EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface

This paper presents an Artificial Intelligence (AI) integrated approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a framework that uses Convolutional Neural Network-Transformer Hybrid Model, named CTHM, for motor imagery EEG classification. The model achieves a test accuracy of 91.73% compared with various machine learning baseline models, including XGBoost, EEGNet, and a transformer-based model. The CTHM achieved a mean accuracy of 90% through stratified cross-validation, showcasing the effectiveness of the CNN-Transformer hybrid architecture in BCI applications.