Categorization of motor imagery EEG signal using optimal feature selection based multi-scale attention model

Brain Computer Interface (BCI) technology is presented for improving the quality of life for individuals with physical impairments. It is based on different physiological sensors, among which Electroencephalography (EEG) is exploited for capturing and interpreting brain activity. In spite of its benefits, traditional EEG based classification models suffer from high computational complexity and limited accuracy. Accurate classification of Motor Imagery (MI) EEG signals is major for developing robust and automated BCI systems. This work presents a Deep Learning (DL) model that integrates a Convolutional Neural Network (CNN) with a Multi-Scale Attention (MSA) network which provides better EEG signal classification. Initially, the Multiscale Principal Component Analysis (MSPCA) is exploited for pre-processing the noise signals. Then, the Beluga Whale Optimization (BWO) is presented for selecting optimal features. The proposed model considers a MSA-CNN, which combines parallel convolutional layers with varying kernel sizes and a Squeeze-and-Excitation (SE) based attention mechanism for extracting discriminative features. The suggested model is evaluated by the PhysioNet EEG MI dataset, with outcomes highlighting superior classification performance compared to existing methods and achieved better accuracies of 99.1% on PhysioNet and 99.02% on BCI Competition IV-2a. This hybrid model offered a scalable and efficient solution for real-time MI-EEG classification in BCI applications.