Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis

By Yu Zhang, Guoxu Zhou, Jing Jin, Xingyu Wang, Andrzej S. Cichocki. International Journal of Neural Systems, 2013

362 citations recorded by the source corpus.

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/92f7c1eae203ce0e655ea2121a4486b51324c58d

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Citations in the corpus

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, background): “Zhang et al. proposed the multi-way CCA (MwayCCA) and the L1-reguralized MwayCCA (L1-MCCA) methods to optimize sine-cosine reference signals based on multiple standard CCA processes with the calibration data [15], [16].”
A High-Speed Brain Speller using steady-State Visual evoked potentials (2014, background): “26, 27, 28 This study aims to explore the communication speed limit of an SSVEP-based BCI.”