  Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor
images recognized from the electroencephalogram (EEG) to control commands. EEG
patterns of different imagination tasks, e.g. hand and foot movements, are
effectively classified with machine learning techniques using band power
features. Recently, also Convolutional Neural Networks (CNNs) that learn both
effective features and classifiers simultaneously from raw EEG data have been
applied. However, CNNs have two major drawbacks: (i) they have a very large
number of parameters, which thus requires a very large number of training
examples; and (ii) they are not designed to explicitly learn features in the
frequency domain. To overcome these limitations, in this work we introduce
Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass
and depthwise convolutional filters. Experimental results obtained on the
publicly available BCI Competition IV Dataset 2a show that our approach
outperforms reference methods in terms of classification accuracy.
