2018/10/05 by Kuan-Jung Chiang, Chiang, Kuan-Jung, Chun-Shu Wei +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #cs.HC #cs.LG #eess.SP #electronic engineering #information engineering #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1810.02842
4 pages, 3 figures, 1 table. For NER'19
arxiv created 2019/03/13 · arxiv updated 2021/02/11
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method. Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zero-training/plug-and-play high-speed SSVEP BCIs.