Feature Reweighting for EEG-based Motor Imagery Classification

by   Taveena Lotey, et al.

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 respectively, compared to the state-of-the-art methods.


Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining

Brain-computer interface (BCI) decodes brain signals to understand user ...

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) si...

Multi-Scale Neural network for EEG Representation Learning in BCI

Recent advances in deep learning have had a methodological and practical...

Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation

Recently, various deep neural networks have been applied to classify ele...

Novel EEG-based BCIs for Elderly Rehabilitation Enhancement

The ageing process may lead to cognitive and physical impairments, which...

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability

EEG-based recognition of activities and states involves the use of prior...

Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps

Binarizing historically degraded as-built drawing (HDAD) maps is a new c...

Please sign up or login with your details

Forgot password? Click here to reset