Abstract
Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.
Highlights
Brain-computer interfaces (BCIs) provide humans with a new communication channel between their brains and external devices [1]
Since the dataset used in this study was designed for a synchronous paradigm where resting data are not available, the performance of CCA coefficient (CACC), which has been proposed for an asynchronous BCI, didn’t outperform the standard Canonical correlation analysis (CCA)
Even with few training trials (e.g., Nt = 2 for multi-way CCA (MwayCCA), L1-regularized multi-way CCA (L1-MCCA), multi-set CCA (MsetCCA), and individual template based CCA (IT-CCA); Nt = 1 for Combination Method), the accuracy of the training-based methods was significantly improved over the standard CCA method
Summary
Brain-computer interfaces (BCIs) provide humans with a new communication channel between their brains and external devices [1]. Current applications of the electroencephalogram (EEG)-based BCIs have been hindered by low communication speed [2]. Steady-state visual evoked potentials (SSVEPs)-based BCIs, which show advantages of high information transfer rate (ITR) and little user training, have received increasing attention [3, 4]. In SSVEP-based BCIs, users gaze at one of multiple visual flickers tagged by frequency or phase, resulting in SSVEPs that exhibit the same properties as the target.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
