脑机接口 / BCI

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition

Zhen Liang, Rushuang Zhou, Li Zhang, Linling Li, Gan Huang, Zhiguo Zhang, Shin Ishii

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

Journal ref IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29(2021) 1913-1925

EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification

Subhranil Bagchi, Deepti R. Bathula

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

Comments Preprint and Supplementary material. 17 pages, 13 figures and 4 tables

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Shu Gong, Kaibo Xing, Andrzej Cichocki, Junhua Li

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

Comments Accepted for publication in the IEEE Transactions on Cognitive and Developmental Systems

Journal ref IEEE Transactions on Cognitive and Developmental Systems, 2021

Convolutional Neural Networks for Automatic Detection of Artifacts from Independent Components Represented in Scalp Topographies of EEG Signals

Giuseppe Placidi, Luigi Cinque, Matteo Polsinelli

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

Journal ref Computers in Biology and Medicine. 132 (2021) 104347

EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks

Ozan Ozdenizci, Safaa Eldeeb, Andac Demir, Deniz Erdogmus, Murat Akcakaya

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

Comments Accepted for publication at Biomedical Signal Processing and Control

Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

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

Comments Accepted for the 25th International Conference on Pattern Recognition (ICPR 2020)

The "Sound of Silence" in EEG -- Cognitive voice activity detection

Rini A Sharon, Hema A Murthy

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

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini

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

Comments 8 pages, 6 figures, 5 tables

A large-scale evaluation framework for EEG deep learning architectures

Felix A. Heilmeyer, Robin T. Schirrmeister, Lukas D. J. Fiederer, Martin Völker, Joos Behncke, Tonio Ball

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

Comments 7 pages, 3 figures, final version accepted for presentation at IEEE SMC 2018 conference

An Analysis of the Accuracy of the P300 BCI

Nitzan S. Artzi, Oren Shriki

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

Object Segmentation in Images using EEG Signals

Eva Mohedano, Graham Healy, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor, Alan F. Smeaton

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

Comments This is a preprint version prior to submission for peer-review of the paper accepted to the 22nd ACM International Conference on Multimedia (November 3-7, 2014, Orlando, Florida, USA) for the High Risk High Reward session. 10 pages

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG

Francesco Stefano Carzaniga, Gary Tom Hoppeler, Michael Hersche, Kaspar Anton Schindler, Abbas Rahimi

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

Comments Published at ICLR 2025, see https://openreview.net/forum?id=b57IG6N20B. Code is available at https://github.com/IBM/eeg-ieeg-brain-compressor

Early Prediction of Epilepsy Seizures VLSI BCI System

Zaghloul Saad Zaghloul, Magdy Bayoumi

专题命中 EEG解码 ：BCI(title,abstract)；EEG(abstract)；分类 eess.SP、cs.HC

Comments Novel predictive BCI VLSI architecture

Head-related Impulse Response-based Spatial Auditory Brain-computer Interface

Chisaki Nakaizumi, Toshie Matsui, Koichi Mori, Shoji Makino, Tomasz M. Rutkowski

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

Comments 5 pages, 1 figure, submitted to 6th International Brain-Computer Interface Conference 2014, Graz, Austria

EgoBrain: Synergizing Minds and Eyes For Human Action Understanding

EgoBrain：协同心智与视觉以实现人类行为理解

Nie Lin, Yansen Wang, Dongqi Han, Weibang Jiang, Jingyuan Li, Ryosuke Furuta, Yoichi Sato, Dongsheng Li

机构 * The University of Tokyo（东京大学） ； Microsoft Research Asia（微软亚洲研究院） 纠错

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

AI总结 研究人员构建了全球首个同步脑电与第一人称视觉的大规模多模态数据集EgoBrain，开发多模态学习框架实现行为理解，跨参与者与环境验证达66.70%准确率，为脑机接口研究提供新范式。

Comments Accepted to ICLR 2026. Camera-ready version. Project page: https://lin-nie.github.io/EgoBrain/

A Landau-Ginzburg Phenomenology of Sleep-Stage Transitions

睡眠阶段转变的朗道-金兹堡现象学

Alexander Poltorak

专题命中 EEG解码 ：EEG(summary_cn,abstract)；cortical(abstract)；分类 q-bio.NC

AI总结 本研究构建朗道-金兹堡现象学模型解释睡眠阶段转变的特性，通过EEG/PSG数据验证其一致性，为睡眠阶段转变的机制研究提供可测试的理论框架。

Comments 23 pages, 5 figures, 7 tables. Theoretical and computational framework; no new empirical data

B[FM]$^2$: Brain Foundation Model via Flow Matching with SplitUNet

B[FM]$^2$: 基于流匹配与SplitUNet的脑基础模型

Jaedong Hwang, Kathleen Zhang, Wei Dai, Konstantinos Kontras, Maarten Vanmarcke, Maarten De Vos, Ila Fiete, Paul Pu Liang

机构 * Massachusetts Institute of Technology（麻省理工学院） ； KU Leuven（鲁汶大学） 纠错

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

AI总结 提出B[FM]$^2$，通过连续时间流匹配直接在原始EEG信号上预训练，避免离散化带来的时间动态损失，并设计SplitUNet网络处理时间-电极不对称性，在9个下游任务中7个达到最优，预训练数据量减少约30倍。

Shared control schematic for brain controlled vehicle based on fuzzy logic

基于模糊逻辑的脑控车辆共享控制方案

Na Dong, Wen-qi Zhang, Zhong-ke Gao

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

AI总结 本文提出基于模糊逻辑的脑控车辆共享控制方案，通过设计辅助控制器实现自动控制与脑控制的协同，提升系统控制性能。

Efficient Coherence Inference Using the Demodulated Band Transform and a Generalized Linear Model

利用解调带变换和广义线性模型实现高效的相干推断

Md Rakibul Mowla, Sukhbinder Kumar, Ariane E. Rhone, Brian J. Dlouhy, Christopher K. Kovach

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

AI总结 本文提出基于广义线性模型的相干推断方法，通过复值时频系数提升计算效率，相比传统替代方法在灵敏度和稳定性上表现更优，适用于大规模EEG/iEEG数据集分析。

Comments 6 pages, 6 figures

Journal ref Journal of Neuroscience Methods 433 (2026): 110791

Early Preconfiguration Failure: A Novel Predictor of the Repetitive Subconcussion

早期预配置失败：重复性亚 concussion 的新型预测指标

Jiajia Li, Zhenzhen Yu, Zhenghao Fu, Guozheng Xu, Jian Song

专题命中 EEG解码 ：EEG(summary_cn,abstract)；cortical(abstract)；分类 q-bio.NC

AI总结 研究通过EEG和视觉注意任务分析，发现早期皮层动态特征可有效区分健康人、重复性亚 concussion 患者和脑创伤患者，揭示毫秒级皮层动态对亚 concussion 诊断的重要性。

Comments 25 pages, 8 figures

Robust Evaluation of Neural Encoding Models via ground-truth approximation

通过地面真实近似方法对神经编码模型进行稳健评估

Giovanni M. Di Liberto

专题命中 EEG解码 ：EEG(summary_cn,abstract)；neural signal(abstract)；分类 q-bio.NC

AI总结 本文提出了一种基于地面真实近似的评估框架，通过皮质相关分析和受试者平均对MEEG信号与预测进行对齐，提高了对刺激相关神经活动的敏感性，从而在合成EEG数据和真实MEEG数据集上显著提升了编码模型的评估效果。

Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition

Pilhyeon Lee, Sunhee Hwang, Jewook Lee, Minjung Shin, Seogkyu Jeon, Hyeran Byun

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

Comments Accepted by the 10th IEEE International Winter Conference on Brain-Computer Interface (BCI 2022). Code is available at https://github.com/DeepBCI/Deep-BCI

Reconsidering Spatial Priors In EEG Source Estimation: Does White Matter Contribute to EEG Rhythms?

PK Douglas, DB Douglas

专题命中 EEG解码 ：EEG(title,abstract)；BCI(abstract,journal_ref)；分类 q-bio.NC；brain-computer interface(journal_ref)

Journal ref 2019 IEEE 7th International Winter Conference on Brain-Computer Interface (BCI), 2019, pp. 1-12,

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

脑电图基础模型对长程时间相关性视而不见：其跨群体脆弱性背后的频谱-时间解离

Marzieh Zare

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

AI总结 研究探讨脑电图基础模型对长程时间相关性的表征及跨群体转移情况。通过探测五种模型，发现它们均未按时间顺序表示LRTC，频谱输入模型能恢复1/f但非DFA，跨群体转移方面各模型表现不一，揭示了模型在这方面的局限性。

Comments I found a computational error in one of the tables that needs to be fixed; and it may take 2-3 weeks

EEG Emotion Recognition From AI-Generated Biodigital Architecture Images

基于人工智能生成的生物数字建筑图像的脑电图情感识别

Hongye Yang, Eva Guttmann-Flury

专题命中 EEG解码 ：EEG(title,abstract)；分类 q-bio.NC、cs.HC

AI总结 该研究利用脑电图数据，通过对人工智能生成的生物数字建筑图像引发的情感反应进行研究，基于现有数据集分析进行相关操作，得出伽马和德尔塔波段准确率高，还明确关键因素与情绪的关联，为建筑设计提供了有价值的见解。

Comments 12 pages, 3 figures; published in the proceedings of SIGraDi 2024

Journal ref Proceedings of the 28th International Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2024): Biodigital Intelligent Systems, 2024, pp. 2443-2454

PaperNet: Efficient Temporal Convolutions and Channel Residual Attention for EEG Epilepsy Detection

PaperNet: 高效时间卷积与通道残差注意机制用于癫痫检测

Md Shahriar Sajid, Abhijit Kumar Ghosh, Fariha Nusrat

机构 * Rajshahi University of Engineering \& Technology, Kazla, Rajshahi-6204, Bangladesh BRAC University, Kha 224 Pragati Sarani, Merul Badda, Dhaka 1212, Bangladesh University of Asia Pacific, 74/A, Green Road, Dhaka-1205, Bangladesh 纠错

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

AI总结 PaperNet通过结合时间卷积、通道残差注意和轻量级循环模块，实现高效脑电图癫痫检测，达到0.96宏F1值。

Comments 15 pages, 4 figures, International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes

基于波形包的可解释脑电生物标志物：低数据量情况下的时空波形字典

Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao, Ajay Thomas, Junseok Park, Matthew J. McGinley, Zhandong Liu

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

AI总结 研究针对脑电分析难题，提出波形包框架，通过学习波形模板字典及扩展表示，在三个数据集上测试，能在低数据量下以少参数实现与先进模型竞争的性能，且具有完全可解释性。

Comments In Review

Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark

脑电到文本在现实场景中可行吗？使用受神经心理学启发的基准进行深入分析

Zihan Zhang, Yu Bao, Xiao Ding, Tianyi Jiang, Kai Xiong

机构 * Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology（哈尔滨工业大学社会计算与交互机器人研究中心） ； Shanghai Innovation Institute（上海创新院） ； State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室） ； Zhongguancun Laboratory（中关村实验室） 纠错

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

AI总结 探讨脑电到文本在现实场景的可行性，利用受神经心理学启发的范式，发现现有基准忽略脑电不稳定性问题。通过实验为无教师强制的EEG2Text解码提供证据，构建COFETT基准，可区分模型性能，实现稳健评估，推动EEG2Text实际应用。

Comments 17 pages, 8 figures. Published in Proceedings of ACL 2026 Main Conference

Journal ref Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers (2026), 1378-1393

The RG-Flow Transformer: Encoding Scale-Free Dynamics in Scarce EEG

用于稀缺神经数据的尺度感知注意力：基于睡眠脑电信号的RG流变压器

Dibakar Sigdel

机构 * Mindverse Computing LLC（Mindverse计算有限责任公司） 纠错

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

AI总结 研究在稀缺脑电数据上比较具有RG归纳偏差的RG流变压器和普通变压器，通过睡眠脑电语料库实验发现二者在睡眠分期准确率上无显著差异，但RG流变压器具有可解释性，能在样本外恢复连续谱指数。

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

DiffEEG：用于学习脑电通用表示的自监督去噪扩散模型

Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri, Belkacem Chikhaoui, Khadidja Henni

机构 * Lebanese American University（黎巴嫩美国大学） ； Institute of Applied Artificial Intelligence, TÉLUQ University（应用人工智能研究所，泰鲁克大学） 纠错

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

AI总结 研究针对基于脑电的癫痫检测面临的注释稀缺和类别不平衡问题，提出DiffEEG自监督基础模型，通过去噪扩散预训练和强化学习微调学习通用神经表示，在癫痫检测中取得较好效果，证明该方法能以最少标记数据实现临床可部署监测。

Comments 19 pages, 6 figures