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The role of brain-computer interfaces in restoring post-stroke lower extremity function: A systematic bibliometric analysis

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Asian Journal of Surgery 2025年第5期48卷 2976-2977页

作者： Cui Gao Huaneng Wen Mingzhu Xu Shaoyang Cui Department of Rehabilitation Medicine Shenzhen Hospital (Fu Tian) of Guangzhou University of Chinese Medicine Shenzhen 518034 China Department of Rehabilitation Medicine Shenzhen Hospital Southern Medical University Shenzhen 518100 China

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Recording Front-End Electronics for Large-Scale Implantable brain-computer interfaces: A Design Perspective

Recording Front-End Electronics for Large-Scale Implantable ...

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Custom Integrated Circuits Conference (CICC)

作者： Xiaohua Huang Dante G. Muratore Delft University of Technology The Netherlands

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

This paper discusses recent advancements in recording front-end electronics for large-scale implantable brain-computer interfaces. Various system architectures and circuit techniques can be leveraged to achieve both area- and power-efficient implementations. Here, we elaborate on the trade-offs between different approaches, with specific examples highlighting details of recently proposed solutions. We also provide several practical design tips and tricks for implementing such front-end electronics in standard CMOS technologies. Finally, we discuss the most interesting future directions for the field.

关键词： Application specific integrated circuits Systems architecture CMOS technology brain-computer interfaces Recording Standards

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Imagined Speech and Visual Imagery as Intuitive Paradigms for brain-computer interfaces

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Seo-Hyun Lee Ji-Ha Park Deok-Seon Kim Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Dept. of Artificial Intelligence Korea University Seoul Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance.

关键词： Visualization Communication systems Neural activity Signal processing Motors brain-computer interfaces Decoding Calibration Synchronization Speech processing

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Generative Adversarial Network with Adaptive Synthesis for brain-computer interfaces in Motor Imagery Classification

Generative Adversarial Network with Adaptive Synthesis for B...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiaoli Zhou Xiyuan Ye Shurui Li Yi Zhao Qiang Du Li Ke School of Computer Science Shenyang Aerospace University Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Motor Imagery (MI) is essential in brain-computer interfaces (BCIs), highlighting the central role of electroencephalography (EEG) in this technology. However, the amount of raw EEG data is often limited. Raw EEG data contains significant noise caused by individual and task-specific differences. These complications often necessitate the removal of noise, making it crucial to develop robust models capable of maintaining performance. To achieve this, synthetic data is essential to obtain cleaner signals. This paper presents a novel a novel contrastive diffusion model to adaptive synthesize clean signals for Motor Imagery Classification (CDASC-MI), achieved through three components: Clean Signal Extractor(CSE) for clean signal feature learning, Adapative Noise Extractor(ANE) for adaptively extracting subject noise and task noise, and Contrastive Module for further separating noise to obtain cleaner signals. Specifically, we first proposed the novel Cell-UNET in CSE, which is an architecture with progressive learning capabilities to effectively capture subtle changes in EEG signals. Additionally, ANE incorporates a novel Subject Feature Learning Network(SFLNet) and Cross-Domain Electrode Attention Network (CDANet). SFLNet and CDANet dynamically capturing variation subject noise and task noise. Ultimately, a Contrastive Module is designed to use contrastive learning during the denoising process to amplify the differences between clean signals and noise, ensuring robust denoising and clearer feature representation. According to experimental results from two public datasets, the proposed method achieved superior accuracies of 96.19% and 89.19%, outperforming existing approaches. It highlights its potential as a powerful method for augmenting EEG datasets and improving the accuracy and highlight its broad application values.

关键词： Representation learning Accuracy Noise Noise reduction Motors Feature extraction Diffusion models brain modeling Electroencephalography brain-computer interfaces

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The Utilization of brain-computer interfaces (BCI) with CLONALG to Amplify Exhibited Neurological Signals

The Utilization of Brain-Computer Interfaces (BCI) with CLON...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Kaushik Sathiyandrakumar Ravenwood High School Brentwood TN

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Nearly 50 million individuals are diagnosed with neurodegenerative diseases every year. Moreover, this figure is expected to grow as the population grows older. These neurodegenerative diseases have had a profound impact on the neurological functioning of an individual. For instance, being diagnosed with cerebral palsy has been correlated with poor movement and coordination, atypical development patterns, and communication and swallowing difficulties. To help these struggling individuals, scientists have developed a plethora of technologies to assimilate them into day-to-day life. For example, botox injections are commonly used to relieve muscle spasms that have prevented swallowing difficulties. However, these existing treatments have all had disadvantages. For example, botox injections have commonly led to side effects, such as a crooked smile, watery and dry eyes, and flu-like symptoms. Overall, these side effects have disrupted individuals' psychological state, causing renewed calls for better treatments. Fortunately, brain-computer interfaces (BCIs) provide hope for these individuals. For example, to minimize the consequences of cerebral palsy, brain-computer interfaces (BCIs) allow for the usage of remaining muscular pathways as a substitute for paralyzed muscles. This substitution allows for the eyes and hands to have renewed control over communication devices. In this research paper, we will analyze techniques to optimize the strength of brain-computer interfaces (BCIs) by specifically analyzing how the CLONALG algorithm will enhance the strength of brain-computer interfaces.

关键词： Support vector machines Surveys Measurement Cerebral palsy Psychology Pattern classification Muscles brain-computer interfaces Optimization Diseases

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A Novel Channel Selection Method for Near-Infrared Spectroscopy-based brain-computer interfaces

A Novel Channel Selection Method for Near-Infrared Spectrosc...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Hyung-Tak Lee Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

brain-computer interface (BCI) studies have focused on enhancing performance, reducing computational costs, and minimizing equipment size for practical applications. One effective approach to address these challenges is minimizing the number of recording channels. Among various neuroimaging modalities used in BCI development, near-infrared spectroscopy (NIRS) has unique characteristics, requiring two optodes to form a single channel. As a result, reducing the number of NIRS channels does not necessarily decrease the total number of optodes, potentially leaving the equipment bulky and inconvenient. In this study, we propose a novel channel selection algorithm, optode-based sequential forward selection (oSFS), which enhances the traditional SFS (tSFS) algorithm by accounting for the number of optodes. The proposed oSFS explores the optimal placement of optodes by simultaneously evaluating optode pairings and classification accuracy. Both tSFS and oSFS significantly improved classification accuracy compared to using all channels, but no significant difference was found between the two methods. Notably, tSFS reduced the number of optodes by only 16% compared to using all channels, whereas oSFS reduced by 52% when obtained the highest accuracy. Our study introduces the first channel selection method tailored to the unique characteristics of NIRS, making a significant step toward the practical application of practical application of NIRS-based BCIs.

关键词： Neuroimaging Accuracy Machine learning brain-computer interfaces Classification algorithms Recording Computational efficiency Functional near-infrared spectroscopy

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MQVAE: Capturing Metastable Dynamics from EEG for brain-computer interfaces

MQVAE: Capturing Metastable Dynamics from EEG for Brain-comp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yihang He Chenfei Ye Guoqing Cai Jiahui Liao Ting Ma School of Electronics and Information Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China School of Biomedical Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Research on neural dynamics indicates that cognitive processes are driven by metastable state transitions in the brain, which highlights the temporally discontinuous nature of brain activity. However, many existing methods fail to account for this discontinuity, limiting their effectiveness in modeling the brain’s temporal dynamics. To address this, we propose the Metastability Quantized Variational Autoencoder (MQVAE), a neurologically inspired neural network designed to capture metastable dynamics from electroencephalography (EEG) for brain-computer interface (BCI). In MQVAE, EEG signals are quantized into a time series of discrete latent variables using vector quantization. This quantization facilitates the identification of metastable states and the construction of a latent state space. To model state transition patterns, we incorporate a transformer module to capture global temporal dependencies among the discrete latent variables. Furthermore, a multi-scale spatial convolution layer is embedded within the encoder to extract hierarchical spatial patterns. Experiments on two public datasets demonstrate that MQVAE outperforms state-of-the-art methods in both emotion recognition and motor imagery classification tasks. Interpretability analyses further confirm that the features learned by MQVAE are neurophysiologically meaningful.

关键词： Limiting Vector quantization Autoencoders Time series analysis brain modeling Transformers Motors Electroencephalography brain-computer interfaces Speech processing

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DeepSeek or ChatGPT: Can brain-computer interfaces/brain-inspired computing achieve leapfrog development with large AI models?

Brain-X

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brain-X 2025年第1期3卷

作者： Bai, Long Chen, Shugeng Wang, Peng Chen, He Yan, Jiaqing Zhu, Xiaojian Song, Enming Tian, Bobo Su, Jiacan Li, Xiaoli Organoid Research Center Institute of Translational Medicine Shanghai University Shanghai China Department of Orthopedics Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai China Wenzhou Institute of Shanghai University Zhejiang Wenzhou China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Department of Language Literature and Communication Faculty of Humanities Vrije Universiteit Amsterdam Amsterdam Netherlands School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou China Pazhou Laboratory Guangdong Guangzhou China School of Electrical and Control Engineering North China University of Technology Beijing China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Key Laboratory of Polar Materials and Devices Department of Electronics Ministry of Education East China Normal University Shanghai China

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Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2025年第15期122卷 e2419304122页

作者： Kim, Hodam Kim, Ju Hyeon Lee, Yoon Jae Lee, Jimin Han, Hyojeong Yi, Hoon Kim, Hyeonseok Kim, Hojoong Kang, Tae Woog Chung, Suyeong Ban, Seunghyeb Lee, Byeongjun Lee, Haran Im, Chang-Hwan Cho, Seong J. Sohn, Jung Woo Yu, Ki Jun Kang, June Yeo, Woon-Hong Georgia Inst Technol Inst Matter & Syst Wearable Intelligent Syst & Healthcare Ctr Atlanta GA 30332 USA Georgia Inst Technol Coll Engn George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Yonsei Univ Div Biomed Engn Wonju 26493 South Korea Inha Univ Dept Mech Engn Incheon 22212 South Korea Georgia Inst Technol Sch Elect & Comp Engn Coll Engn Atlanta GA 30332 USA Hanyang Univ Dept Biomed Engn Seoul 04763 South Korea Kumoh Natl Inst Technol Dept Aeronaut Mech & Elect Convergence Engn Gumi 39177 South Korea Chungnam Natl Univ Dept Mech Engn Daejeon 34134 South Korea Kumoh Natl Inst Technol Sch Mech Syst Engn Gumi 39177 South Korea Yonsei Univ Sch Elect & Elect Engn Funct Biointegrated Elect & Energy Management Lab Seoul 03722 South Korea Georgia Inst Technol Coll Engn Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Sch Med Atlanta GA 30332 USA Georgia Inst Technol Parker H Petit Inst Bioengn & Biosci Atlanta GA 30332 USA

Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 k Omegacm-2) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.

关键词： micro-brain sensors brain-computer interfaces electroencephalography augmented reality conducting polymer

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brain–computer interfaces for human gait restoration

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Control Theory and Technology 2021年第4期19卷 516-528页

作者： Zoran Nenadic Department of Biomedical Engineering University of CaliforniaIrvineCA 92697USA Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCA 92697USA

In this review article, we present more than a decade of our work on the development of brain–computer interface (BCI)systems for the restoration of walking following neurological injuries such as spinal cord injury (SCI) or stroke. Most ofthis work has been in the domain of non-invasive electroencephalogram-based BCIs, including interfacing our system witha virtual reality environment and physical prostheses. Real-time online tests are presented to demonstrate the ability ofable-bodied subjects as well as those with SCI to purposefully operate our BCI system. Extensions of this work are alsopresented and include the development of a portable low-cost BCI suitable for at-home use, our ongoing eforts to develop afully implantable BCI for the restoration of walking and leg sensation after SCI, and our novel BCI-based therapy for strokerehabilitation.

关键词： brain-computer interfaces Neuroprosthesis Spinal cord injury Paraplegia Gait

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