Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition

TL;DR

This paper introduces HTCCA, a hybrid template canonical correlation analysis method that enhances SSVEP recognition in BCI systems, especially under data-limited conditions, by combining multi-subject training data.

Contribution

The study proposes a novel HTCCA algorithm that improves SSVEP detection accuracy and information transfer rate with limited trials, outperforming existing methods.

Findings

HTCCA outperforms compared methods in accuracy and ITR.

Effective with small numbers of trials.

Demonstrated on public benchmark datasets.

Abstract

In this study, an advanced CCA-based algorithn called hybrid template canonical correlation analysis (HTCCA) was proposed to improve the performance of brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) uuder data-linited condition. The HTCCA method combines the training data from several subjects to construct SSVEP templates. The experinental results evaluated on two public benchmark datasets showed that the proposed method outperforms the compared methods in both detection accuracy and information transfer rate when the number of tuials is small.Considering that user-friendly experience will become a key factor for BCI in practical application, it is very necessary to develop effective methods based on limited EEG samples. This study demonstrates that the proposed method has great potential in the application of SSVEP-based brain-computer interfaces.