Merged CNNs for the classification of EEG motor imagery signals

The using of Electroencephalography (EEG) signals for motor imagery (MI) has recently gained significant attention due to their remarkable ability to detect an individual’s intention to perform specific actions. MI signals have proven useful in enabling individuals with disabilities to control devices such as wheelchairs through neural commands, and have even expanded into applications like autonomous driving. Therefore, ensuring accurate classification of MI tasks from EEG signals is crucial for the development of a reliable Brain-Computer Interface (BCI) system. This article introduces a novel approach to classifying MI tasks using Deep Learning (DL) techniques. The proposed methodology encompasses several steps, including data preprocessing, feature extraction using Common Spatial Pattern (CSP) and Wavelet Packet Decomposition (WPD), and the evaluation of four distinct classifiers. These classifiers involve combinations of two, three, four, and five Convolutional Neural Networks (CNNs). Empirical evaluations highlight the effectiveness of employing five CNNs, which yield the most favorable results. Our approach demonstrates promising performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the method achieves accuracy, precision, recall, and F1 score values of 64.75%, 64.94%, 65.63%, and 64.13%, respectively, indicating its potential to enhance the accuracy of MI task classification within EEG signals.