Development Of Brain-computer Interface Systems Research Articles

CTNet: a convolutional transformer network for EEG-based motor imagery classification

Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.

Silent EEG classification using cross-fusion adaptive graph convolution network for multilingual neurolinguistic signal decoding

Brain–computer interface (BCI) based multilingual silent reading electroencephalography (EEG) decoding technology provides a convenient and fast communication method for multilingual patients with language disorders. Developing a silent reading EEG signals decoding method that can be applied to multiple languages, it is necessary not only to select the appropriate feature channels for each language but also to distinguish the spatial and temporal relationships between each channel. The existing graph convolution network that learns the characteristics and connections of each node on a fixed graph is inadequate to capture both the temporal and spatial relationships between each channel. Therefore, we use a feature matrix and its adaptive graph to represent each EEG signal and propose a Cross-fusion Adaptive Graph Convolution Network (CFA-GCN) for decoding. We collect novel EEG signal datasets of a subject who silently read 7 Chinese and 9 English words over 26 days, which can be used for further research and development of BCI systems. The proposed method achieves state-of-the-art performance with 45.30% and 47.62% decoding accuracy for silent reading EEG signals of English and Chinese words, respectively. This work demonstrates the feasibility of decoding silent reading EEG signals from bilingual words. The CFA-GCN demonstrates the potential of utilizing Brain–computer Interface (BCI) systems to facilitate effective communication for patients with multilingual impairments.

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.

Dynamic selective auditory attention detection using RNN and reinforcement learning

The cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. The proposed dynamic SAAD is modeled as a sequential decision-making problem, which is solved by recurrent neural network (RNN) and reinforcement learning methods of Q-learning and deep Q-learning. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach with RNN as agent provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness.

This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes). The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

Efficient MAC Protocols for Brain Computer Interface Applications

Brain computer interface (BCI) systems permit individuals with motor disorders to utilize their thoughts as a mean to control external devices. BCI is a promising interdisciplinary field that gained the attention of many researchers. Yet, the development of BCI systems is facing several challenges, such as network lifetime. The Medium Access Control (MAC) Protocol is the bottle- neck of network reliability. There are many MAC protocols that can be utilized for dependable transmission in BCI applications by altering their control parameters. However, modifying these parameters is another source of concern due to the scarcity in knowledge about the effect of modification. Also, there is still no instrument that can receive and actualize these parameters on transmitters embedded inside the cerebrum. In this paper, we propose two novel MAC protocols using passive UHF-RFID, the proposed protocols provide efficient and reliable communication between the transmitters and the receiver. The UHF-RFID transmitters were used because they are energy efficient which makes them compatible with BCI application. The first protocol is designed for the EEG signals. While the second protocol was designed for the ECoG signals. The evaluation results showed the validity of the proposed protocols in terms of network performance. The results also proved that the protocols are suitable and reliable for designing efficient BCI applications.

Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain-computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable Q wavelet transform is an arduous task. In this article, an adaptive tunable Q wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.

Mind Your Mind

A brain-computer interface (BCI) system is a system that leverages brainwave information acquired by a designated brain monitoring device to interact with a computerized system. Over the past 40 years, many BCI applications have been developed in a variety of domains, from entertainment to medical field and even to computer security mechanisms. Until now, the development of BCI systems has focused on improving their accuracy, functionality, and ease of use, and not enough effort and attention has been invested in securing these systems and the sensitive data they acquire. In this article, we present the principles of brain activity data acquisition, with a special focus on EEG, and we present a taxonomy of BCI applications and domains. We also provide a comprehensive survey that covers eight possible attacks aimed at BCI systems. For each BCI application, we created an ecosystem and data and attack flow-diagram, which comprehensively describes the roles and interactions of the players associated with the BCI application and presents the most vulnerable vectors and components within its ecosystem; we identified gaps between existing security solutions and the presented attacks and vulnerabilities. Finally, we provide several concrete suggestions for improving the security of BCI systems in cyber-space.

EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses

In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays.

A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces

Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.

Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach

The translation of brain activities into signals in brain---computer interface (BCI) systems requires a robust and accurate classification to develop a communication system for motor disabled people. In BCIs, motor imagery (MI) tasks generate brain activities, which are generally measured by electroencephalogram (EEG) signals. The aim of this research was to introduce a method for the extraction of discriminatory information from the MI-based EEG signals for BCI applications. The proposed scheme develops an optimal allocation (OA)-based approach to discover the most effective representatives with minimal variability from a large number of MI-based EEG data. To investigate a suitable classifier for the OA-based features, the least square support vector machine (LS-SVM) and Naive Bayes (NB) methods are applied separately on the extracted features for discriminating the MI activities. Experimental results on datasets, IVa and IVb of BCI Competition III, show that the OA-based features with the LS-SVM classifier yields better performances compared to the NB classifiers. The results also demonstrate that the classification performance is improved up to 21.16 %, through the use of the OA algorithm with the LS-SVM, compared to the reported four prominent methods. This implies that the OA technique is promising for extracting representative characteristics from MI-based EEG data, which can be reliably used with the LS-SVM to identify different signals of brain activities in the development of BCI systems.