Classification of the four‐class motor imagery signals using continuous wavelet transform filter bank‐based two‐dimensional images

Abstract

AbstractThe feature extraction technique plays a vital role in obtaining better classification accuracy. In this paper, a novel framework is proposed, which develops two‐dimensional (2D) images for convolutional neural network (CNN) to classify four (left hand, right hand, feet, and tongue) MI tasks. 2D image is formed by decomposing each trial using continuous wavelet transform (CWT) filter bank after pre‐processing the MI‐based EEG data by multi‐class common spatial pattern (CSP) method. Obtained images are used to train the CNN model for classification. The proposed framework is evaluated using publicly available BCI competition IV dataset 2a by calculating the classification accuracy for all subjects. Results show that the proposed framework has been giving better classification accuracy than some existing CNN‐based and conventional machine learning‐based approaches compared in this paper. The average time required to train CNN using the proposed framework is 12.67 s, acceptable for online MI‐based BCI applications.