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

TL;DR

This paper introduces a novel multiset canonical correlation analysis (MsetCCA) method that optimizes reference signals for SSVEP frequency recognition in BCIs, improving accuracy over traditional CCA methods especially with fewer channels and shorter data windows.

Contribution

The study proposes MsetCCA, a new approach that learns joint spatial filters to enhance SSVEP frequency recognition accuracy by optimizing reference signals based on training EEG data.

Findings

MsetCCA outperforms CCA, MwayCCA, and PCCA in recognition accuracy.

The method is especially effective with limited channels and short data windows.

Experimental results confirm the robustness and superiority of MsetCCA.

Abstract

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 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 ten 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.