Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance.

ABSTRACT:

Introduction

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.

Methods

We evaluated the multilinear model a

SUBMITTER: Sliwowski M 

PROVIDER: S-EPMC10061076 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature