Feature Reweighting for EEG-based Motor Imagery Classification
Taveena Lotey Prateek Keserwani Debi Prosad Dogra Partha Pratim Roy
Classification of motor imagery (MI) using non-invasive
electroencephalographic (EEG) signals is a critical objective as it is used to
predict the intention of limb movements of a subject. In recent research,
convolutional neural network (CNN) based methods have been widely utilized for
MI-EEG classification. The challenges of training neural networks for MI-EEG
signals classification include low signal-to-noise ratio, non-stationarity,
non-linearity, and high complexity of EEG signals. The features computed by
CNN-based networks on the highly noisy MI-EEG signals contain irrelevant
information. Subsequently, the feature maps of the CNN-based network computed
from the noisy and irrelevant features contain irrelevant information. Thus,
many non-contributing features often mislead the neural network training and
degrade the classification performance. Hence, a novel feature reweighting
approach is proposed to address this issue. The proposed method gives a noise
reduction mechanism named feature reweighting module that suppresses irrelevant
temporal and channel feature maps. The feature reweighting module of the
proposed method generates scores that reweight the feature maps to reduce the
impact of irrelevant information. Experimental results show that the proposed
method significantly improved the classification of MI-EEG signals of Physionet
EEG-MMIDB and BCI Competition IV 2a datasets by a margin of 9.34% and 3.82%,
respectively, compared to the state-of-the-art methods.