The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, approaches based on transfer learning (TL) have recently emerged. However, many BCI paradigms are limited in that they consist of a structure that shows labels first and then measures "imagery", the negative effects of source subjects containing data that do not contain control signals have been ignored in many cases of the subject-to-subject TL process. The main purpose of this paper is to propose a method of excluding subjects that are expected to have a negative impact on subject-to-subject TL training, which generally uses data from as many subjects as possible. In this paper, we proposed a BCI framework using only high-confidence subjects for TL training. In our framework, a deep neural network selects useful subjects for the TL process and excludes noisy subjects, using a co-teaching algorithm based on the small-loss trick. We experimented with leave-one-subject-out validation on two public datasets (2020 international BCI competition track 4 and OpenBMI dataset). Our experimental results showed that confidence-aware TL, which selects subjects with small loss instances, improves the generalization performance of BCI.
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, 430074 China（教育部长图像处理与智能控制重点实验室，华中科技大学，武汉，430074中国）；Zhongguancun Academy, Beijing, 100084 China（中关村学院，北京，100084中国）
University of Hong Kong（香港大学）；Shenzhen Loop Area Institute（深圳河套学院）；Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harvard University（哈佛大学）；Columbia University（哥伦比亚大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
Carnegie Mellon University（卡内基梅隆大学）；Kyung Hee University（Kyung Hee大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of Pittsburgh（匹兹堡大学）
Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation
School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；Chongqing Institute for Brain and Intelligence（重庆脑与智能研究院）；Guangyang Bay Laboratory（广阳湾实验室）；King Abdullah II School of Information Technology, The University of Jordan（约旦大学阿卜杜拉二世信息技术学院）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Computing, SRM University（SRM大学计算机学院）；School of Engineering, The Amity University（阿米提大学工程学院）；School of Aeronautics, Northwestern Polytechnical University（西北工业大学航空学院）
Weighing votes in human-machine collaboration for hazard recognition: Inferring hazard perceptual threshold and decision confidence from electroencephalogram wavelets
