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.
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Acknowledgements
The current authors gratefully acknowledge the support provided to this study by the Ministry of Science and Technology, Taiwan, Republic of China, under Contract MOST 108-2221-E-218-018-MY2 and Higher Education Sprout Project, Ministry of Education, Taiwan, Republic of China, under Contract 1300-107P735.
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C-CC and C-CL contributed equally to this paper. Y-JC analyzed data and wrote the main paper; B-SL collected all data and provided statistical analysis; C-HY took parts in surveillance of performing the experiment and study design; EC So dedicated his effort to study design and interpretive analysis; C-CL and C-CC designed the study and directed the experiment. All authors discussed the results and implications and commented on the manuscript at all stages.
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Chuang, CC., Lee, CC., Yeng, CH. et al. Convolutional denoising autoencoder based SSVEP signal enhancement to SSVEP-based BCIs. Microsyst Technol 28, 237–244 (2022). https://doi.org/10.1007/s00542-019-04654-2
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DOI: https://doi.org/10.1007/s00542-019-04654-2