脑机接口 / BCI

EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network

Jian Cui, Zirui Lan, Olga Sourina, Wolfgang Müller-Wittig

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

Journal ref IEEE Transactions on Neural Networks and Learning Systems, 2022

Brain-Computer Interfaces and Quantum Robots

Eliano Pessa, Paola zizzi

专题命中 BCI数据与评测 ：brain-computer interface(title,abstract)；BCI(abstract)

Comments 7 pages, contributed paper to the Conference ".mERGERs. Physical and Cognitive Mutations in Humans and Machines", Laval (France), 24-25 April 2009

EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines

Tiago Vasconcelos Afonso, Florian Heinrichs

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

Comments Keywords: Functional data analysis, functional neural networks, EEG data, eye-tracking 18 pages, 2 figures, 9 tables

Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation

Dong-Young Kim, Dong-Kyun Han, Hye-Bin Shin

专题命中 BCI数据与评测 ：brain-computer interface(abstract,comments)；BCI(abstract)；EEG(abstract)；分类 eess.SP、cs.LG、cs.HC

Comments Submitted to 2023 11th IEEE International Winter Conference on Brain-Computer Interface

Temporal Context and Architecture: A Benchmark for Naturalistic EEG Decoding

时间上下文与架构：自然 EEG解码的基准测试

Mehmet Ergezer

机构 * School of Computing \& Data Science, Wentworth Institute of Technology Boston, MA USA Email 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

AI总结 本文通过对比不同架构在自然 EEG解码中的表现，揭示了效率与鲁棒性之间的权衡，S5在参数效率上表现优异，而EEGXF在鲁棒性和保守性上更具优势。

Journal ref ICASSP 2026

Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification

HM Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman, Ahmmad Nur Swapnil, Maoyejatun Hasana, Ahmed Hossain Chowdhury, A. B. M. Alim Al Islam

机构 * Bangladesh University of Engineering and Technology (BUET)（孟加拉工程大学） ； Dhaka Medical College（达卡医学院） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing

Qingzhu Zhang, Jiani Zhong, Zongsheng Li, Xinke Shen, Quanying Liu

机构 * Department of Biomedical Engineering, Southern University of Science and Technology（生物医学工程系，南方科技大学） ； School of Data Science, University of California, San Diego（数据科学学院，加州大学圣地亚哥分校） ； School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（科学与工程学院，香港中文大学（深圳）） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 q-bio.NC、cs.LG

Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces

Osman Berke Guney, Deniz Kucukahmetler, Huseyin Ozkan

机构 * Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA（电气与计算机工程系，波士顿大学） ； Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany（人类认知与脑科学Max Planck研究所，莱比锡，德国） ； Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey（工程与自然科学学院，萨班奇大学，伊斯坦布尔，土耳其） 纠错

专题命中 BCI数据与评测 ：brain-computer interface(title)；BCI(abstract)；分类 eess.SP、cs.LG

Comments 31 pages (including a 7-page appendix); 6 figures (2 in the appendix)

Journal ref Journal of Neural Engineering, Volume 22, Number 5, 2025

Explicit modelling of subject dependency in BCI decoding

Michele Romani, Francesco Paissan, Andrea Fossà, Elisabetta Farella

机构 * University of Trento（特伦托大学） ； University of Bologna（博洛尼亚大学） ； Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会） 纠错

专题命中 BCI数据与评测 ：BCI(title)；brain-computer interface(abstract)；分类 cs.LG、cs.HC

Comments 5 pages, 3 figures, conference paper

BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces

Timothee Robert, MohammadAli Shaeri, Mahsa Shoaran

机构 * Robotics（机器人学） ； Integrated Neurotechnologies Laboratory（整合神经技术实验室） ； EPFL（苏黎世联邦理工学院） 纠错

专题命中 BCI数据与评测 ：brain-computer interface(title,abstract)；分类 q-bio.NC、eess.SP

Comments Accepted for publication in IEEE Neural Engineering (NER) Conference'25

Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data

Lorenzo Veronese, Andrea Moglia, Luca Mainardi, Pietro Cerveri

机构 * Department of Electronics, Information, and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学） ； Università di Pavia（帕维亚大学） 纠错

专题命中 BCI数据与评测 ：neural decoding(title,abstract)；分类 q-bio.NC、cs.LG

Comments 14 pages, 5 figures

Journal ref J. Neural Eng. 22 046048 (2025)

mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup

Xuan-Hao Liu, Bao-Liang Lu, Wei-Long Zheng

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

Comments CogSci 2025 Oral

Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition

Shadi Sartipi, Mujdat Cetin

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning

Dustin Pulver, Prithila Angkan, Paul Hungler, Ali Etemad

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG、cs.HC

Comments This paper has been accepted to the 25th International Conference on Multimodal Interaction (ICMI 2023). 8 pages, 6 figures, 6 tables

A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition

Zhe Wang, Yongxiong Wang, Jiapeng Zhang, Yiheng Tang, Zhiqun Pan

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEG

Gao Xueqi, Xu Chao, Song Yihang, Hu Jing, Xiao Jian, Meng Zhaopeng

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.LG

Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset

M. L. Menezes, A. Samara, L. Galway, A. Sant'anna, A. Verikas, F. Alonso-Fernandez, H. Wang, R. Bond

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP、cs.HC

Comments Published at Personal and Ubiquitous Computing Journal (PUC)

Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces

Zahra Tarkhani, Lorena Qendro, Malachy O'Connor Brown, Oscar Hill, Cecilia Mascolo, Anil Madhavapeddy

专题命中 BCI数据与评测 ：brain-computer interface(title)；BCI(abstract)；分类 cs.LG、cs.HC

Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients

Aleksandar Miladinović, Miloš Ajčević, Pierpaolo Busan, Joanna Jarmolowska, Giulia Silveri, Susanna Mezzarobba, Piero Paolo Battaglini, Agostino Accardo

专题命中 BCI数据与评测 ：BCI(title,abstract)；分类 eess.SP、cs.LG

The Spatial Real and Virtual Sound Stimuli Optimization for the Auditory BCI

Nozomu Nishikawa, Yoshihiro Matsumoto, Shoji Makino, Tomasz M. Rutkowski

专题命中 BCI数据与评测 ：BCI(title,abstract)；分类 q-bio.NC、cs.HC

Comments APSIPA ASC 2012

Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

Andrea Apicella, Pasquale Arpaia, Giovanni D'Errico, Davide Marocco, Giovanna Mastrati, Nicola Moccaldi, Roberto Prevete

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG

Comments This work has been published on Neurocomputing journal in its final version. Please refer to the final Open Access version of the paper on https://doi.org/10.1016/j.neucom.2024.128354 . Old title "Machine Learning Strategies to Improve Generalization in EEG-based Emotion Assessment: a Systematic Review" has been changed to the current one

Journal ref Neurocomputing, 2024

EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation

Lisa Haxel, Jaivardhan Kapoor, Ulf Ziemann, Jakob H. Macke

机构 * Excellence Cluster Machine Learning, University of Tübingen（卓越集群机器学习中心，图宾根大学） ； Tübingen AI Center, University of Tübingen（图宾根人工智能中心，图宾根大学） ； Department of Neurology and Stroke, University of Tübingen（神经学与中风部门，图宾根大学） ； Hertie Institute for Clinical Brain Research, University of Tübingen（赫尔特研究所，图宾根大学） ； Department of Empirical Inference, Max Planck Institute for Intelligent Systems（经验推理部门，智能系统马克斯·普朗克研究所） ； Hertie Institute for AI in Brain Health, Tübingen（赫尔特人工智能与脑健康研究所，图宾根） 纠错

专题命中 BCI数据与评测 ：BCI(abstract)；brain-computer interface(abstract)；neural signal(abstract)；分类 q-bio.NC、cs.LG、cs.HC

Comments Preprint

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

Xia Chen, Xiangbin Teng, Han Chen, Yafeng Pan, Philipp Geyer

专题命中 BCI数据与评测 ：BCI(abstract)；brain-computer interface(abstract)；EEG(abstract)；分类 q-bio.NC、eess.SP、cs.HC

Comments 19 pages, 6 figures

Journal ref Biomedical Signal Processing and Control, 2023

MindBigData 2022 A Large Dataset of Brain Signals

David Vivancos, Felix Cuesta

专题命中 BCI数据与评测 ：BCI(abstract)；brain computer interface(abstract)；EEG(abstract)；分类 q-bio.NC、eess.SP、cs.LG

Comments 15 pages, 10 figures

A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing

用于临床解码的脑电图基础模型压力测试：数据集标识和目标阴性对照

Marzieh Zare

机构 * Université Laval（拉瓦尔大学） ； NeuroGenis Inc.（NeuroGenis公司） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG

AI总结 研究预训练脑电图基础模型在临床解码中的表现，通过多种方法在多数据集多任务上对六个模型进行基准测试，发现模型结论依赖评估单元、数据集偏移等因素，如在韩国痴呆症等任务中不同模型和方法表现各异。

Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition

用于跨数据集基于脑电图的情绪识别的掩码生成对比表示学习

Huqin Weng, Jiayang Huang, Yimin Wen, Jie Du, Chi-Man Vong, Chuangquan Chen

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG

AI总结 提出掩码生成对比表示学习框架MGCRL用于跨数据集脑电图情绪识别，基于区域感知时空编码器，整合生成与对比学习，通过关键设计实现跨数据集泛化的细粒度和全局判别表示。

An Enhanced Source-Free Unsupervised Domain Adaptation Framework for Cross-Dataset EEG Emotion Recognition via Predictive Coding and Test-Time Training

一种增强的无源无监督域适应框架：基于预测编码和测试时训练的跨数据集脑电情感识别

Md Niaz Imtiaz, Naimul Khan

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 eess.SP

AI总结 提出一种无源无监督域适应框架，通过非对比预测编码自监督预训练和双阶段自适应优化（多损失自适应正则化与局部一致性学习），结合轻量测试时训练，解决跨数据集脑电情感识别中的域偏移和噪声伪标签问题。

Comments Under review

EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications

EEG-Bench: 一个用于临床应用中EEG基础模型的基准测试

Ard Kastrati, Josua Bürki, Jonas Lauer, Cheng Xuan, Raffaele Iaquinto, Roger Wattenhofer

机构 * ETH Zurich（苏黎世联邦理工学院） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG

AI总结 EEG-Bench为临床应用中的EEG基础模型提供了一个统一的基准测试框架，评估了11个诊断任务，并展示了简单模型在临床分布偏移下的竞争力。

Comments Foundation Models for the Brain and Body (BrainBodyFM@NeurIPS)

DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition

Fo Hu, Can Wang, Qinxu Zheng, Xusheng Yang, Bin Zhou, Gang Li, Yu Sun, Wen-an Zhang

机构 * College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院） ； Institute of Wenzhou, Zhejiang University（浙江大学温州市研究院） ； College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学学院） ； Key Laboratory for Biomedical Engineering of MOE of China, Department of Biomedical Engineering, Zhejiang University（中国教育部生物医学工程重点实验室，浙江大学生物医学工程学院） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.LG

Comments 14 pages, 9 figures

LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition

Huan Liu, Shusen Yang, Yuzhe Zhang, Mengze Wang, Fanyu Gong, Chengxi Xie, Guanjian Liu, Zejun Liu, Yong-Jin Liu, Bao-Liang Lu, Dalin Zhang

机构 * School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院） ； School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院） ； Joint School of Design and Innovation, Xi’an Jiaotong University（西安交通大学联合设计与创新学院） ； Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系） ； Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系） ； Space Information Research Institute, Hangzhou Dianzi University（杭州电子科技大学空间信息研究所） 纠错

专题命中 BCI数据与评测 ：EEG(title,abstract)；分类 cs.HC