing COVID-19 Effect on Emotion and Mental Health during Pandemic Using the EEG Signals

COVID-19, a WHO-declared public health emergency of worldwide concern, is quickly spreading over the world, posing a physical and mental health hazard. The COVID-19 has resulted in one of the world's most significant worldwide lockdowns, affecting human mental health. In this research work, a modified Long Short-Term Memory (MLSTM)-based Deep Learning model framework is proposed for analyzing COVID-19 effect on emotion and mental health during the pandemic using electroencephalogram (EEG) signals. The participants of this study were volunteers that recovered from COVID-19. The EEG dataset of 40 people is collected to predict emotion and mental health. The results of the MLSTM model are also compared with the other literature classifiers. With an accuracy of 91.26%, the MLSTM beats existing classifiers when using the 70-30 partitioning technique.

Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence

Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.

Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects' activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant's blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Revisión de electroencefalografía portable y su aplicabilidad en neurociencias

La electroencefalografía (EEG) es una técnica que permite registrar la actividad eléctrica del cerebro y ha sido estudiada durante los últimos cien años en diferentes ámbitos de la neurociencia. En los últimos años se ha investigado y desarrollado equipos de medición que sean portables y que permitan una buena calidad de la señal, por lo cual se realizó una revisión bibliográfica de las compañías fabricantes de algunos dispositivos de electroencefalografía portable disponibles en el mercado, se exponen sus características principales, algunos trabajos encontrados que fueron realizados con los dispositivos, comparaciones entre los mismos y una discusión acerca de las ventajas y desventajas de sus características. Finalmente se concluye que a la hora de comprar un dispositivo para electroencefalografía portable es necesario tener en cuenta el uso que se le va a dar y el costo-beneficio que tiene el equipo de acuerdo con sus características. Encephalography is a technique that allows the recording of electrical activity of the brain and has been studied during the last hundred years in different areas of neuroscience. For several years, measuring equipment that are portable and that allow a good signal quality to have been researched and developed, so a literature review of the manufacturing companies of some of portable electroencephalography devices available on the market was carried out: Its main features are exposed, as well as some of the work found that were made with those, comparisons between them and a discussion about the advantages and disadvantages of their features. It is concluded that, when a portable encephalography device is bought, it’s necessary to take into consideration the use that it will be having and the cost-benefit that the device has according to its features.

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review

OBJECTIVE

Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities.

APPROACH

For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application.

MAIN RESULTS

In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained.

SIGNIFICANCE

This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.

Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience

As the field of educational neuroscience continues to grow, questions have emerged regarding the ecological validity and applicability of this research to educational practice. Recent advances in mobile neuroimaging technologies have made it possible to conduct neuroscientific studies directly in naturalistic learning environments. We propose that embedding mobile neuroimaging research in a cycle (Matusz, Dikker, Huth, & Perrodin, 2019), involving lab-based, seminaturalistic, and fully naturalistic experiments, is well suited for addressing educational questions. With this review, we take a cautious approach, by discussing the valuable insights that can be gained from mobile neuroimaging technology, including electroencephalography and functional near-infrared spectroscopy, as well as the challenges posed by bringing neuroscientific methods into the classroom. Research paradigms used alongside mobile neuroimaging technology vary considerably. To illustrate this point, studies are discussed with increasingly naturalistic designs. We conclude with several ethical considerations that should be taken into account in this unique area of research.

Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS) is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.

The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.

Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion

In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top-down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain-computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.

Persistence of the "Moving Things Are Alive" Heuristic into Adulthood: Evidence from EEG

Although a growing number of studies indicate that simple strategies, intuitions, or cognitive shortcuts called heuristics can persistently interfere with scientific reasoning in physics and chemistry, the persistence of heuristics related to learning biology is less known. In this study, we investigate the persistence of the "moving things are alive" heuristic into adulthood with 28 undergraduate students who were asked to select between two images, one of which one represented a living thing, while their electroencephalographic signals were recorded. Results show that N2 and LPP event-related potential components, often associated with tasks requiring inhibitory control, are higher in counterintuitive trials (i.e., in trials including moving things not alive or nonmoving things alive) compared with intuitive ones. To our knowledge, these findings represent the first neurocognitive evidence that the "moving things are alive" heuristic persists into adulthood and that overcoming this heuristic might require inhibitory control. Potential implications for life science education are discussed.

EEG in game user analysis: A framework for expertise classification during gameplay

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

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

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

EEG-Based Tool for Prediction of University Students' Cognitive Performance in the Classroom

This study presents a neuroengineering-based machine learning tool developed to predict students' performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students' performance, and to design the machine learning tool. This analysis showed a negative correlation between students' performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4-7 Hz, alpha: 8-13 Hz, low beta: 14-20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

It's all about time: precision and accuracy of Emotiv event-marking for ERP research

Background

The use of consumer-grade electroencephalography (EEG) systems for research purposes has become more prevalent. In event-related potential (ERP) research, it is critical that these systems have precise and accurate timing. The aim of the current study was to investigate the timing reliability of event-marking solutions used with Emotiv commercial EEG systems.

Method

We conducted three experiments. In Experiment 1 we established a jitter threshold (i.e. the point at which jitter made an event-marking method unreliable). To do this, we introduced statistical noise to the temporal position of event-marks of a pre-existing ERP dataset (recorded with a research-grade system, Neuroscan SynAmps² at 1,000 Hz using parallel-port event-marking) and calculated the level at which the waveform peaks differed statistically from the original waveform. In Experiment 2 we established a method to identify ‘true’ events (i.e. when an event should appear in the EEG data). We did this by inserting 1,000 events into Neuroscan data using a custom-built event-marking system, the ‘Airmarker’, which marks events by triggering voltage spikes in two EEG channels. We used the lag between Airmarker events and events generated by Neuroscan as a reference for comparisons in Experiment 3. In Experiment 3 we measured the precision and accuracy of three types of Emotiv event-marking by generating 1,000 events, 1 s apart. We measured precision as the variability (standard deviation in ms) of Emotiv events and accuracy as the mean difference between Emotiv events and true events. The three triggering methods we tested were: (1) Parallel-port-generated TTL triggers; (2) Arduino-generated TTL triggers; and (3) Serial-port triggers. In Methods 1 and 2 we used an auxiliary device, Emotiv Extender, to incorporate triggers into the EEG data. We tested these event-marking methods across three configurations of Emotiv EEG systems: (1) Emotiv EPOC+ sampling at 128 Hz; (2) Emotiv EPOC+ sampling at 256 Hz; and (3) Emotiv EPOC Flex sampling at 128 Hz.

Results

In Experiment 1 we found that the smaller P1 and N1 peaks were attenuated at lower levels of jitter relative to the larger P2 peak (21 ms, 16 ms, and 45 ms for P1, N1, and P2, respectively). In Experiment 2, we found an average lag of 30.96 ms for Airmarker events relative to Neuroscan events. In Experiment 3, we found some lag in all configurations. However, all configurations exhibited precision of less than a single sample, with serial-port-marking the most precise when paired with EPOC+ sampling at 256 Hz.

Conclusion

All Emotiv event-marking methods and configurations that we tested were precise enough for ERP research as the precision of each method would provide ERP waveforms statistically equivalent to a research-standard system. Though all systems exhibited some level of inaccuracy, researchers could easily account for these during data processing.

The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System

Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can open avenues for real-time measurement of cognitive functions in athletes outside the laboratory.

Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects

The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.

Wireless User-Generic Ear EEG

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm² wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.

The EEG-Based Attention Analysis in Multimedia m-Learning

In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become a very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through algorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers analyzed the difference in learners' attention. The study found no significant difference in attention in different media, but learners using text media had the highest attention value. Later, the researchers studied the attention of learners with different learning styles and found that active and reflective learners' attention exhibited significant differences when using video media to learn.