  Lack of adequate training samples and noisy high-dimensional features are key
challenges faced by Motor Imagery (MI) decoding algorithms for
electroencephalogram (EEG) based Brain-Computer Interface (BCI). To address
these challenges, inspired from neuro-physiological signatures of MI, this
paper proposes a novel Filter-Bank Convolutional Network (FBCNet) for MI
classification. FBCNet employs a multi-view data representation followed by
spatial filtering to extract spectro-spatially discriminative features. This
multistage approach enables efficient training of the network even when limited
training data is available. More significantly, in FBCNet, we propose a novel
Variance layer that effectively aggregates the EEG time-domain information.
With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm
on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the
OpenBMI dataset, and two large datasets from chronic stroke patients. The
results show that, by achieving 76.20% 4-class classification accuracy, FBCNet
sets a new SOTA for BCIC-IV-2a dataset. On the other three datasets, FBCNet
yields up to 8% higher binary classification accuracies. Additionally, using
explainable AI techniques we present one of the first reports about the
differences in discriminative EEG features between healthy subjects and stroke
patients. Also, the FBCNet source code is available at
https://github.com/ravikiran-mane/FBCNet.
