FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
This work addresses the problem of improving Motor Imagery classification accuracy for EEG-based Brain-Computer Interfaces, particularly benefiting applications in assistive technology and neurorehabilitation, with incremental advancements in method design.
The paper tackled the challenges of limited training samples and noisy high-dimensional features in Motor Imagery decoding for EEG-based Brain-Computer Interfaces by proposing FBCNet, a Filter-Bank Convolutional Network, which achieved 76.20% 4-class classification accuracy on the BCIC-IV-2a dataset and up to 8% higher binary classification accuracies on other datasets.
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.