The Development of a Multicommand Tactile Event-Related Potential-Based Brain-Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator.

A tactile event-related potential (ERP)-based brain-computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain-computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.

Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.

Convolutional Neural Network for Drowsiness Detection Using EEG Signals.

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

Emotiv EPOC+ fed electrical muscle stimulation system; an inexpensive brain-computer interface for rehabilitation of neuro muscular disorders.

Advancements in the Neuro-rehabilitation across Pakistan is warranted to effectively and efficiently deal with the disease burden of neurological conditions. Being a developing country, an in-expensive treatment approach is required to culminate the rise in the disease occurrence in Pakistan. Brain-Computer Interfaces (BCIs) have come up as a new channel for communication and control, eliminating the need of physical input, opening doors to a wide array of applications in terms of assistive and rehabilitative devices for paralyzed patients and those with neuromuscular disorders. Even with a promising prospect, BCIs and electroencephalograms (EEG) can be very expensive and therefore, they are not practically applicable. For this reason, the purpose of the current study was to come up with a possibility of an inexpensive BCI for rehabilitation of patients with neuro-muscular disorders in Pakistan by using a low-cost and readily available equipment like Emotiv EPOC+ EEG headset and electrical muscle stimulator.

An empirical evaluation of a hands-free computer interaction for users with motor disabilities.

Standard computer input devices such as a mouse or a keyboard are not well suited to the needs of users with severe motor disabilities in their interaction with standard computer interfaces. The emergence of contemporary human computer interfaces has allowed for the development of innovative solutions for hands-free Human-Computer Interaction (HCI), which can improve the quality and accessibility of Information and Communication Technology (ICT) for motor-impaired users. The objectives of this study were to design, develop and evaluate a solution for a hands-free HCI, based on the Emotiv EPOC+ device, which, among other capabilities, also enables controlling the computer with facial expressions and motion sensors. Ten non-disabled adults and eight adults with a severe motor disability participated in an experiment to evaluate the proposed HCI solution. Eighteen users completed six experimental tasks successfully using both their existing computer use approach as well as the proposed hands-free computer use approach. The times necessary to complete the tasks were measured and analyzed, along with users' subjective observations about the difficulty level of both computer use approaches. Users' perceptions about the new hands-free computer use approach were assessed as well. Although there were no significant differences in both user types regarding the difficulty level in completing the tasks, disabled users solved the tasks with less effort. Positive perceptions about perceived usefulness, ease of use, appropriateness of the technology, and satisfaction with the proposed solution for touchless interaction were assessed for both user types. Scores were significantly higher for disabled users in the case of measuring the perceived usefulness, perceived ease of use and satisfaction with the solution. This study showed that users with severe motor difficulties find new HCI less challenging compared to their existing computer use approach than the non-disabled who use standard HCI. When compared with non-disabled users, the disabled ones can be equally effective when confronted with a new HCI technology. Future work is needed to improve the proposed solution and to analyze the impact of different factors on users with motor disabilities and their adoption of an innovative technology for touchless interaction with a computer.

Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.

The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.

Enhancing Working Memory and Reducing Anxiety in University Students: A Neurofeedback Approach.

(1) Background: Neurofeedback training (NFT) has emerged as a promising approach for enhancing cognitive functions and reducing anxiety, yet its specific impact on university student populations requires further investigation. This study aims to examine the effects of NFT on working memory improvement and anxiety reduction within this demographic. (2) Methods: A total of forty healthy university student volunteers were randomized into two groups: an experimental group that received NFT and a control group. The NFT protocol was administered using a 14-channel Emotiv Epoc X headset (EMOTIV, Inc., San Francisco, CA 94102, USA) and BrainViz software version Brain Visualizer 1.1 (EMOTIV, Inc., San Francisco, CA 94102, USA), focusing on the alpha frequency band to target improvements in working memory and reductions in anxiety. Assessment tools, including the Corsi Block and Memory Span tests for working memory and the State-Trait Anxiety Inventory-2 (STAI-2) for anxiety, were applied pre- and post-intervention. (3) Results: The findings indicated an increase in alpha wave amplitude in the experimental group from the second day of NFT, with statistically significant differences observed on days 2 (p < 0.05) and 8 (p < 0.01). Contrary to expectations based on the previous literature, the study did not observe a concurrent positive impact on working memory. Nonetheless, a significant reduction in state anxiety levels was recorded in the experimental group (p < 0.001), corroborating NFT's potential for anxiety management. (4) Conclusions: While these results suggest some potential of the technique in enhancing neural efficiency, the variability across different days highlights the need for further investigation to fully ascertain its effectiveness. The study confirms the beneficial impact of NFT on reducing state anxiety among university students, underscoring its value in psychological and cognitive performance enhancement. Despite the lack of observed improvements in working memory, these results highlight the need for continued exploration of NFT applications across different populations and settings, emphasizing its potential utility in educational and therapeutic contexts.

A resting state EEG study on depressed persons with suicidal ideation.

Background: Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life-threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain region. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD. Methods: Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with and MDD persons with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between-group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) between PSD data and the scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS). Results: When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered. Conclusions: The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self-referencing. Future studies should confirm these findings in a larger population sample.

SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task.

This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.

A robust and reliable online P300-based BCI system using Emotiv EPOC + headset.

Brain-computer interface (BCI) system aims to enable interaction with people and therefore the environment without muscular activation, using changes in brain signals due to the execution of cognitive tasks. The target of the presented work is to investigate the power of Emotiv EPOC + headset to detect and record the P300 wave. Moreover, the effect of preprocessing the acquired signal was studied. Five participants were asked to attend different sessions to an equivalent 6x6 matrix while the rows and columns were randomly flashed at a rate of 200 ms. The acquired EEG data were sent wirelessly to OpenViBE software, which is employed to run the P300 speller. Two classification methods were tried: Linear discriminate analysis (LDA) and support vector machine (SVM). The capability of the headset to detect the P300 signals is proven by the results. Additionally, results show that participants reached accuracy up to 90 and 70% after only two training sessions for Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers, respectively. The significance of this work is to demonstrate that such a portable and affordable headset might be useful to design and implement a robust and reliable online P300-based BCI system.

The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study.

Everyone experiences stress at certain times in their lives. This feeling can motivate, however, if it persists for a prolonged period, it leads to negative changes in the human body. Stress is characterized, among other things, by increased blood pressure, increased pulse and decreased alpha-frequency brainwave activity. An overview of the literature indicates that music therapy can be an effective and inexpensive method of improving these factors. The objective of this study was to analyze the impact of various types of music on stress level in subjects. The conducted experiment involved nine females, aged 22. All participants were healthy and did not have any neurological or psychiatric disorders. The test included four types of audio stimuli: silence (control sample), rap, relaxing music and music triggering an autonomous sensory meridian response (ASMR) phenomenon. The impact of individual sound types was assessed using data obtained from four sources: a fourteen-channel electroencephalograph, a blood pressure monitor, a pulsometer and participant's subjective stress perception. The conclusions from the conducted study indicate that rap music negatively affects the reduction of stress level compared to the control group (p < 0.05), whereas relaxing music and ASMR calms subjects much faster than silence (p < 0.05).

A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies.

The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson's correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.

Evaluating If Children Can Use Simple Brain Computer Interfaces.

Background: The options for severely disabled children with intact cognition to interact with their environment are extremely limited. A brain computer interface (BCI) has the potential to allow such persons to gain meaningful function, communication, and independence. While the pediatric population might benefit most from BCI technology, research to date has been predominantly in adults. Methods: In this prospective, cross-over study, we quantified the ability of healthy school-aged children to perform simple tasks using a basic, commercially available, EEG-based BCI. Typically developing children aged 6-18 years were recruited from the community. BCI training consisted of a brief set-up and EEG recording while performing specific tasks using an inexpensive, commercially available BCI system (EMOTIV EPOC). Two tasks were trained (driving a remote-control car and moving a computer cursor) each using two strategies (sensorimotor and visual imagery). Primary outcome was the kappa coefficient between requested and achieved performance. Effects of task, strategy, age, and learning were also explored. Results: Twenty-six of thirty children completed the study (mean age 13.2 ± 3.6 years, 27% female). Tolerability was excellent with >90% reporting the experience as neutral or pleasant. Older children achieved performance comparable to adult studies, but younger age was associated with lesser though still good performance. The car task demonstrated higher performance compared to the cursor task (p = 0.027). Thought strategy was also associated with performance with visual imagery strategies outperforming sensorimotor approaches (p = 0.031). Conclusion: Children can quickly achieve control and execute multiple tasks using simple EEG-based BCI systems. Performance depends on strategy, task and age. Such success in the developing brain mandates exploration of such practical systems in severely disabled children.

Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface.

INTRODUCTION: Brain computer interface is an emerging technology to treat the sequelae of stroke. The purpose of this study was to explore the motor imagery related desynchronization of sensorimotor rhythms of stroke patients and to assess the efficacy of an upper limb neurorehabilitation therapy based on functional electrical stimulation controlled by a brain computer interface. METHODS: Eight severe chronic stroke patients were recruited. The study consisted of two stages: screening and therapy. During screening, the ability of patients to desynchronize the contralateral oscillatory sensorimotor rhythms by motor imagery of the most affected hand was assessed. In the second stage, a therapeutic intervention was performed. It involved 20 sessions where an electrical stimulator was activated when the patient's cerebral activity related to motor imagery was detected. The upper limb was assessed, before and after the intervention, by the Fugl-Meyer score (primary outcome). Spasticity, motor activity, range of movement and quality of life were also evaluated (secondary outcomes). RESULTS: Desynchronization was identified in all screened patients. Significant post-treatment improvement (p < 0.05) was detected in the primary outcome measure and in the majority of secondary outcome scores. CONCLUSIONS: The results suggest that the proposed therapy could be beneficial in the neurorehabilitation of stroke individuals.

A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases.

In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI's data analytics of a subject's MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain⁻Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject's response and users' intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user's brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

Acquiring research-grade ERPs on a shoestring budget: A comparison of a modified Emotiv and commercial SynAmps EEG system.

This study compared the performance of a low-cost wireless EEG system to a research-grade EEG system on an auditory oddball task designed to elicit N200 and P300 ERP components. Participants were 15 healthy adults (6 female) aged between 19 and 40 (M = 28.56; SD = 6.38). An auditory oddball task was presented comprising 1,200 presentations of a standard tone interspersed by 300 trials comprising a deviant tone. EEG was simultaneously recorded from a modified Emotiv EPOC and a NeuroScan SynAmps RT EEG system. The modifications made to the Emotiv system included attaching research grade electrodes to the Bluetooth transmitter. Additional modifications enabled the Emotiv system to connect to a portable impedance meter. The cost of these modifications and portable impedance meter approached the purchase value of the Emotiv system. Preliminary analyses revealed significantly more trials were rejected from data acquired by the modified Emotiv compared to the SynAmps system. However, the ERP waveforms captured by the Emotiv system were found to be highly similar to the corresponding waveform from the SynAmps system. The latency and peak amplitude of N200 and P300 components were also found to be similar between systems. Overall, the results indicate that, in the context of an oddball task, the ERP acquired by a low-cost wireless EEG system can be of comparable quality to research-grade EEG acquisition equipment.

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.

Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children.

Background. Previous work has demonstrated that a commercial gaming electroencephalography (EEG) system, Emotiv EPOC, can be adjusted to provide valid auditory event-related potentials (ERPs) in adults that are comparable to ERPs recorded by a research-grade EEG system, Neuroscan. The aim of the current study was to determine if the same was true for children. Method. An adapted Emotiv EPOC system and Neuroscan system were used to make simultaneous EEG recordings in nineteen 6- to 12-year-old children under "passive" and "active" listening conditions. In the passive condition, children were instructed to watch a silent DVD and ignore 566 standard (1,000 Hz) and 100 deviant (1,200 Hz) tones. In the active condition, they listened to the same stimuli, and were asked to count the number of 'high' (i.e., deviant) tones. Results. Intraclass correlations (ICCs) indicated that the ERP morphology recorded with the two systems was very similar for the P1, N1, P2, N2, and P3 ERP peaks (r = .82 to .95) in both passive and active conditions, and less so, though still strong, for mismatch negativity ERP component (MMN; r = .67 to .74). There were few differences between peak amplitude and latency estimates for the two systems. Conclusions. An adapted EPOC EEG system can be used to index children's late auditory ERP peaks (i.e., P1, N1, P2, N2, P3) and their MMN ERP component.

Actividad eléctrica cerebral de la atención en adolescentes policonsumidores por medio de un equipo de BCI (Brain Control Interface) / Atividade elétrica cerebral da atenção em adolescentes policonsumidores por meio de um equipamento de Brain Control Interface (BCI) / Brain electrical activity of attention in polydrug adolescents using an equipment BCI (Brain Control Interface)

Resumen El consumo de sustancias psicoactivas es un problema de salud pública que afecta cada vez más a la población adolescente. La presente investigación tuvo como objetivo registrar la actividad eléctrica cerebral (EEG) en tareas de atención (sostenida y selectiva) en un grupo de adolescentes policonsumidores. Se empleó un diseño ex post-facto retrospectivo con grupo cuasi control, en 46 adolescentes con edades entre los 12 los 17 años: 23 policonsumidores y 23 cuasi-controles. Para el registro de la actividad eléctrica cerebral se utilizó un equipo de BCI (Brain Control Interface) Emotiv EPOC research grade 14 Channel Mobile EEG y se aplicó el Programa virtual de entrenamiento cerebral Brain HQ con el módulo "enfoco mi atención" para la evaluación de la atención. Los resultados mostraron un incremento de ondas cerebrales beta-β (13-30 Hz), theta-θ (4-7 Hz) y delta-δ (3-4 Hz) en áreas frontales y prefrontales en los adolescentes policonsumidores en tareas de atención en comparación con el grupo cuasi-control. Se identificó una diferencia significativa con respecto al tiempo de respuesta entre los adolescentes consumidores de sustancias psicoactivas frente al grupo cuasi-control en ambos tipos de tareas atencionales.

Resumo O consumo de substâncias psicoativas é um problema de saúde pública que afeta cada vez mais a população adolescente. Esta pesquisa teve como objetivo registrar a atividade elétrica cerebral (EEG) em tarefas de atenção (sustentada e alternada) num grupo de adolescentes policonsumidores. Foi empregado um desenho ex post-facto retrospectivo com grupo quasecontrole, em en 46 adolescentes entre 12 e 17 anos de idade: 23 policonsumidores e 23 quase-controles. Para o registro da atividade elétrica cerebral, foi utilizado um equipamento de Brain Control Interface (BCI) Emotiv EPOC research grade 14 Channel Mobile EEG e foi aplicado o Programa Virtual de Treinamento Cerebral Brain HQ, com o módulo "foco minha atenção" para a avaliar a atenção. Os resultados mostraram um aumento de ondas cerebrais beta-β (13-30 Hz), theta-θ (4-7 Hz) e delta-δ (3-4 Hz) em áreas frontais e pré-frontais nos adolescentes policonsumidores em tarefas de atenção em comparação com o grupo quase-controle. Foi identificada uma diferença significativa a respeito do tempo de resposta entre os adolescentes consumidores de substâncias psicoativas ante o grupo quase-controle em ambos os tipos de tarefas de atenção.

Abstract The consumption of psychoactive substances is a public health problem that increasingly affects the adolescent population. This investigation had the objective of record the brain electrical activity (EEG) in attention tasks (sustained and selective) in a group of polyconsumers. Employment a retrospective ex post-facto design with a quasi-control group with 46 adolescents between 12-17 years old: 23 polyconsumers and 23 quasi-controls. For the recording of brain electrical activity, it was used a equipment BCI (Brain Control Interface) research grade 14 Channel Mobile EEG and applied the Brain Training Virtual Program "Brain HQ" module "focus my attention" to evaluate the attention. The results showed an increase in beta-β (1330 Hz), theta-θ (4-7 Hz) and delta-δ (3-4 Hz) brain waves in frontal and prefrontal areas in adolescent polyonsumers versus the quasi-control group in attention tasks. Likewise, identified a significant difference with respect to the response time between adolescents consuming psychoactive substances in relation to the quasi-control group in both types of attentional tasks.

Validation of the Emotiv EPOC(®) EEG gaming system for measuring research quality auditory ERPs.

Background. Auditory event-related potentials (ERPs) have proved useful in investigating the role of auditory processing in cognitive disorders such as developmental dyslexia, specific language impairment (SLI), attention deficit hyperactivity disorder (ADHD), schizophrenia, and autism. However, laboratory recordings of auditory ERPs can be lengthy, uncomfortable, or threatening for some participants - particularly children. Recently, a commercial gaming electroencephalography (EEG) system has been developed that is portable, inexpensive, and easy to set up. In this study we tested if auditory ERPs measured using a gaming EEG system (Emotiv EPOC(®), www.emotiv.com) were equivalent to those measured by a widely-used, laboratory-based, research EEG system (Neuroscan). Methods. We simultaneously recorded EEGs with the research and gaming EEG systems, whilst presenting 21 adults with 566 standard (1000 Hz) and 100 deviant (1200 Hz) tones under passive (non-attended) and active (attended) conditions. The onset of each tone was marked in the EEGs using a parallel port pulse (Neuroscan) or a stimulus-generated electrical pulse injected into the O1 and O2 channels (Emotiv EPOC(®)). These markers were used to calculate research and gaming EEG system late auditory ERPs (P1, N1, P2, N2, and P3 peaks) and the mismatch negativity (MMN) in active and passive listening conditions for each participant. Results. Analyses were restricted to frontal sites as these are most commonly reported in auditory ERP research. Intra-class correlations (ICCs) indicated that the morphology of the research and gaming EEG system late auditory ERP waveforms were similar across all participants, but that the research and gaming EEG system MMN waveforms were only similar for participants with non-noisy MMN waveforms (N = 11 out of 21). Peak amplitude and latency measures revealed no significant differences between the size or the timing of the auditory P1, N1, P2, N2, P3, and MMN peaks. Conclusions. Our findings suggest that the gaming EEG system may prove a valid alternative to laboratory ERP systems for recording reliable late auditory ERPs (P1, N1, P2, N2, and the P3) over the frontal cortices. In the future, the gaming EEG system may also prove useful for measuring less reliable ERPs, such as the MMN, if the reliability of such ERPs can be boosted to the same level as late auditory ERPs.