Convolutional denoising autoencoder based SSVEP signal enhancement to SSVEP-based BCIs

Chia Chun Chuang, Chien Ching Lee, Chia Hong Yeng, Edmund Cheung So, Bor Shyh Lin, Yeou Jiunn Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

For steady state visually evoked potential (SSVEP) based brain computer interfaces (BCIs), the elicited SSVEP signals always contain noises and then the performance of SSVEP-based BCIs would be greatly degraded in practical applications. Therefore, to develop an SSVEP signal enhancement would be able to increase the accuracy of SSVEP-based BCIs. In this study, a convolutional denoising autoencoder based SSVEP signal enhancement is proposed to suppress the noise components. The convolutional denoising autoencoder is applied to estimate and suppress the noise components. To effectively estimate the noise components, a sinusoid wave is designed as an ideal SSVEP signal. To ignore the effects of phase, cross correlation is adopted to estimate the phase in the training stage. The experimental results evaluated by using signal-to-noise ratio and canonical correspondence analysis showed that the proposed approaches can effectively suppress the noises components. Therefore, the proposed approach can be applied to develop robust SSVEP-based BCIs.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalMicrosystem Technologies
Early online date14 Oct 2019
DOIs
StateE-pub ahead of print - 14 Oct 2019

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