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RMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.

Neural Netw

January 2025

Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 138632, Singapore. Electronic address:

Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals.

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