脑机接口 / BCI

Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders

基于稀疏自编码器的脑电图基础模型机制可解释性

William Lehn-Schiøler, Magnus Ruud Kjær, Rahul Thapa, Magnus Guldberg Pedersen, Anton Mosquera Storgaard, Nick Williams, Radu Gatej, Tue Lehn-Schiøler, Andreas Brink-Kjær, Sadasivan Puthusserypady, Sándor Beniczky, James Zou, Lars Kai Hansen

机构 * BrainCapture ； DTU Health Tech（技术大学丹麦健康技术） ； DTU Compute（技术大学丹麦计算） ； Department of Biomedical Data Science（生物医学数据科学系） ； Department of Computer Science（计算机科学系） ； Seer Medical（Seer医疗） ； Filadelfia Epilepsy Hospital（菲拉德尔菲亚癫痫医院） ； University Hospital of Copenhagen（哥本哈根大学医院） 纠错

专题命中 EEG解码 ：EEG(title,summary_cn)；分类 cs.LG、cs.HC

AI总结 通过TopK稀疏自编码器从三种EEG Transformer中提取稀疏特征字典，结合临床分类法评估单语义性和纠缠性，并引入概念引导分析目标与非目标区域，揭示模型表征失败和临床纠缠问题。

Comments Preprint. 14 pages, 7 figures, 4 tables

Neural Signals Generate Clinical Notes in the Wild

神经信号在真实环境中生成临床笔记

Jathurshan Pradeepkumar, Zheng Chen, Jimeng Sun

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） ； SANKEN, Osaka University（大阪大学SANKEN） 纠错

专题命中 EEG解码 ：EEG(summary_cn,abstract)；neural signal(title)；分类 eess.SP、cs.LG

AI总结 本文提出CELM模型，首个能从长期EEG记录中生成多尺度临床报告的EEG-语言基础模型，通过整合预训练EEG模型与语言模型，实现了可扩展的多模态学习，实验表明其在多个评估设置中均优于现有方法。

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

跨受体解码的通用性：深度学习方法的综述

Taida Li, Yujun Yan, Fei Dou, Wenzhan Song, Xiang Zhang

机构 * Department of Computer Science, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校计算机科学系） ； Department of Computer Science, Dartmouth College（达特茅斯学院计算机科学系） ； School of Computing, University of Georgia（佐治亚大学计算科学学院） ； School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院） 纠错

专题命中 EEG解码 ：EEG(title,summary_cn)；分类 eess.SP、cs.LG

AI总结 本文综述了深度学习方法在跨受体解码中的应用，分析了多源领域问题的评估协议，并系统分类了特征对齐、对抗学习等方法，探讨了理论限制和EEG基础模型的发展。

Comments Accepted manuscript in Progress in Biomedical Engineering. Minor update: corrected author affiliation in comment

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks

基于EEG的在线解码大鼠自控步态速度使用循环神经网络

Alejandro de Miguel, Nelson Totah, Uri Maoz

机构 * Schmid College of Science and Technology, Chapman University（施密特科学与技术学院，查普曼大学） ； LUCID-AI, Chapman University（LUCID-AI，查普曼大学） ； Helsinki Institute of Life Science, University of Helsinki（赫尔辛基生命科学研究所，赫尔辛基大学） ； Neuroscience Center, University of Helsinki（神经科学中心，赫尔辛基大学） ； Faculty of Pharmacy, University of Helsinki（药学院，赫尔辛基大学） ； Crean College of Health and Behavioral Sciences, Chapman University（克里安健康与行为科学学院，查普曼大学） ； Department of Biology and Bioengineering, California Institute of Technology（生物学与生物工程系，加州理工学院） ； Anderson School of Management, University of California Los Angeles（安德森管理学院，加州大学洛杉矶分校） 纠错

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；neural decoding(abstract)；cortical(abstract)

AI总结 本研究利用循环神经网络从大鼠EEG中非侵入性解码自控步态速度，实现高准确率的连续解码。

Comments 17 pages, 1 table and 7 figures

EEG-to-Gait Decoding via Phase-Aware Representation Learning

通过相意识表示学习实现EEG到步态解码

Xi Fu, Weibang Jiang, Rui Liu, Gernot R. Müller-Putz, Cuntai Guan

机构 * College of Computing and Data Science, Nanyang Technological University, Singapore 639798（计算与数据科学学院，南洋理工大学，新加坡） ； Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China（计算机科学与工程系，上海交通大学，上海） ； Institute of Neural Engineering, Graz University of Technology, Graz, Austria（神经工程研究所，格拉茨技术大学，奥地利） ； Centre of AI in Medicine (C-AIM), Nanyang Technological University, Singapore（医学人工智能中心（C-AIM），南洋理工大学，新加坡） 纠错

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；cortical(abstract)

AI总结 NeuroDyGait通过相意识表示学习实现EEG到步态解码，提升跨受试者性能并满足实时BCI需求。

Inter- and Intra-Subject Variability in EEG: A Systematic Survey

EEG中的跨受体和内受体变异性：系统调查

Xuan-The Tran, Thien-Nhan Vo, Son-Tung Vu, Thoa-Thi Tran, Manh-Dat Nguyen, Thomas Do, Chin-Teng Lin

机构 * Vietnam Maritime University（越南海洋大学） ； HAI-Smartlink Research Lab, Anchi STE Company（HAI-Smartlink研究实验室，Anchi STE公司） ； Hanoi Architectural University（河内建筑大学） ； Greenwich Vietnam, FPT University（格林威治越南，FPT大学） ； Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（计算智能与脑机接口实验室，计算机科学学院，澳大利亚人工智能研究所，工程与信息科技学院，悉尼技术大学） 纠错

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

AI总结 本文系统研究了EEG在不同范式中的变异性，分析了生物、状态、技术及分析因素，并提出了研究设计和报告的建议。

SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces

SAFE: 用于隐私保护脑机接口的安全且准确的联邦学习

Tianwang Jia, Xiaoqing Chen, Dongrui Wu

机构 * Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学） ； Zhongguancun Academy（中关村学院） 纠错

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

AI总结 SAFE通过联邦学习在保护隐私的同时提升脑机接口的解码准确性和对抗鲁棒性，无需使用目标受试者的校准数据。

Comments 12 pages, 9 figures

A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration

用于解码皮层脑电信号的综合通道方法：通过空间信息整合提升精度

Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；neural decoding(abstract)

AI总结 本文提出了一种用于解码皮层脑电信号的综合通道方法，通过整合多脑区空间信息提升解码精度，并在多个数据集上验证了其优越性能。

Journal ref 2025 33rd International Conference on Electrical Engineering (ICEE)

GCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding

GCMCG：一种面向多范式、多任务和跨受试者的EEG解码图注意力与专家融合网络

Yiqiao Chen, Zijian Huang, Juchi He, Fazheng Xu, Zhenghui Feng

机构 * Faculty of frontier sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区） ； Doumaomao NetTech Co., Ltd.（杜马莫莫网络技术有限公司） ； University of Melbourne（墨尔本大学） ； The University of New South Wales（新南威尔士大学） 纠错

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

AI总结 GCMCG通过图注意力与专家融合网络提升多范式、多任务和跨受试者EEG解码的准确性和泛化能力。

Comments 46 pages, 11 figures

CAT-Net: A Cross-Attention Tone Network for Cross-Subject EEG-EMG Fusion Tone Decoding

Yifan Zhuang, Calvin Huang, Zepeng Yu, Yongjie Zou, Jiawei Ju

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；neural decoding(abstract)

Comments This is the extended version with technical appendices. The version of record appears in AAAI-26. Please cite the AAAI version

RatioWaveNet: A Learnable RDWT Front-End for Robust and Interpretable EEG Motor-Imagery Classification

Marco Siino, Giuseppe Bonomo, Rosario Sorbello, Ilenia Tinnirello

机构 * Department of Electrical, Electronics and Informatics Engineering, University of Catania（电子、电子与信息工程系，卡塔尼亚大学） ； Department of Engineering, University of Palermo（工程系，巴勒莫大学） 纠错

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

NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training

Suli Wang, Yangshen Deng, Zhenghua Bao, Xinyu Zhan, Yiqun Duan

机构 * Technical University of Darmstadt（达姆斯塔特技术大学） ； University of Edinburgh（爱丁堡大学） ； University of Technology Sydney（悉尼技术大学） 纠错

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

DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm

Jingyuan Wang, Junhua Li

机构 * School of Computer Science and Electronic Engineering, University of Essex（计算机科学与电子工程学院，埃塞克斯大学） 纠错

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

EEG Study of the Influence of Imagined Temperature Sensations on Neuronal Activity in the Sensorimotor Cortex

Anton Belichenko, Daria Trinitatova, Aigul Nasibullina, Lev Yakovlev, Dzmitry Tsetserukou

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；cortical(abstract)

Comments Accepted to the IEEE International Conference on Systems, Man, and Cybernetics 2025 (IEEE SMC 2025), 6 pages, 6 figures, 1 table

Mind2Matter: Creating 3D Models from EEG Signals

Xia Deng, Shen Chen, Jiale Zhou, Lei Li

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；neural decoding(abstract)

EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters

Taveena Lotey, Aman Verma, Partha Pratim Roy

专题命中 EEG解码 ：EEG(title,abstract)；brain computer interface(abstract)；neural decoding(abstract)；motor imagery(abstract)

EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method

Wei Peng, Kang Liu, Jiaxi Shi, Jianchen Hu

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

Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

Xiaoqing Chen, Ziwei Wang, Dongrui Wu

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

Journal ref IEEE Trans. on Neural Systems and Rehabilitation Engineering, 32:1703-1714, 2024

SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs

Yisheng Li, Shuqiang Wang

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

Comments 11 pages, 5 figures

Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance

Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena

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

Comments 8 pages, 3 figures, 3 tables, draft of the accepted work at IJCNN, WCCI 2024

Neuro-GPT: Towards A Foundation Model for EEG

Wenhui Cui, Woojae Jeong, Philipp Thölke, Takfarinas Medani, Karim Jerbi, Anand A. Joshi, Richard M. Leahy

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

Comments Paper accepted by the 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals

Sung-Jin Kim, Dae-Hyeok Lee, Yeon-Woo Choi

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

Comments 4 pages, 1 table

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

Chin-Teng Lin, Kuan-Chih Huang, Yu-Ting Liu, Yang-Yin Lin, Tsung-Yu Hsieh, Nikhil R. Pal, Shang-Lin Wu, Chieh-Ning Fang, Zehong Cao

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

Comments The current version is accepted by iFuzzy 2020

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI

Jesse A. Livezey, Joshua I. Glaser

专题命中 EEG解码 ：neural decoding(title,abstract)；brain-computer interface(abstract)；EEG(abstract)；neural signal(abstract)

Comments 22 pages, 3 figures

Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy

Jeong-Hyun Cho, Ji-Hoon Jeong, Seong-Whan Lee

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

Comments Accepted to IEEE EMBC 2020

A novel multimodal approach for hybrid brain-computer interface

Zhe Sun, Zihao Huang, Feng Duan, Yu Liu

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

A novel approach to classify natural grasp actions by estimating muscle activity patterns from EEG signals

Jeong-Hyun Cho, Ji-Hoon Jeong, Dong-Joo Kim, Seong-Whan Lee

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

Comments 4 pages, 4 figures, conference

Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces

Michael Hersche, José del R. Millán, Luca Benini, Abbas Rahimi

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

Riemannian geometry applied to detection of respiratory states from EEG signals: the basis for a brain-ventilator interface

X Navarro-Sune, A. L. Hudson, F. De Vico Fallani, J. Martinerie, A. Witon, P. Pouget, M. Raux, T. Similowski, M. Chavez

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain computer interface(abstract)；cortical(abstract)

Comments 14 pages, 7 figures

Journal ref IEEE Transactions on Biomedical Engineering, 2016

Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects

Andreea Ioana Sburlea, Luis Montesano, Javier Minguez

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；cortical(abstract)

Comments 18 pages, 5 figures