脑机接口 / BCI

FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network

Young-Min Go, Seong-Hyun Yu, Hyeong-Yeong Park, Minji Lee, Ji-Hoon Jeong

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

Comments 12 pages,5 figures, and 2 tables

The evolution of AI approaches for motor imagery EEG-based BCIs

Aurora Saibene, Silvia Corchs, Mirko Caglioni, Francesca Gasparini

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

Comments Submitted to Italian Workshop on Artificial Intelligence for Human Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Italy

Cueless EEG imagined speech for subject identification: dataset and benchmarks

无提示EEG想象言语用于受试者识别：数据集与基准测试

Ali Derakhshesh, Zahra Dehghanian, Reza Ebrahimpour, Hamid R. Rabiee

机构 * Department of Computer Engineering, Sharif University of Technology（沙斐大学计算机工程系） ； Center for Cognitive Science, Institute for Convergence Science and Technology (ICST)（融合科学与技术研究所认知科学中心） ； Sharif University of Technology（沙斐大学） 纠错

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

AI总结 本研究提出无提示EEG想象言语范式，通过自然选择和想象语义词实现高准确率的受试者识别，展示了其在脑机接口中的应用潜力。

Journal ref IEEE Transactions on Biometrics, Behavior, and Identity Science ( Volume: 8, Issue: 2, March 2026)

Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models

面向EEG-语言基础模型的语义对齐连续隐式预测建模

Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee, Seong-Whan Lee

专题命中 EEG解码 ：EEG(title,title_cn)；neural decoding(abstract)；分类 cs.LG

AI总结 本文提出BLPM模型，通过CELP编码器与MQSD模块对齐EEG语义，解决EEG基础模型预训练的关键挑战，在多基准任务中实现良好泛化。

Comments 19 pages, 3 figures; supplementary material included

Teager-Kaiser Energy Methods For EEG Feature Extraction In Biomedical Applications

Teager-Kaiser能量方法在生物医学应用中用于EEG特征提取

Ioanna Chourdaki, Kleanthis Avramidis, Christos Garoufis, Athanasia Zlatintsi, Petros Maragos

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

AI总结 本文研究非线性Teager-Kaiser能量算子（TKEO）在运动想象、情绪识别和癫痫检测三个任务中建模EEG能量动态，通过Gabor滤波器组和能量分离算法提取能量描述符，并与传统特征比较，发现TKEO特征在癫痫检测中平衡准确率提升约15%，在运动想象中略有提升，在情绪识别中性能相当。

Comments Accepted to EUSIPCO 2026

EMAG: Differentiable 4D Gaussian Mixture Splatting for EEG Spatial Super-Resolution

EMAG: 可微分的4D高斯混合喷溅用于EEG空间超分辨率

Alex Lazarovich, Ofir Itzhak Shahar, Gur Elkin, Ohad Ben-Shahar

机构 * Stein Faculty of Computer and Information Science（计算机与信息科学学院） ； Ben-Gurion University of the Negev（贝内尔-加利利大学） 纠错

专题命中 EEG解码 ：EEG(title,title_cn)；cortical(abstract)；分类 cs.LG

AI总结 提出EMAG框架，通过可微分的各向异性4D时空高斯混合模型，从稀疏低密度电极重建高密度EEG信号，实现空间超分辨率，并在三个基准上超越现有方法。

PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning

PRiSE-EEG: 一种基于先验的EEG基础模型，具有深度分层专家，用于跨范式EEG表示学习

Wei Xiong, Jiangtong Li, Kun Zhu, Jie Li

专题命中 EEG解码 ：EEG(title,title_cn)；cortical(abstract)；分类 eess.SP

AI总结 本文提出PRiSE-EEG，一种基于先验的EEG基础模型，通过深度分层专家实现跨范式EEG表示学习，通过CKA校准的深度分层专家分配提升了模型的跨范式性能。

Comments 32 pages, 14 figures

TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning

TRACE: 基于自回归跨通道专家的EEG表示学习时序路由

Fan Ma, Qier An, Peng Chen, Lingfei Qian, Xiang Lan, Mingyang Jiang, Zhiling Gu, Xenophon Papademetris, Hua Xu

机构 * Department of Biomedical Informatics and Data Science, Yale University（耶鲁大学生物医学信息学与数据科学系） 纠错

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

AI总结 TRACE通过自回归跨通道专家实现EEG表示学习，解决多通道非平稳信号的时序路由问题，提升跨通道一致性与时间适应性。

BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal

BandRouteNet: 一种用于EEG去噪的自适应频段路由神经网络

Phat Lam

机构 * Ho Chi Minh City University of Technology（胡志明市技术大学） 纠错

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

AI总结 本文提出BandRouteNet，通过频段特定处理与全频段上下文建模，有效去除EEG信号中的EOG和EMG干扰，提升去噪性能与参数效率。

Comments Preprint version, 5 pages

Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation

迈向统一的多任务EEG分析与低秩适应

Sicheng Dai, Kai Chen, Hongwang Xiao, Shan Yu, Qiwei Ye

机构 * Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所） ； School of Artifcial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院） ； State Key Laboratory of Brain Cognition（脑认知国家重点实验室） ； Beijing Academy of Artificial Intelligence（北京人工智能研究院） ； State Key Laboratory of Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室） 纠错

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

AI总结 本文提出MTEEG框架，通过低秩适应模块解决多任务EEG分析中的任务冲突问题，展示了多任务方法在提升性能和降低计算成本方面的优势。

Journal ref EMBC 2026

Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

零样本神经先验用于可泛化的跨被试和跨任务脑电解码

Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech

机构 * Carnegie Mellon University（卡内基梅隆大学） 纠错

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

AI总结 针对脑电信号跨被试和跨任务泛化难题，提出零样本解码框架，采用渐进解冻策略微调Transformer，在大型数据集上实现优于基线的性能。

Comments EEG Decoding research

EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses

基于EEG的受关注说话人提取从记录的语音混音中，应用于神经引导的听力假体

Simon Van Eyndhoven, Tom Francart, Alexander Bertrand

机构 * ESAT Laboratory of KU Leuven（KU莱顿大学ESAT实验室） ； KU Leuven（KU莱顿大学） ； Department of Electrical Engineering (ESAT)（电气工程系（ESAT）） ； Department of Neurosciences（神经科学系） 纠错

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

AI总结 本文提出了一种基于EEG的受关注说话人提取方法，利用麦克风阵列记录和EEG记录来实现噪声环境下的说话人分离与去噪，展示了在无干净语音信号的情况下，通过EEG进行的听觉注意力检测的鲁棒性。

Comments This paper is published in IEEE Transactions on Biomedical Engineering (2016) and is under copyright. Please cite this paper as: S. Van Eyndhoven, T. Francart, and A. Bertrand, "EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses", IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, pp. 1045-1056, 2017

Journal ref IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, pp. 1045-1056, 2017

SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval

SATTC: 结构感知的无标签测试时校准用于跨主体EEG到图像检索

Qunjie Huang, Weina Zhu

机构 * Yunnan University（云南大学） 纠错

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

AI总结 本文提出SATTC，一种无标签测试时校准方法，通过结合几何专家和结构专家，提升跨主体EEG到图像检索的准确性与稳定性。

Comments Accepted to CVPR 2026. Official code: https://github.com/QunjieHuang/SATTC-CVPR2026. This version corrects the dataset naming in the paper text and abstract: the experiments were conducted on THINGS-EEG2, whereas the previous version referred to it as THINGS-EEG in parts of the paper. Citations were already correct. Results are unchanged

Motor Imagery EEG Signal Classification Using Minimally Random Convolutional Kernel Transform and Hybrid Deep Learning

使用最小随机卷积核变换和混合深度学习进行运动想象EEG信号分类

Jamal Hwaidi, Mohamed Chahine Ghanem

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

AI总结 本文提出了一种基于最小随机卷积核变换和混合深度学习的运动想象EEG信号分类方法，通过高效特征提取和低计算成本实现高准确率分类。

Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks

Henrique de Lima Alexandre, Clodoaldo Aparecido de Moraes Lima

机构 * Escola de Artes, Ciências e Humanidades (USP) São Paulo, SP（艺术、科学与人文学院（USP）圣保罗，SP） 纠错

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

Comments 15 pages, BRACIS

Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling

Jiaheng Wang, Zhenyu Wang, Tianheng Xu, Yuan Si, Ang Li, Ting Zhou, Xi Zhao, Honglin Hu

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

EEG-DBNet: A Dual-Branch Network for Temporal-Spectral Decoding in Motor-Imagery Brain-Computer Interfaces

Xicheng Lou, Xinwei Li, Hongying Meng, Jun Hu, Meili Xu, Yue Zhao, Jiazhang Yang, Zhangyong Li

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

NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification

Eeshan G. Dandamudi

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

EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms

Martin Wimpff, Leonardo Gizzi, Jan Zerfowski, Bin Yang

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

Comments Submitted to: Journal of Neural Engineering

Uncertainty Detection and Reduction in Neural Decoding of EEG Signals

Tiehang Duan, Zhenyi Wang, Sheng Liu, Sargur N. Srihari, Hui Yang

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

An intertwined neural network model for EEG classification in brain-computer interfaces

Andrea Duggento, Mario De Lorenzo, Stefano Bargione, Allegra Conti, Vincenzo Catrambone, Gaetano Valenza, Nicola Toschi

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

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

Abbas Salami, Javier Andreu-Perez, Helge Gillmeister

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

Journal ref IEEE Access (2022)

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn

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

Journal ref IEEE Transactions on Biomedical Engineering 2021

A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification

Xiangyun Li, Peng Chen, Zhanpeng Bao

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

Comments 22 pages, 5 figures

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots

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

Comments 8 pages, 5 figures

Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing

Denghao Li, Pablo Ortega, Xiaoxi Wei, Aldo Faisal

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

Real-Time EEG Classification via Coresets for BCI Applications

Eitan Netzer, Alex Frid, Dan Feldman

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

Siamese Neural Networks for EEG-based Brain-computer Interfaces

Soroosh Shahtalebi, Amir Asif, Arash Mohammadi

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

Classifying Single-Trial EEG during Motor Imagery with a Small Training Set

Yijun Wang

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

Comments 13 pages, 3 figures

Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms

Heon-Gyu Kwak, Gi-Hwan Shin, Yeon-Woo Choi, Dong-Hoon Lee, Yoo-In Jeon, Jun-Su Kang, Seong-Whan Lee

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

Comments Submissoion version for IEEE International BCI Winter Conference 2025