Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

• Published in: Journal of neural engineering Vol. 12; no. 4; pp. 46008 - 46021
• Main Authors: Chen, Xiaogang, Wang, Yijun, Gao, Shangkai, Jung, Tzyy-Ping, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2015
• Subjects: Adult; Algorithms; brain-computer interface; Brain-Computer Interfaces; canonical correlation analysis; Classification; Communication Aids for Disabled; Correlation analysis; Data Interpretation, Statistical; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Female; filter bank; Filter banks; Frequency bands; Harmonics; High speed; Human-computer interface; Humans; Machine Learning; Male; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Statistics as Topic; steady-state visual evoked potentials; Visual Cortex - physiology; Visual Perception - physiology; Word Processing - methods
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2560/12/4/046008

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ∼33.3 characters min, the online BCI speller obtained an average ITR of 151.18 20.34 bits min−1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.