A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials

By Masaki Nakanishi, Yu-Te Wang, Tzyy-Ping Jung, Dezhong Yao. PLoS ONE, 2015

352 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5f023f5a2c385d09bb9faf5b2cb502307316435f

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, methodology): “1) Extended CCA-Based Method: Our recent studies proposed an extended CCA-based method, which incorporated individual template signals obtained by averaging multiple training trials as χ̄njk = 1 Nt ∑Nt h=1 χnjkh [4], [9], [18], [19].”