Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs

TL;DR

This paper introduces a novel CCA-based method that combines subject-specific and subject-independent data to improve the accuracy and efficiency of SSVEP-based brain-computer interfaces, outperforming existing methods.

Contribution

The study proposes a new CCA-based approach utilizing cross-subject data and an ensemble version to enhance SSVEP BCI performance, surpassing TRCA and extended CCA methods.

Findings

Higher ITR than TRCA and extended CCA

Outperforms in limited training data scenarios

Effective with fewer electrodes and shorter time windows

Abstract

Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfer rate (ITR) and increasing number of targets. However, SSVEP-based methods can be improved in terms of their accuracy and target detection time. We propose a new method based on canonical correlation analysis (CCA) to integrate subject-specific models and subject-independent information and enhance BCI performance. We propose to use training data of other subjects to optimize hyperparameters for CCA-based model of a specific subject. An ensemble version of the proposed method is also developed for a fair comparison with ensemble task-related component analysis (TRCA). The proposed method is compared with TRCA and extended CCA methods. A publicly available, 35-subject SSVEP benchmark dataset is used for comparison studies and performance is quantified by classification accuracy and ITR. The ITR of the proposed method is higher than those of TRCA and extended CCA. The proposed method outperforms extended CCA in all conditions and TRCA for time windows greater than 0.3 s. The proposed method also outperforms TRCA when there are limited training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve performance of SSVEP-based BCIs.