L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 21; no. 6; pp. 887 - 896
• Main Authors: Zhang, Yu, Zhou, Guoxu, Jin, Jing, Wang, Minjue, Wang, Xingyu, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Arrays; Brain-computer interface (BCI); Brain-Computer Interfaces; Correlation; Correlation analysis; Data Interpretation, Statistical; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Harmonic analysis; Human-computer interface; Humans; l1-regularization; Male; multiway canonical correlation analysis (MCCA); Optimization; Pattern Recognition, Automated - methods; Recognition; Reference signals; Rehabilitation; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic; steady-state visual evoked potential (SSVEP); Tensile stress; Vectors; Young Adult

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of...