Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance
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 impact of long-term recordings on motor imagery
decoding from two main perspectives: model requirements regarding dataset size
and potential for patient adaptation. We evaluated the multilinear model and
two deep learning (DL) models on a long-term BCI and Tetraplegia NCT02550522
clinical trial dataset containing 43 sessions of ECoG recordings performed with
a tetraplegic patient. In the experiment, a participant executed 3D virtual
hand translation using motor imagery patterns. We designed multiple
computational experiments in which training datasets were increased or
translated to investigate the relationship between models' performance and
different factors influencing recordings. Our analysis showed that adding more
data to the training dataset may not instantly increase performance for
datasets already containing 40 minutes of the signal. DL decoders showed
similar requirements regarding the dataset size compared to the multilinear
model while demonstrating higher decoding performance. Moreover, high decoding
performance was obtained with relatively small datasets recorded later in the
experiment, suggesting motor imagery patterns improvement and patient
adaptation. Finally, we proposed UMAP embeddings and local intrinsic
dimensionality as a way to visualize the data and potentially evaluate data
quality.