脑机接口 / BCI

EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface

基于EEG的AI-BCI轮椅前进：用于脑机接口的混合深度学习与运动想象

Bipul Thapa, Biplov Paneru, Bishwash Paneru, Khem Narayan Poudyal

机构 * Department of Computer Science and Engineering, Kathmandu University（加德满都大学计算机科学与工程系） ； Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院电子与通信工程系） ； Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布万大学工程学院普尔乔克校区应用科学与化学工程系） 纠错

专题命中 运动想象 ：EEG(title,title_cn)；BCI(title,title_cn)；motor imagery(title,abstract)；brain computer interface(title)

AI总结 本文提出一种结合运动想象左右手运动机制的AI集成方法，利用EEG数据模拟轮椅导航，采用CNN-Transformer混合模型实现高准确率的EEG分类。

Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals

皮质-状态空间模型：一种用于从脑电信号中解码运动想象的深度状态空间模型

Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura

机构 * Keio University（庆应大学） 纠错

专题命中 运动想象 ：cortical(title,summary_cn)；EEG(title,abstract)；motor imagery(title,abstract)；brain-computer interface(abstract)

AI总结 针对运动想象脑电信号分类中受生理伪迹影响及难以捕捉细粒度依赖关系的问题，提出Cortical-SSM扩展深度状态空间模型，在两个大规模数据集上验证，优于基线方法且可解释，提高了脑电分类可靠性，支持脑机接口系统发展。

Comments Accepted by Journal of Neural Engineering

Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet

在运动想象EEG分类中平衡可解释性和性能：ANFIS-FBCSP-PSO和EEGNet的比较研究

Farjana Aktar, Mohd Ruhul Ameen, Akif Islam, Md Ekramul Hamid

机构 * University of Rajshahi（拉贾沙希大学） 纠错

专题命中 运动想象 ：EEG(title,title_cn)；BCI(summary_cn,abstract)；motor imagery(title)；brain-computer interface(abstract)

AI总结 本文比较了ANFIS-FBCSP-PSO与EEGNet在BCI竞赛IV-2a数据集上的性能，发现模糊神经模型在内子试验中表现更优，而深度模型在跨受试者测试中更具泛化能力，为选择MI-BCI系统提供指导。

Comments Accepted at the 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence and Networking (QPAIN 2026)

Journal ref 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

CFSPMNet：跨受体傅里叶引导的空间-块Mamba网络用于中风患者EEG运动想象解码

Xiangkai Wang, Yun Zhao, Dongyi He, Qingling Xia, Gen Li, Xinlai Xing, Yuchi Pan, Bin Jiang

机构 * School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院） ； Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots（重庆市人形机器人感知与自主学习重点实验室） ； Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission（重庆市教育委员会先进设备智能重点实验室） ； School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆理工大学电子工程学院智能健康学院） ； Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系） ； School of Pharmacy and Bioengineering, Chongqing University of Technology（重庆理工大学药学院与生物工程学院） 纠错

专题命中 运动想象 ：EEG(title,title_cn)；motor imagery(title,abstract)；BCI(abstract,abstract_cn)；分类 cs.LG

AI总结 本文提出CFSPMNet，通过傅里叶域状态重构和共享-私有原型匹配，提升中风患者EEG运动想象解码的跨患者适应性，实现68.23%和73.33%的准确率。

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

通过强化学习学习连续神经解码的残余运动学校正

Jiamian Li, Niall McShane, Attila Korik, Naomi du Bois, Karl McCreadie, Leen Jabban, Benjamin Metcalfe, Özgür Şimşek, Damien Coyle

机构 * University of Bath（巴斯大学） ； University of Ulster（阿尔斯特大学） ； Bath Institute for the Augmented Human（巴斯增强人类研究所） 纠错

专题命中 运动想象 ：BCI(summary_cn,abstract)；EEG(summary_cn,abstract)；neural decoding(title)；motor imagery(abstract)

AI总结 研究针对基于EEG的BCI连续3D运动想象解码难题，提出CNN-LSTM-RL两阶段框架，用强化学习对解码器输出校正残余运动学误差，实验表明该框架有效提升解码性能，推动相关领域发展。

MI 2 MI: Training Dyad with Collaborative Brain-Computer Interface and Cooperative Motor Imagery Tasks for Better BCI Performance

Shiwei Cheng, Jialing Wang

专题命中 运动想象 ：BCI(title,abstract)；brain-computer interface(title,abstract)；motor imagery(title,abstract)；EEG(abstract)

Functional Connectivity-Guided Band Selection for Motor Imagery Brain-Computer Interfaces

基于功能连接的带宽选择用于运动想象脑机接口

Natália Araújo do Carmo, Aarthy Nagarajan

专题命中 运动想象 ：brain-computer interface(title,abstract)；motor imagery(title,abstract)；BCI(abstract,abstract_cn)；EEG(abstract,abstract_cn)

AI总结 本文提出基于功能连接的带宽选择方法，用于改进运动想象脑机接口的解码性能，通过计算相位基连接性识别最判别性的频段，减少CSP拟合次数，提升鲁棒性和准确性。

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

用于运动想象解码中主题自适应脑电基础模型的堆叠式LoRA

Aymen Sarhane, Fouad Lbakali, Mouad Souissi, Jonathan Lys, Giulia Lioi

机构 * IMT Atlantique, Lab-STICC（法国布雷斯特的IMT大西洋学院，Lab-STICC实验室） 纠错

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract,abstract_cn)；brain-computer interface(abstract)

AI总结 脑机接口的脑电图解码面临受试者间差异大的挑战。基于现有脑电基础模型，研究不同低秩适应策略对运动想象分类的影响，提出堆叠式LoRA框架，有效减轻受试者间差异，在多数组合中获最佳准确率。

Comments 10 pages, 1 figure, 2 tables. Accepted at the 2026 IEEE World Congress on Computational Intelligence (WCCI 2026), Maastricht, Netherlands

Optimal Time Window and Frequency Bandwidth Parameter Combination for Subject-Specific Motor Imagery EEG Classification

面向主体特异性运动想象EEG分类的最优时间窗口和频率带宽参数组合

Matthew A. McCartney, Liisa A. Kivioja, Sonal S. Baberwal, Shirley Coyle

专题命中 运动想象 ：EEG(title,title_cn)；motor imagery(title)；分类 eess.SP

AI总结 本文研究了在运动想象EEG分类中，同时优化时间窗口和频率带宽参数对分类性能的影响，通过在109名受试者上训练和测试不同参数组合的主体特异性模型，并利用重复测量方差分析揭示了不同带宽和时间窗口在准确性上的显著差异，发现(0,4)秒和(4,12)Hz的组合在所有受试者中表现最佳。

Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

Huanyu Wu, Siyang Li, Dongrui Wu

专题命中 运动想象 ：brain-computer interface(title,abstract)；EEG(title,abstract)；motor imagery(title,abstract)；分类 cs.LG、cs.HC

Journal ref IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:527-536, 2024

From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs

从脑电图清理到解码：伪迹拒绝在基于运动想象的脑机接口中的作用

Davoud Hajhassani, Bruno Aristimunha, Paul-Adrien Graignic, Apolline Mellot, Lionel Kusch, Arnaud Delorme, Thomas Semah, Arnault H. Caillet

专题命中 运动想象 ：EEG(title,summary_cn)；BCI(summary_cn,abstract)；motor imagery(abstract)；分类 eess.SP

AI总结 提出快速自动伪迹拒绝方法FAAR，通过计算伪迹敏感特征、自适应阈值选择，自动拒绝污染脑电片段，在13个公开MI数据集上验证其能降低跨被试性能变异，尤其提升低信噪比条件下的解码性能。

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Rattanaphon Chaisaen, Phairot Autthasan, Nopparada Mingchinda, Pitshaporn Leelaarporn, Narin Kunaseth, Suppakorn Tammajarung, Poramate Manoonpong, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Comments in press

Journal ref IEEE Sensors Journal 2020

Research on Event-Related Desynchronization of Motor Imagery and Movement Based on Localized EEG Cortical Sources

Yuqing Wang

专题命中 运动想象 ：motor imagery(title,abstract)；cortical(title,abstract)；EEG(title)；分类 q-bio.NC、eess.SP

3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention

Shiwei Cheng, Yuejiang Hao

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

Ce Zhang, Young-Keun Kim, Azim Eskandarian

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Comments Provisionally Accepted by Journal of Neural Engineering

Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning

Patcharin Cheng, Phairot Autthasan, Boriwat Pijarana, Ekapol Chuangsuwanich, Theerawit Wilaiprasitporn

专题命中 运动想象 ：brain-computer interface(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；EEG(abstract)

Journal ref TENCON 2018 - 2018 IEEE Region 10 Conference

Emotion-robust EEG Classification for Motor Imagery

Abdul Moeed

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain computer interface(abstract)

Comments 9 pages

Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery

基于EEG的混合视觉和运动想象的机器人抓取与放置控制

Yichang Liu, Tianyu Wang, Ziyi Ye, Yawei Li, Yu-Gang Jiang, Shouyan Wang, Yanwei Fu

机构 * Institute of Science and Technology for Brain-inspired Intelligence (ISTBI), Fudan University（复旦大学类脑智能科学与技术研究院） ； School of Data Science, Fudan University（复旦大学大数据学院） ； Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身智能研究院） ； Shanghai Key Laboratory of Multimodal Embodied AI（上海市多模态具身智能重点实验室） ； Shanghai Innovation Institute（上海创新研究院） ； ETH Zürich（苏黎世联邦理工学院） 纠错

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；neural signal(abstract)

AI总结 本文提出一种整合EEG视觉和运动想象与机器人控制的框架，实现实时意图驱动的抓取与放置。系统通过离线预训练解码器在在线流式管道中零样本方式实现神经信号与物理控制的连接，实现视觉意图到机器人动作的转换，验证了纯想象基BCI范式在实际人机协作中的灵活性。

Comments ICRA 2026

AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification

Galvin Brice S. Lim, Brian Godwin S. Lim, Argel A. Bandala, John Anthony C. Jose, Timothy Scott C. Chu, Edwin Sybingco

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Comments This work has been submitted to the IEEE for possible publication

Journal ref IEEE Access. 13(2025) 187383-187409

Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

Syed Saim Gardezi, Soyiba Jawed, Mahnoor Khan, Muneeba Bukhari, Rizwan Ahmed Khan

专题命中 运动想象 ：BCI(title,abstract)；motor imagery(title,abstract)；brain-computer interface(abstract)；EEG(abstract)

Comments 26 pages

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

Tianwang Jia, Lubin Meng, Siyang Li, Jiajing Liu, Dongrui Wu

专题命中 运动想象 ：brain-computer interface(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；EEG(abstract)

Journal ref IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:3442-3451, 2024

A Spatial-Spectral and Temporal Dual Prototype Network for Motor Imagery Brain-Computer Interface

Can Han, Chen Liu, Yaqi Wang, Crystal Cai, Jun Wang, Dahong Qian

专题命中 运动想象 ：brain-computer interface(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；EEG(abstract)

EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification

Wangdan Liao, Weidong Wang

专题命中 运动想象 ：BCI(title,abstract)；motor imagery(title,abstract)；brain-computer interface(abstract)；EEG(abstract)

Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification

Shadi Sartipi, Mujdat Cetin

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling

Xiong Xiong, Li Su, Jinguo Huang, Guixia Kang

专题命中 运动想象 ：brain computer interface(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；EEG(abstract)

Comments 22 pages, 7 figures

Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

Mahbod Nouri, Faraz Moradi, Hafez Ghaemi, Ali Motie Nasrabadi

专题命中 运动想象 ：BCI(title,abstract)；motor imagery(title,abstract)；brain-computer interface(abstract)；EEG(abstract)

Comments 27 pages, 6 figures, 8 tables, 1 algorithm

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Yimin Hou, Shuyue Jia, Xiangmin Lun, Ziqian Hao, Yan Shi, Yang Li, Rui Zeng, Jinglei Lv

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

Sion An, Soopil Kim, Philip Chikontwe, Sang Hyun Park

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Comments 6 pages. This paper was accepted at IROS2020

Journal ref 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network

Zumray Dokur, Tamer Olmez

专题命中 运动想象 ：EEG(title,abstract)；motor imagery(title,abstract)；BCI(abstract)；brain-computer interface(abstract)

Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems

Jianli Yu, Zhuliang Yu

专题命中 运动想象 ：BCI(title,abstract)；motor imagery(title,abstract)；brain-computer interface(abstract)；EEG(abstract)