Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance. (arXiv:2209.03789v1 [eess.SP])

In brain-computer interfaces (BCI) research, recording data is time-consuming
and expensive, which limits access to big datasets. This may influence the BCI
system performance as machine learning methods depend strongly on the training
dataset size. Important questions arise: taking into account neuronal signal
characteristics (e.g., non-stationarity), can we achieve higher decoding
performance with more data to train decoders? What is the perspective for
further improvement with time in the case of long-term BCI studies? In this
study, we investigated the …

arxiv bci dataset deep learning impact long-term performance