A Novel CNN With Sliding Window Technique for Enhanced Classification of MI-EEG Sensor Data

The major challenge in fully using the motor imagery (MI)-based brain-computer interface (MI-BCI) capabilities is accurately classifying the MI electroencephalography (MI-EEG) signals. Despite numerous advancements in signal processing and deep learning (DL) techniques, there is significant scope for improvement in the accuracy currently available in the state-of-the-art. This can be achieved by leveraging spatial and temporal features of MI-EEG signal. We propose SWCNet, a convolutional neural network (CNN)-based model, and integrate it with the sliding window technique to increase the accuracy. In this work, a new CNN architecture has been proposed to extract more features from data, whereas the sliding window technique enhances temporal features by augmenting the input sensor data along the temporal dimension. We have thoroughly evaluated the performance of SWCNet using subject-dependent and subject-independent approaches for four different datasets. Our analysis includes general accuracy metrics, an ablation study, a parametric sensitivity study, and a detailed classwise performance evaluation for the tongue, foot, left-hand, and right-hand movements. The proposed model achieves accuracies of 97.42%, 94.46%, 92.27%, and 90.82% for the BCI Competition IV-2a (BCIC-IV-2a), BCI Competition IV-2b (BCIC-IV-2b), High Gamma, and OpenBMI datasets, respectively. SWCNet outperforms the state-of-the-art methods with higher accuracy for all the datasets, demonstrating its superior generalizability. SWCNet holds promise in enhancing the effectiveness of BCI applications, especially in medical rehabilitation.