Frequency recognition of steady-state visually evoked potentials using binary subband canonical correlation analysis with reduced dimension of reference signals
Md. Rabiul Islam, Toshihisa Tanaka, Masaki Nakanishi, Md. Khademul Islam Molla
Abstract
This paper presents a frequency recognition method of steady-state visual evoked potentials (SSVEPs) using binary subbands with canonical correlation analysis (CCA). The first subband contains all the target frequencies of SSVEPs. The second one includes the SSVEP signal corresponding to a desired number of higher order stimulus frequencies, which is obtained by filtering out of required range of lower order stimuli. The full dimension of artificial reference signals are used for first subband, whereas a reduced dimension of references is employed for second subband to compute canonical correlation. The weighted sum of the obtained correlation values are used to recognize the frequency of an SSVEP. The experimental results show the superiority of the proposed method compared to the state-of-the-art recognition methods.
BibTeX
@inproceedings{icassp2016_frequencyrecogni,
title = {Frequency recognition of steady-state visually evoked potentials using binary subband canonical correlation analysis with reduced dimension of reference signals},
author = {Md. Rabiul Islam and Toshihisa Tanaka and Masaki Nakanishi and Md. Khademul Islam Molla},
booktitle = {ICASSP 2016},
year = {2016}
}