EEG Based BCI for Intelligent Wheelchair Control System Using Deep Learning

This research study presents the design of an Electroencephalography (EEG) based Brain Computer Interface (BCI) for intelligent wheelchair control to assist patients with mobility disorders. The concept of this research is to enable a direct communication link between the human brain and the machine without physical movement. This study used the BCI Competition IV 2a dataset, which contains EEG recordings of nine subjects performing four motor imagery (MI) tasks that were mapped to wheelchair navigation commands such as turning left, right, moving forward, and stopping. In this study, a deep learning architecture, TCFormer (Temporal Convolutional Transformer), was implemented to learn the spatial and temporal correlations between EEG channels. A lightweight Fusion Head module was added to enhance performance. It consisted of one-dimensional convolution and adaptive pooling operations for improved local temporal feature extraction. The proposed TCFormer-Fusion model achieved an overall classification accuracy of 75%, outperforming the baseline TCFormer model by 72%. Overall, this research study demonstrates that transformer-based models can learn complex EEG signal representations for motor imagery classification. The proposed model contributes toward developing an intelligent wheelchair control system that operates on brain signals, reducing external assistance. This work, with further optimization and real-time implementation, can contribute significantly to the assistive technology and human-computer interaction fields.