脑机接口 / BCI

NeuroPilot: A Realtime Brain-Computer Interface system to enhance concentration of students in online learning

Asif Islam, Farhan Ishtiaque, Md. Muhyminul Haque, Farhana Sarker, Ravi Vaidyanathan, Khondaker A. Mamun

专题命中 EEG解码 ：brain-computer interface(title,abstract)；BCI(abstract)；EEG(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Comments V2: Author list updated with consent from all authors. Minor grammatical errors fixed. This work has been submitted to the IEEE for possible publication

PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding

Changhong Jing, Yan Liu, Shuqiang Wang, Bruce X. B. Yu, Gong Chen, Zhejing Hu, Zhi Zhang, Yanyan Shen

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

Uncovering the EEG Temporal Representation of Low-dimensional Object Properties

Jiahua Tang, Song Wang, Jiachen Zou, Chen Wei, Quanying Liu

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

CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding

Yuchen Zhou, Jiamin Wu, Zichen Ren, Zhouheng Yao, Weiheng Lu, Kunyu Peng, Qihao Zheng, Chunfeng Song, Wanli Ouyang, Chao Gou

机构 * Shanghai Artificial Intelligence Laboratory（上海人工智能实验室） ； Sun Yat-sen University（中山大学） ； The Chinese University of Hong Kong（香港中文大学） ； Karlsruher Institut fur Technologie（卡尔斯鲁厄理工学院） 纠错

专题命中 EEG解码 ：EEG(title,abstract)；brain-computer interface(abstract)；cortical(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program

Camilla Mannino, Pierpaolo Sorrentino, Mario Chavez, Marie-Costance Corsi

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

Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG

Siavash Shams, Richard Antonello, Gavin Mischler, Stephan Bickel, Ashesh Mehta, Nima Mesgarani

机构 * Columbia University（哥伦比亚大学） ； The Feinstein Institutes for Medical Research（费森特医学研究所） 纠错

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

Comments Accepted at Interspeech 2025 Code at https://github.com/SiavashShams/neuro2semantic

PlugSelect: Pruning Channels with Plug-and-Play Flexibility for Electroencephalography-based Brain Computer Interface

Xue Yuan, Keren Shi, Ning Jiang, Jiayuan He

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

Geometric Machine Learning on EEG Signals

Benjamin J. Choi

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

Comments Accepted in Proceedings of Machine Learning Research (PMLR), 2025

Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge

Param Rajpura, Yogesh Kumar Meena

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

Comments 5 pages, 4 figures, 1 table, accepted draft for IEEE EMBC 2024

Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research

Maryna Kapitonova, Tonio Ball

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

Comments 13 pages, 5 figures

Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?

Rishan Mehta, Param Rajpura, Yogesh Kumar Meena

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；motor imagery(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning

Chun-Ren Phang, Akimasa Hirata

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Comments 14 pages, 6 figures

Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals

Young-Eun Lee, Seo-Hyun Lee, Soowon Kim, Jung-Sun Lee, Deok-Seon Kim, Seong-Whan Lee

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

Comments 5 pages, 2 figures

SSVEP-Based BCI Wheelchair Control System

Ce Zhou

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

Comments 108 pages

EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

Andac Demir, Iya Khalil, Bulent Kiziltan

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract)；brain-computer interface(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Novel EEG-based BCIs for Elderly Rehabilitation Enhancement

Aurora Saibene, Francesca Gasparini, Jordi Solé-Casals

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

Journal ref Proceedings of the Italian Workshop on Artificial Intelligence for an Ageing Society 2021 co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) Vol-3108 26-40

Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis

Pranali Kokate, Sidharth Pancholi, Amit M. Joshi

专题命中 EEG解码 ：EEG(title,abstract)；brain-computer interface(abstract)；motor imagery(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Comments 41 Pages, Figures 32, Table 9

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Nik Khadijah Nik Aznan, Jason D. Connolly, Noura Al Moubayed, Toby P. Breckon

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

Comments Accepted as a full paper at the 2019 International Conference on Robotics and Automation (ICRA)

Feature Learning from Incomplete EEG with Denoising Autoencoder

Junhua Li, Zbigniew Struzik, Liqing Zhang, Andrzej Cichocki

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

Comments The paper was accepted for publication by Neurocomputing

Journal ref Neurocomputing, 2015, 165: 23-31

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, Dalin Zhang

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

Comments 10 pages

Towards gaze-independent c-VEP BCI: A pilot study

S. Narayanan, S. Ahmadi, P. Desain, J. Thielen

专题命中 EEG解码 ：BCI(title,abstract)；brain-computer interface(abstract,comments)；EEG(abstract)；分类 cs.LG、cs.HC

Comments 6 pages, 3 figures, 9th Graz Brain-Computer Interface Conference 2024

Journal ref 9th Graz Brain-Computer Interface Conference (2024) 343-348

Pioneering EEG Motor Imagery Classification Through Counterfactual Analysis

Kang Yin, Hye-Bin Shin, Hee-Dong Kim, Seong-Whan Lee

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

Classification of BCI-EEG based on augmented covariance matrix

Igor Carrara, Théodore Papadopoulo

专题命中 EEG解码 ：BCI(title)；EEG(title)；motor imagery(abstract)；分类 cs.LG、cs.HC

ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?

ZIPBrain：脑电基础模型能否更快、可本地部署且仍保持准确？

Lingwei Li, Yirong Kan, Peng Chen, Xu Cao, Zheng Chen, Yasuhiko Nakashima

专题命中 EEG解码 ：EEG(title,summary_cn)

AI总结 本研究提出即插即用模块ZIPBrain，利用EEG低SNR特性减少token数量，使EEG基础模型推理速度提升32.7%（最高41.8%）且平均准确率提升1.3%-10.5%，可本地部署。

Comments 7 pages(14 pages including appendix), 5 figures

Does EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding

脑电基础模型能否迁移至语音？显性与想象语音解码的基准测试

Owais Mujtaba Khanday, Mohamed Baha Ben Ticha, Sanae Belfrouh, Marc Ouellet, Jose A. Gonzalez-Lopez

机构 * University of Granada, Spain（格拉纳达大学） ； Research Centre for Information and Communication Technologies (CITIC-UGR), Spain（信息与通信技术研究中心） ； University of Chouaib Doukkali, Morocco（舒艾卜·杜卡利大学） ； Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada, Spain（大脑、心智与行为研究中心） 纠错

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract,abstract_cn)；motor imagery(abstract)

AI总结 本文开展首个脑电基础模型语音解码基准测试，对比LaBraM等模型与EEGNet等基线，发现通用脑电预训练未在语音任务上展现一致优势，为语音专用基础模型研究提供依据。

Comments 6 pages, 1 figure, 3 tables, submitted to IberSPEECH 2026

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

MSBraM：用于分层脑电动力学学习的多尺度自监督脑基础模型

Tao Zhou, Jing Han, Lingyu Shu, Zixing Zhang

机构 * Hunan University（湖南大学） 纠错

专题命中 EEG解码 ：EEG(title,summary_cn)

AI总结 针对现有方法难捕捉EEG信号多尺度时间结构的问题，提出MSBraM模型。该模型采用两阶段预训练框架，经多尺度神经分词器和课程多尺度掩码策略学习分层EEG表征。实验显示其在多个下游任务中性能优越，证明显式建模多尺度时间动态对EEG基础模型的重要性。

Multimodal Pretraining for Generalizable EEG Representation Learning

用于可泛化脑电信号表征学习的多模态预训练

Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi

机构 * University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校） 纠错

专题命中 EEG解码 ：EEG(title,summary_cn)

AI总结 研究针对癫痫EEG模型应用局限问题，开发多模态EEG基础模型，结合多种编码器，通过创新预训练技术创建癫痫相关表征。在多个数据集和评估设置下实验，该模型实现强大癫痫检测与适应新场景能力，支持可解释癫痫定位，性能达当前最优。

EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors

EVA-Net: 基于视频衍生运动先验的跨被试脑电运动解码

Ziyuan Li, Yueru Sun, Yimeng Zhang

机构 * South China University of Technology（华南理工大学） 纠错

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

AI总结 提出EVA-Net两阶段框架，利用动作视频作为语义先验，通过跨模态对比学习和对齐减少个体差异，实现跨被试脑电运动解码，在EEGMMI上取得8.66%的LOSO准确率提升。

miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models

miMamba：基于多尺度倒置曼巴模型的脑电情感识别

Xin Zhou, Dawei Huang, Xiaojing Peng, Lijun Yin

机构 * Department of Computer Science, State University of New York at Binghamton（纽约州立大学布法罗分校计算机科学系） ； College of Big Data and Internet, Shenzhen Technology University（深圳科技大学大数据与互联网学院） 纠错

专题命中 EEG解码 ：EEG(title,abstract)；brain-computer interface(abstract)；分类 eess.SP、cs.LG、cs.HC

AI总结 研究脑电情感识别，提出多尺度倒置曼巴（MS-iMamba）网络，含多尺度时间块和时空融合块，能捕捉不同尺度子序列特征及时空交互，实验表明其仅用四通道脑电信号就优于现有方法。

Comments IEEE Transactions on Affective Computing (2025)

Journal ref IEEE Transactions on Affective Computing ( Volume: 16, Issue: 4, Oct.-Dec. 2025)

Dual-Stream EEG Decoding for 3D Visual Perception

用于3D视觉感知的双流脑电解码

Ninon Lizé Masclef, Taisija Demcenko, Antonella Catanzaro, Nataliya Kosmyna

机构 * Massachusetts Institute of Technology（麻省理工学院） 纠错

专题命中 EEG解码 ：EEG(title,summary_cn)

AI总结 提出一种模仿生物视觉腹侧和背侧通路的双流脑电解码模型，通过圆形回归预测角度和EEG条件多视图扩散实现3D重建，揭示了时间动态的通道参与模式。

Comments 17 pages, 4 figures. Accepted at the Symmetry and Geometry in Neural Representations Workshop (NeurReps), NeurIPS 2025. To appear in Proceedings of Machine Learning Research (PMLR)