Blending CNNs with different signal lengths for real-time EEG classification sensitive to the changes

Takashi Ota; Kenji Funahashi

doi:10.1117/12.2624198

30 April 2022 Blending CNNs with different signal lengths for real-time EEG classification sensitive to the changes

Takashi Ota, Kenji Funahashi

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121771Z (2022) https://doi.org/10.1117/12.2624198
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Although a lot of BMI research using CNN has been performed, CNN’s response to changes in the input EEG is too late to proceed in real-time. We propose a method to improve the real-time performance by blending multiple CNNs with different input signal length. The proposed method generates a classifier which has the advantage of a classifier with short input signal length, i.e., fast response to changes in the input signal, and also the advantage of a classifier with long input signal length, i.e., high classification performance.

Citation Download Citation

Takashi Ota and Kenji Funahashi "Blending CNNs with different signal lengths for real-time EEG classification sensitive to the changes", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121771Z (30 April 2022); https://doi.org/10.1117/12.2624198

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