Multi-information Improves the Performance of CCA-based SSVEP Classification

Overview

Journal Cogn Neurodyn

Publisher Springer

Specialty Neurology

Date 2024 Feb 26

PMID 38406193

Authors

Xiangguo Yin

Mingxing Lin

Affiliations

Soon will be listed here.

Abstract

The target recognition algorithm based on canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. To reduce visual fatigue and improve the information transfer rate (ITR), how to improve the accuracy of algorithms within a short time window has become one of the main problems at present. There were filter bank CCA (FBCCA), individual template CCA (ITCCA), and temporally local CCA (TCCA), which improve the CCA algorithm from different aspects.This paper proposed to consider individual, frequency, and time information at the same time, so as to extract features more effectively. A comparison of the various methods was performed using benchmark dataset. Classification accuracy and ITR were used for performance evaluation. In the different extensions of CCA, the method incorporating the above three kinds of information simultaneously achieved the best performance within a short time window. This study explores the effect of using a variety of information to improve the CCA algorithm.

Citing Articles

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.

Liang L, Zhang Q, Zhou J, Li W, Gao X Sensors (Basel). 2023; 23(14).

PMID: 37514603 PMC: 10385518. DOI: 10.3390/s23146310.

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