The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
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）
Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI
Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑认知与脑智能技术重点实验室）；School of Future Technology, University of Chinese Academy of Sciences (UCAS)（中国科学院大学未来技术学院）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院教育部智能感知与图像理解重点实验室）
Leveraging Foundation Models for Calibration-Free c-VEP BCIs
Department of Biomedical Engineering, University of Calgary（生物医学工程系，卡尔加里大学）；Department of Electrical and Computer Engineering, Queen’s University（电气与计算机工程系，皇后大学）；Department of Pediatric and Clinical Neuroscience, University of Calgary（儿科与临床神经科学系，卡尔加里大学）；Department of Electrical and Software Engineering, University of Calgary（电气与软件工程系，卡尔加里大学）
Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding
Institute of Signal Processing and System Theory, University of Stuttgart（信号处理与系统理论研究所，斯图加特大学）；Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité - Universitätsmedizin Berlin（临床神经技术实验室，精神病学与神经科学系，Charité-柏林大学医学院）
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Huazhong University of Science and Technology（华中科技大学）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）；Zhengzhou University（郑州大学）；School of Electrical and Information Engineering（电气工程学院）；School of Civil and Hydraulic Engineering（土木工程与水利工程学院）
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
Huazhong University of Science and Technology（华中科技大学）；Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）；China Electronic System Technology Co., Ltd.（中国电子系统技术有限公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology（华中科技大学土木与水利工程学院）
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）
Tsinghua University（清华大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院 北京协和医学院）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）
