A Strong and Simple Deep Learning Baseline for BCI MI Decoding

Understand

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.

• Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature.
• We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches.
• EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time.