Accepted by the 11th IEEE International Winter Conference on Brain-Computer Interface (BCI 2023). Code is available at https://github.com/DeepBCI/Deep-BCI
This paper focuses on subject adaptation for EEG-based visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited, by transferring knowledge from abundant data of source subjects. Existing approaches consider the scenario that samples of source subjects are accessible during training. However, it is often infeasible and problematic to access personal biological data like EEG signals due to privacy issues. In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation. To tackle this challenging problem, we propose classifier-based data generation to simulate EEG samples from source subjects using classifier responses. Using the generated samples and target subject data, we perform subject-independent feature learning to exploit the common knowledge shared across different subjects. Notably, our framework is generalizable and can adopt any subject-independent learning method. In the experiments on the EEG-ImageNet40 benchmark, our model brings consistent improvements regardless of the choice of subject-independent learning. Also, our method shows promising performance, recording top-1 test accuracy of 74.6% under the 5-shot setting even without relying on source data. Our code can be found at https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Source_Free_Subject_Adaptation_for_EEG.
Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval
School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications, Hangzhou, Zhejiang（浙江省脑机协同智能技术与应用重点实验室，杭州，浙江）；College of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；College of Artifical Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；Brainnetcome Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网络中心）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计）
EEGReXferNet: A Lightweight Gen-AI Framework for EEG Subspace Reconstruction via Cross-Subject Transfer Learning and Channel-Aware Embedding
IUCRC BRAIN Center, Cullen College of Engineering, University of Houston（IUCRC BRAIN中心、工程学院、休斯顿大学）；Department of Computer Engineering, Purdue University（计算机工程系、普渡大学）
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
Nara Institute of Science and Technology (NAIST)（奈良科学技术大学）；Department of Dynamic Brain Imaging, ATR（动态脑成像部门，ATR）；Center for Advanced Intelligence Project, RIKEN（先进智能项目中心，RIKEN）
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能项目中心）；Advanced Telecommunications Research Institute International (ATR)（国际电气通信基础技术研究所）
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
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）
An Enhanced Source-Free Unsupervised Domain Adaptation Framework for Cross-Dataset EEG Emotion Recognition via Predictive Coding and Test-Time Training
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
University Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；TAU Inria（特拉维夫大学-法国国家信息与自动化研究所联合机构）；LISN-CNRS（法国国家科学研究中心LISN实验室）；Roche Pharma Research and Early Development（罗氏制药研发部）；Roche Innovation Center Basel（罗氏创新中心巴塞尔分部）；F. Hoffmann–La Roche Ltd（罗氏控股有限公司）
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition
Shenzhen University（深圳大学）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；City University of Hong Kong（香港城市大学）；Hong Kong Center for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程中心）
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；Université Paris-Saclay（巴黎萨克雷大学）；TAU（特拉维夫大学）；LISN-CNRS（法国国家科学研究中心南巴黎大学实验室）；Roche Pharma Research and Early Development（罗氏制药研发与早期研发部）；Neuroscience and Rare Diseases（神经科学与罕见病部）；Roche Innovation Center Basel（罗氏巴塞尔创新中心）；F. Hoffmann–La Roche Ltd.（霍夫曼-拉罗什有限公司）
FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification
Department of Biomedical Informatics, Emory University（埃默里大学生物医学信息学系）；Ritchie Centre, Hudson Institute of Medical Research（哈德逊医学研究所里奇中心）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Department of Obstetrics and Gynaecology, Monash University（莫纳什大学妇产科学系）；Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）
School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；Institute of Computing and Intelligence, Harbin Institute of Technology（哈尔滨工业大学计算与智能研究院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Mental Health Center, Shenzhen Kangning Hospital（深圳市精神卫生中心（深圳市康宁医院））
