Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features. (arXiv:2210.02544v2 [eess.SP] UPDATED)

In brain signal processing, deep learning (DL) models have become commonly
used. However, the performance gain from using end-to-end DL models compared to
conventional ML approaches is usually significant but moderate, typically at
the cost of increased computational load and deteriorated explainability. The
core idea behind deep learning approaches is scaling the performance with
bigger datasets. However, brain signals are temporal data with a low
signal-to-noise ratio, uncertain labels, and nonstationary data in time. Those
factors may influence the training …

arxiv brain brain-computer interface computer deep learning features