Brain-computer Interface Technology Research Articles

Enhancing Emotion Recognition through Deep Learning and Brain-Computer Interface Technology

Deep learning, a specialized branch of machine learning, uses complex neural networks for advanced data processing tasks in which feature extraction and progression require more than one layer of processing using a Neural Network. The Brain Computer Interface is a direct communication link between the brain's electrical activity and an external equipment. The actual brain data is gathered using the BCI technology with electrodes fitted in Brain cells and the research focuses on the recognition of multimodal emotions after feature extraction and multilayer processing with an Artificial Neural Network (ANN). Several organizations have lately released enormous datasets containing experimental data on people's physiological signals (EEGs, eye movements) while they experience different emotions. These datasets collections are being exclusively designed to encourage the development of successful deep-learning emotion identification algorithms where the volunteers had to do some set of tasks regarding the research purpose. In this study, we evaluate deep learning approaches more specifically by the Artificial Neural Network (ANN) using the SEED-IV Electroencephalogram (EEG) dataset. In this paper, we briefly represented the implementation of Emotion Recognition using Artificial Neural Network (ANN), which is one of the topics of our research. Furthermore, we explore the operating principles of artificial neural networks, which are our primary model for identifying emotions. Finally, we offer a few proposals for implementing neural networks on EEG inputs. With the ANN and 3D convolutional layers, we finally achieved a 63.425% accuracy rate.

Classification of Motor-Imagery Tasks using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.

Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.

Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification.

Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.

Brain-Controlled, AR-based Home Automation System using SSVEP-based Brain-Computer Interface and EOG-based Eye Tracker: A Feasibility Study for the Elderly End User.

Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user's preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.

Human 2.0 – Legal Implications of Brain-Computer Interface Technology

Stellen Sie sich vor, Sie könnten durch reine Gedankenkraft Geräte steuern – das ist mit Blick auf die aktuellen Entwicklungen von Brain-Computer Interfaces (BCI) keine Zukunftsmusik mehr. Je weiter die Entwicklung von BCI-Technologie voranschreitet und zu marktfähigen Produkten führt, desto mehr rücken auch potenzielle Sicherheitsrisiken in den Fokus. Angriffe auf BCI könnten theoretisch neurologische Daten ausspionieren oder Gehirnaktivitäten manipulieren und so ernsthafte Schäden verursachen. Die vorliegende Publikation führt in die Thematik BCI ein und untersucht sie aus der Datenschutz-, Medizinprodukte- und Menschenrechtsperspektive.

Improving Performance of Motor Imagery-based Brain-computer Interface in Poorly Performing Subjects Using a Hybrid-imagery Method utilizing Combined Motor and Somatosensory Activity.

The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60±21.62%, 71.25±19.53%, and 84.09±12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybrid-condition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three paradigms. The Hybrid-condition II paradigm provided high concentration and discrimination to poor performers in the motor imagery-based brain-computer interface and generated the enhanced event-related desynchronization pattern in three modalities corresponding to different types of somatosensory stimuli in motor and somatosensory regions compared to the Control-condition and Hybrid-condition I. The hybrid-imagery approach can help improve motor imagery-based brain-computer interface performance, especially for poorly performing users, thus contributing to the practical use and uptake of brain-computer interface.

Human 2.0 – Legal Implications of Brain-Computer Interface Technology

Human 2.0 – Legal Implications of Brain-Computer Interface Technology

The Effectiveness of Brain-computer Interface Technology in the Metaverse

The Effectiveness of Brain-computer Interface Technology in the Metaverse

Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation

Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: “quadriceps muscle,” “neurofeedback,” “biofeedback,” “muscle activation,” “motor learning,” “anterior cruciate ligament,” and “cortical plasticity.” We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps “exercise” the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care. Level of EvidenceV, expert opinion.

Evaluation of the User Adaptation in a BCI Game Environment

Brain-computer interface (BCI) technology is a developing field of study with numerous applications. The purpose of this paper is to discuss the use of brain signals as a direct communication pathway to an external device. In this work, Zombie Jumper is developed, which consists of 2 brain commands, imagining moving forward and blinking. The goal of the game is to jump over static or moving “zombie” characters in order to complete the level. To record the raw EEG data, a Muse 2 headband is used, and the OpenViBE platform is employed to process and classify the brain signals. The Unity engine is used to build the game, and the lab streaming layer (LSL) protocol is the connective link between Muse 2, OpenViBE and the Unity engine for this BCI-controlled game. A total of 37 subjects tested the game and played it at least 20 times. The average classification accuracy was 98.74%, ranging from 97.06% to 99.72%. Finally, playing the game for longer periods of time resulted in greater control.

Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data is a significant part of the Brain-Computer Interface (BCI) technology that allows motor-disabled persons to connect with external devices. Channel selection, feature extraction, and classification are essential requirements for an effective BCI system. Non-stationary EEG data confuses designing EEG-based BCIs. In this study, the Pearson correlation coefficient (PCC) technique is employed for channel selection for EEG signals in the BCI system. It selects the most associated fourteen channels for the sensorimotor area of subject's brain. The popular signal processing technique wavelet packet decomposition (WPD) is employed for feature extraction. After that approximate entropy (ApEn) feature is calculated for selected channels. The proposed study is a novel scheme combining Pearson correlation coefficient-based channel selection technique and wavelet packet decomposition for classifying MI signals. Finally, extracted features are classified with the help of two benchmark techniques, Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) and achieve maximum accuracy of 91.66% and 90.33%, respectively. The proposed technique is examined on freely available EEG datasets BCI competition-IV-Dataset I to prove its superiority over previously reported approaches. Obtained experimental findings demonstrated advantages over previous methods in terms of classification accuracy.

Comparing users' performance and game experience between a competitive and collaborative brain-computer interface

ABSTRACT The recent advancements in Brain-Computer Interfaces (BCIs) technology made possible for its employment in entertainment applications, such as brain-controlled games. However, there are still many challenges to overcome in the design and development of such applications, especially when considering the interaction between multiple users. This paper presents the results of a controlled experiment aimed at comparing the performance and game experience of both competitive and collaborative matches in a multiplayer BCI game. In the experiment, sixteen subjects were divided in pairs into two groups, and each pair played a total of five competitive or collaborative matches of the attention-based game. Quantitative and qualitative data were collected to investigate key features of the subjects' interaction with both the BCI platform and the other players. It was found that there is no statistically significant difference in performance between the subjects of both groups, and between multiplayer and single player modes. Although the group playing competitively reported overall better scores for game experience, there was no statistically significant difference between groups. There were significant correlations between performance and positive aspects of game experience for both modes, and multiplayer matches were significantly shorter than single player ones, although this effect could not be attributed to the mode of interaction alone.

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System.

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.

Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices

Virtual reality has been widely used in various industries, such as entertainment, communication, and healthcare, to mention a few. In the health industry, in combination with the brain–computer interfaces (BCIs), virtual reality could produce rehabilitation measures that may contribute novel strategies such as remote rehabilitation or telerehabilitation. The design and development of BCIs integrate different processes, including biosignals acquisition and processing, feature extraction and selection, classification of signals, and application of the technology to patients under rehabilitation treatments. This manuscript presents a literature review of the papers focused on the implementation of BCI and assistive technologies for remote rehabilitation based on virtual reality implementation. The purpose of this comprehensive review is to identify those studies that take advantage of virtual reality in combination with a biomedical technology to improve the performances of diverse rehabilitation processes. Various revisited studies provided a complete system for remote rehabilitation. These findings could lead to applying these models in diverse rehabilitation tasks.

Study of event-related potentials by withdrawal friction on the fingertip.

The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain-computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface. When friction is suddenly removed from the surface of an item, a technique called event-related potential, (ERP) is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A 64-channels EEG collecting system was utilized to acquire EEG data from the individuals. [Corrections added on 5 December 2022, after first online publication: The preceding sentence has been updated.] The event-related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years. It was discovered that the P3 component of event-related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed. It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain-computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation.

An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology

An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology

Technologie informatyczne neuronauk w przekazie medialnym

Technological changes in the modern media coverage require the use of advanced computer technologies. The usage of interaction and the dominance of visual components caused necessary using artificial intelligence (AI) algorithms. The AI algorithms are used at all stages of the preparation of media coverage and in studies of its impact on recipients. Appropriate adaptation of AI methods to dynamic media transmission uses Brain Computer Interfaces (BCI) technologies. Experimental research with the BCI technologies may indicate a new direction of development and improvement of contemporary forms of media coverage.

Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease

Purpose To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies. Methods Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory. Results Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual’s identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies. Conclusions With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation. IMPLICATIONS FOR REHABILITATION Individuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies. The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy. People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities. Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.

Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.

Research advances in non-invasive brain-computer interface control strategies

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.