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

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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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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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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Learning-based EEG rhythm analysis for the enhancement of motor imagery-based brain-computer interface performance

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Wu, Xiaopei Ouyang, Rui Zhang, Chao Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

One of the major challenges in achieving stability and adaptability in motor imagery brain-computer interfaces (MI-BCI) is the individual variability in motion-related neural activity. This paper presents a novel approach to motor imagery electroencephalogram (MI-EEG) data analysis and MI-BCI optimization, making the following contributions: (1) We introduce EhythmNet, a compact one-dimensional convolutional neural network (CNN) designed to capture the inherent frequency features in MI-EEG data. (2) We propose using kernel coefficients derived from EhythmNet to construct a temporal bandpass filter within the conventional MI-BCI framework, where independent component analysis (ICA) is employed as the spatial filter. Experimental results consistently show that replacing the conventional bandpass filter (CBF) with a kernel-based bandpass filter (KBF) significantly improves stability and MI classification performance (KBF-ICA: 82.3 +/- 2.7 %, CBF-ICA: 72.4 +/- 7.5 %, EEGnet: 74.5 +/- 6.3). (3) To optimize MI-BCI, we advocate for integrating KBF with the ICA spatial filter. Our experimental findings clearly demonstrate that the KBF-ICA approach outperforms traditional methods, such as common spatial pattern (CSP) and EEGnet, particularly in cross-subject testing scenarios (KBF-ICA: 70.8 %, KBFCSP: 50.6 %, EEGnet: 45.1 %). Furthermore, with optimized EEG channel selection, the recognition rate of KBFICA can reach 91.8 %, whereas the other two methods, CSP and EEGnet, do not exhibit significant performance improvement through channel optimization. In conclusion, we highlight the potential of combining traditional MI-BCI decoding models with deep learning techniques to address current performance limitations in MI-BCIs. This integration holds significant theoretical and practical implications, providing a promising direction for future advancements in MI-BCI technology.

关键词： EEG rhythm brain-computer interface Motor imagery Independent component analysis Transferability

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Understanding and Addressing Variability Through Recalibration in a brain-computer interface for Intelligent Vehicle Control Using Long-Term Implanted Human Neural Data: A Case Study

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IEEE ACCESS 2025年 13卷 50379-50393页

作者： Xiao, Yicong Kellis, Spencer Reiche, Christopher. F. Solzbacher, Florian Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Blackrock Neurotech Salt Lake City UT 84108 USA USC Keck Sch Med Neurorestorat Ctr Los Angeles CA 90033 USA Univ Utah Dept Mat Sci & Engn Salt Lake City UT 84112 USA Univ Utah Dept Biomed Engn Salt Lake City UT 84112 USA

This work involved human subject in its research. Approval of all ethical and experimental procedures and protocols was granted by the FDA and IRB (NCT01997125). Even when trained on sufficient neural data, the performance of a brain-computer interface (BCI) is susceptible to error due to both systematic and random noise. Error from both sources may increase over time with changes in the neural signal or recording process. In practice, systematic error could be addressed through recalibration of model parameters, where the frequency of recalibration is based on the rate of performance loss. Random prediction error could be addressed by incorporating more observations into each prediction. However, recalibration incurs computational overhead and time delay;increasing the number of observations per prediction increases the latency from observation to prediction. The needs of the BCI application will determine whether the necessary frequency of recalibration or increased observations per prediction are valid configurations. This study evaluates recalibration as a mechanism to address accumulating systematic error in the unique context of vehicle driving, using neural data recorded previously in a driving simulation environment. A prior study demonstrated vehicle control but required some modifications to the paradigm to address variability, such as increasing the amount of data used to make each inference. In this study, offline analysis revealed substantial variation in the neural data recorded during the original experiments. Adding recalibration using labels assigned by the route navigation system improved classification accuracy from 55% to over 90% and reduced the latency of the classifier output. These findings demonstrate the potential of recalibration in BCI as a mitigation for neural variability, with application to vehicle control by individuals with tetraplegia.

关键词： Accuracy Training Support vector machines Navigation Electrodes Adaptation models Urban areas Systematics Salt Motors brain-computer interface support vector machine offline analysis discrete control neural activity vehicle system control

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Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2025年第1期22卷 1-13页

作者： Kim, Myeong Sun Park, Hyunju Kwon, Ilho An, Kwang-Ok Kim, Hayeon Park, Gyulee Hyung, Wooseok Im, Chang-Hwan Shin, Joon-Ho Minist Hlth & Welf Translat Res Ctr Rehabil Robots Natl Rehabil Ctr Seoul South Korea Minist Hlth & Welf Dept Rehabil & Assist Technol Rehabil Res Inst Natl Rehabil Ctr Seoul South Korea Minist Hlth & Welf Dept Healthcare & Publ Hlth Res Rehabil Res Inst Natl Rehabil Ctr Seoul South Korea Hanyang Univ Dept Artificial Intelligence Seoul South Korea Hanyang Univ Dept Biomed Engn Seoul South Korea Minist Hlth & Welf Dept Rehabil Med Natl Rehabil Ctr Seoul South Korea Natl Rehabil Ctr Dept Rehabil Med Seoul 01022 South Korea

Backgroundbrain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent *** randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural *** to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference = 0.52, 95% CI = 0.03-1.00, p = 0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p = 0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional *** training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery *** registrationCRIS (KCT0009013).

关键词： Stroke Rehabilitation brain-machine interface brain-computer interface Randomized clinical trial

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Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke

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JOURNAL OF NEUROPHYSIOLOGY 2025年第1期133卷 333-341页

作者： Svejgaard, Benjamin Modrau, Boris Hernandez-Gloria, Jose Jesus Wested, Carina L. Dosen, Strahinja Stevenson, Andrew J. T. Mrachacz-Kersting, Natalie Aalborg Univ Ctr Neurotechnol & Rehabil Aalborg Denmark Aalborg Univ Hosp Dept Neurol Aalborg Denmark North Denmark Reg Hosp Neuroenhed Nord Bronderslev Denmark Albert Ludwigs Univ Freiburg Inst Sport & Sport Sci Freiburg Germany Aalborg Univ Dept Hlth Sci & Technol Aalborg Denmark Albert Ludwigs Univ Freiburg BrainLinks Brain Tools Ctr IMBIT Freiburg Germany

In a recently developed associative rehabilitative brain-computer interface (BCI) system, electroencephalography (EEG) is used to identify the most active phase of the motor cortex during attempted movement and deliver precisely timed peripheral stimulation during training. This approach has been demonstrated to facilitate corticospinal excitability and functional recovery in patients with lower limb weakness following stroke. The current study expands those findings by investigating changes in corticospinal excitability following the associative BCI intervention in patients with post stroke with upper limb weakness. In a randomized controlled trial, 24 patients with subacute stroke, subdivided into an intervention group and a "sham" control group, performed 30 wrist extensions. The intervention comprised 30 pairings of single peripheral nerve stimulation at the motor threshold, timed so that the generated afferent volley arrived at the motor cortex during the peak negativity of the movement-related cortical potential (MRCP), which was identified with EEG. The sham group underwent the same intervention, though the intensity of the nerve stimulation was below the perception threshold. Immediately after training, patients in the associative group exhibited significantly larger amplitudes of muscular-evoked potentials, compared with pretraining measurements in response to transcranial magnetic stimulation. These changes persisted for at least 30 min and were not observed in the sham group. We demonstrate that motor-evoked potential amplitudes increased significantly following paired associative BCI training targeting upper limb muscles in patients with subacute stroke, which is in line with results from lower limb studies. NEW & NOTEWORTHY We have demonstrated that a single training session with an associative brain-computer interface increased corticospinal excitability in patients suffering from upper limb weakness following stroke. This is the first time such an eff

关键词： brain-computer interface corticospinal excitability stroke transcranial magnetic stimulation upper limb

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Construction of a Multimodal 3D Atlas for a Micrometer-Scale brain-computer interface Based on Mixed Reality

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CURRENT MEDICAL SCIENCE 2025年第2期45卷 194-205页

作者： Zhou, Hong Yan, Zi-neng Gao, Wei-hang Lv, Xiang-xin Luo, Rui Hoellwarth, Jason Shih He, Lei Yang, Jia-ming Zhang, Jia-yao Wang, Hong-lin Xie, Yi Chen, Xiao-liang Xue, Ming-di Fang, Ying Duan, Yu-yu Li, Rui-yuan Wang, Xu-dong Wang, Rui-lin Xie, Mao Huang, Li Liu, Peng-ran Ye, Zhe-wei Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Orthoped Surg Wuhan 430022 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Lab Intelligent Med Wuhan 430022 Peoples R China Hosp Special Surg Osseointegrat Limb Replacement Ctr Limb Lengthening & Complex Reconstruct Serv New York NY 10021 USA Univ Chicago Chicago IL 60637 USA Fuzhou Univ Dept Orthoped Affiliated Prov Hosp Fuzhou 350001 Peoples R China Fujian Prov Hosp Dept Orthoped Fuzhou 350001 Peoples R China Ningxia Med Univ Peoples Hosp Ningxia Hui Autonomous Reg Yinchuan 750001 Peoples R China Hubei Univ Chinese Med Coll Clin Chinese Med Wuhan 430065 Peoples R China Wuhan Neuracom Technol Dev Co Ltd Wuhan 430200 Peoples R China

ObjectiveTo develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) *** invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0 T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR *** study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical *** multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.

关键词： brain-computer interface Mixed reality Three-dimensional atlas Multimodal imaging

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A Bibliometric Analysis of the Application of brain-computer interface in Rehabilitation Medicine Over the Past 20 Years

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JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2025年 18卷 1297-1317页

作者： Huang, Jinghui Huang, Lele Li, Ying Fang, Fanfu Naval Med Univ Dept Rehabil Med Affiliated Hosp 1 Shanghai 200433 Peoples R China Peking Univ Third Hosp Inst Sports Med Peking Dept Sports Med Beijing 100191 Peoples R China

Objective: This study aims to conduct a bibliometric analysis of the application of brain- computer interface (BCI) in rehabilitation medicine, assessing the current state, developmental trends, and future potential of this field. By systematically analyzing relevant literature, we seek to identify key research themes and enhance understanding of BCI technology in rehabilitation. Methods: We utilized bibliometric analysis tools such as VOSviewer and CiteSpace to screen and analyze 426 relevant articles from the Web of Science Core Collection (WoSCC) database. We quantitatively evaluated citation patterns, publication trends, and the collaboration networks of research institutions and authors to uncover research hotspots and frontier dynamics in the field. Results: The findings indicate a continuous increase in research publications since 2003, with a notable peak occurring between 2019 and 2021. The analysis revealed that motor imagery, motor recovery, and signal processing are the predominant research themes. Furthermore, the United States and China are leading in the publication volume related to BCI and rehabilitation medicine. Key research institutions include the University of T & uuml;bingen and the New York State Department of Health, with significant contributions from scholars like Niels Birbaumer. Conclusion: Although the current research on BCI in rehabilitation medicine shows significant potential and efficacy, further exploration of certain research directions is needed, along with the promotion of interdisciplinary collaboration to comprehensively address complex real-world issues such as motor function impairment. Future research should focus on optimizing training models, enhancing technical feasibility, and exploring home rehabilitation applications to facilitate the broader adoption of BCI technology in rehabilitation medicine.

关键词： brain-computer interface BCI rehabilitation motor recovery motor imagery signal processing

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