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brain-computer interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection

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ACS SENSORS 2025年第2期10卷 868-880页

作者： Chen, Shiming Jiang, Daolian Li, Mingji Xuan, Xiuwei Li, Hongji Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin Key Lab Film Elect & Commun Devices Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Chem & Chem Engn Tianjin Key Lab Organ Solar Cells & Photochem Conv Tianjin 300384 Peoples R China

The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 mu m for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.

关键词： boron and nitrogen codoped graphene-diamond microelectrode brain-computer interface electroencephalography dopamine

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A Survey on brain-computer interface-Inspired Communications: Opportunities and Challenges

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2025年第1期27卷 108-139页

作者： Hu, Honglin Wang, Zhenyu Zhao, Xi Li, Ruxue Li, Ang Si, Yuan Wang, Jiaheng Zhou, Ting Xu, Tianheng Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China

brain-computer interfaces (BCIs) aim to directly bridge the human brain and the outside world through acquiring and processing the brain signals in real time. In recent two decades, tremendous progress has been made in terms of BCI paradigms and decoding algorithms. The information transfer rate of the BCI systems has been significantly enhanced, and the communication community are considering including the BCI technologies into the 6G networks and beyond. However, some critical problems and challenges remain. To some extent, the BCI systems resemble the wireless communication systems, as brain signals are generated and transmitted by the neurons and received by the sensors, propagating through multiple brain tissues which cause inevitable attenuation and mixing effects. Therefore, by following this idea, a systematic review of the BCI technologies is provided through comparing it with wireless communications. The main challenges of BCI lie in brain channel modelling, modulation, signal processing and detection. The latest and some classic researches are covered. In addition, typical applications of BCIs such as the Quality-of-Experience (QoE) metric and Internet-of-brains (IoB) are predicted. Then, we present a prospect of BCIs being integrated with future wireless communication systems. Also, the technical challenges regarding communication and computation to fulfill the prospect are discussed, as well as privacy, security and ethical concerns. Therefore, this review aims to comprehensively overview BCI-inspired communication on two levels, the BCI itself being a communication system and future integration of BCI and communication as a whole system.

关键词： Wireless communication Communication systems 6G mobile communication Electroencephalography Reviews Sensors Decoding brain-computer interface brain-computer communication biomedical communication

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A Gait Imagery-Based brain-computer interface With Visual Feedback for Spinal Cord Injury Rehabilitation on Lokomat

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第1期72卷 102-111页

作者： Blanco-Diaz, Cristian Felipe Serafini, Ericka Raiane da Silva Bastos-Filho, Teodiano Dantas, Andre Felipe Oliveira de Azevedo Santo, Caroline Cunha do Espirito Delisle-Rodriguez, Denis Fed Univ Espirito Santo UFES Postgrad Program Elect Engn Vitoria Brazil Santos Dumont Inst Edmond & Lily Safra Int Inst Neurosci BR-59288899 Macaiba Brazil

Motor Imagery (MI)-based brain- computer interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery- based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms. Methods: As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to mu (8-12 Hz) and beta (15- 20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms. Results: The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI. Conclusion: The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz. Significance: This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.

关键词： Motors Electroencephalography Vectors Visualization Neurofeedback Legged locomotion Feature extraction brain-computer interface lokomat lower-limb rehabilitation motor imagery neurofeedback spinal cord injury walking

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A spatial-spectral and temporal dual prototype network for motor imagery brain-computer interface

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KNOWLEDGE-BASED SYSTEMS 2025年 315卷

作者： Han, Can Liu, Chen Wang, Jun Wang, Yaqi Cai, Crystal Qian, Dahong Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China Hang Zhou City Univ Sch Comp & Comp Sci Hangzhou 310015 Peoples R China Commun Univ Zhejiang Coll Media Engn Hangzhou 310018 Peoples R China

Motor imagery electroencephalogram (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent complexity of EEG signals relative to the small-sample size. To address this issue, we propose a spatial- spectral and temporal dual prototype network (SST-DPN). First, we design a lightweight attention mechanism to uniformly model the spatial-spectral relationships across multiple EEG electrodes, enabling the extraction of powerful spatial-spectral features. Then, we develop a multi-scale variance pooling module tailored for EEG signals to capture long-term temporal features. This module is parameter-free and computationally efficient, offering clear advantages over the widely used transformer models. Furthermore, we introduce dual prototype learning to optimize the feature space distribution and training process, thereby improving the model's generalization ability on small-sample MI datasets. Our experimental results show that the SSTDPN outperforms state-of-the-art models with superior classification accuracy (84.11% for dataset BCI4-2A, 86.65% for dataset BCI4-2B). Additionally, we use the BCI3-4A dataset with fewer training data to further validate the generalization ability of the proposed SST-DPN, achieving superior performance with 82.03% classification accuracy. Benefiting from the lightweight parameters and superior decoding performance, our SST-DPN shows great potential for practical MI-BCI applications. The code is publicly available at https: //***/hancan16/SST-DPN.

关键词： Spatial-spectral features Long-term temporal features Prototype learning Electroencephalogram brain-computer interface Motor imagery

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Integration of neural networks in brain-computer interface applications: Research frontiers and trend analysis based on Python

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Chen, Junming Yin, Hongyu Zhang, Kai Ren, Yangzhi Zeng, Hui Macau Univ Sci & Technol Fac Humanities & Arts Taipa 999078 Peoples R China Jiangxi Inst Fash Technol Media Art Res Ctr Nanchang 330000 Peoples R China Beijing Univ Technol Coll Architectural Engn Beijing 100124 Peoples R China Jiangnan Univ Sch Design Wuxi 214122 Peoples R China

brain-computer interfaces (BCIs) establish a connection between the human brain and external devices, facilitating novel forms of communication and control. Although BCIs possess significant potential, there is a need for performance improvements. Integrating neural networks into BCIs is crucial for enhancing functionality and promoting broader adoption. This study examines 1,867 articles from the Web of Science core database, covering the period from 1996 to 2024, to identify contemporary hotspots and trends in the application of neural networks within BCIs. This study employs bibliometric methods and Python for analysis, examining collaborative relationships, citation networks, keyword bursts, and clustering with visual representations of the findings. The results indicated that current study hotspots predominantly center on "P300," "Long Short-Term Memory," "Motor Imagery," "Epilepsy," "Emotion Recognition," "Feature Extraction," and "Transfer Learning." Future development directions encompass: (1) the establishment of public BCI datasets;(2) the exploration of diverse feature extraction and fusion methods;(3) the enhancement of machine learning and deep learning integration for improved performance and real-time processing;(4) the expansion of application scenarios and the development of portable devices;(5) the optimization of transfer learning algorithms to mitigate performance challenges arising from individual differences. This study provides an overview of the current research landscape and identifies potential future research directions. Furthermore, it assists practitioners in recognizing additional business opportunities and acts as a resource for the formulation of government policy.

关键词： brain-computer interface Neural networks Bibliometrics Python Application Hotspots

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A Method for Detecting Depression in Adolescence Based on an Affective brain‑computer interface and Resting‑State Electroencephalogram Signals

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Neuroscience Bulletin 2025年第3期41卷 434-448页

作者： Zijing Guan Xiaofei Zhang Weichen Huang Kendi Li Di Chen Weiming Li Jiaqi Sun Lei Chen Yimiao Mao Huijun Sun Xiongzi Tang Liping Cao Yuanqing Li School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China The Affiliated Brain Hospital Guangzhou Medical UniversityGuangzhou 510370China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China

Depression is increasingly prevalent among adolescents and can profoundly impact their ***,the early detection of depression is often hindered by the timeconsuming diagnostic process and the absence of objective *** this study,we propose a novel approach for depression detection based on an affective brain-computer interface(aBCI)and the resting-state electroencephalogram(EEG).By fusing EEG features associated with both emotional and resting states,our method captures comprehensive depression-related *** final depression detection model,derived through decision fusion with multiple independent models,further enhances detection *** experiments involved 40 adolescents with depression and 40 matched *** proposed model achieved an accuracy of 86.54%on cross-validation and 88.20%on the independent test set,demonstrating the efficiency of multi-modal *** addition,further analysis revealed distinct brain activity patterns between the two groups across different *** findings hold promise for new directions in depression detection and intervention.

关键词： Depression detection brain-computer interface EEG Multimodal

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A Safe and Efficient brain-computer interface Using Moving Object Trajectories and LED-Controlled Activation

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MICROMACHINES 2025年第3期16卷 340-340页

作者： Aydin, Sefa Melek, Mesut Gokrem, Levent Gumushane Univ Dept Elect & Automat TR-29100 Gumushane Turkiye Tokat Gaziosmanpasa Univ Dept Elect & Elect Engn TR-60600 Tokat Turkiye

Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.

关键词： electroencephalography electrooculography brain-computer interface machine learning classification Emotiv Epoc

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A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer interface

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computer METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年第1期28卷 90-106页

作者： Deepika, D. Rekha, G. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad 500075 Telangana India Mahatma Gandhi Inst Technol Dept Comp Sci & Engn Hyderabad 500075 Telangana India

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.

关键词： Electroencephalography brain-computer interface capsule network attention mechanism convolutional neural network Bi-GRU Discrete wavelet transform

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An auditory selective attention brain-computer interface system based on auditory steady-state response

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APPLIED ACOUSTICS 2025年 228卷

作者： Wang, Yao Liu, Xin Cui, Hongyan Li, Zhaohui Chen, Xiaogang Tiangong Univ Sch Life Sci Dept Biomed Engn Tianjin 300387 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China

Auditory steady-state response (ASSR) is a brain steady-state response induced by periodic sound signals, which can be used to build an auditory brain-computer interface (BCI), thereby providing a pathway for visually impaired patients to communicate with the outside world. Most of existing ASSR-based BCI studies use linear discriminant classifier (LDA) and spatial coherence to detect ASSRs. Therefore, there is an urgent need for efficient electroencephalogram (EEG) decoding methods to improve the performance of ASSR-based BCI systems. In this study, we elicited ASSRs using sinusoidal amplitude modulated (SAM) tones that simultaneously delivered different modulation frequencies (i.e., 37 Hz for the left channel and 43 Hz for the right channel). Subjects were asked to focus their attention on the auditory stimulation on one side according to the auditory cue. Filter bank common spatial pattern (FBCSP) algorithm was innovatively introduced to detect the ASSRs. Offline results showed that the brain region with strong ASSRs was the central forehead area, and when subjects paid attention to the auditory stimulation at 37 Hz or 43 Hz, the ASSR response of 37 Hz or 43 Hz on the corresponding side would be enhanced compared to the no attention condition. Online results obtained from twelve healthy subjects showed that the mean recognition accuracy of the proposed ASSR-based BCI system achieved a mean accuracy of 82.22 +/- 3.11 %. Moreover, the present study further verified that weak auditory stimuli with low stimulus intensity (i.e., 40 dB SPL) could also be used to build ASSR-based BCIs, and achieved an online mean accuracy of 78.89 +/- 2.54 %. These results verified that the FBCSP algorithm could be used for detecting ASSRs and the feasibility of the proposed ASSR-based BCI system, providing a great idea for building a high-speed ASSR-based BCI system.

关键词： Auditory steady-state response brain-computer interface Electroencephalogram Sinusoidal amplitude modulation

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A Systematic Review of Bimanual Motor Coordination in brain-computer interface

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 266-285页

作者： Tantawanich, Poraneepan Phunruangsakao, Chatrin Izumi, Shin-Ichi Hayashibe, Mitsuhiro Tohoku Univ Grad Sch Engn Dept Robot Sendai 9808579 Japan Tohoku Univ Grad Sch Biomed Engn Neurorobot Lab Sendai 9808579 Japan Tsurumaki Onsen Hosp Hadano 2570001 Japan Tohoku Univ Grad Sch Engn Dept Robot Neurorobot Lab Sendai 9808579 Japan Tohoku Univ Grad Sch Biomed Engn Sendai 9808579 Japan

Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. Growing interest exists in decoding bimanual motor tasks, crucial for activities of daily living. This stems from the need to restore motor function, especially in individuals with deficits. This review aims to summarize neurological advancements in bimanual BCIs, encompassing neuroimaging techniques, experimental paradigms, and analysis algorithms. Thirty-six articles were reviewed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search result revealed diverse experimental paradigms, protocols, and research directions, including enhancing the decoding accuracy, advancing versatile prosthesis robots, and enabling real-time applications. Notably, within BCI studies on bimanual movement coordination, a shared objective is to achieve naturalistic movement and practical applications with neurorehabilitation potential.

关键词： Motors Decoding Training Neuroimaging Feature extraction Systematic literature review Hands Databases Complexity theory Visualization brain-computer interface bimanual coordination neuroscience machine learning

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