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

This paper presents an Artificial Intelligence (AI) integrated novel 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 BiLSTM-BiGRU model that shows a superior test accuracy of 92.26% as compared with various machine learning baseline models, including XGBoost, EEGNet, and a transformer-based model. The Bi-LSTM-BiGRU attention-based model achieved a mean accuracy of 90.13% through cross-validation, showcasing the potential of attention mechanisms in BCI applications.