🤖 AI Summary
To address insufficient EEG signal decoding accuracy in motor imagery (MI)-based brain–computer interface (BCI) wheelchair control, this study proposes an attention-enhanced hybrid deep learning model integrating bidirectional LSTM and bidirectional GRU (BiLSTM-BiGRU). The architecture effectively captures long-range temporal dependencies in EEG sequences while adaptively amplifying discriminative feature weights via a learnable attention mechanism. Evaluated on standard MI-EEG datasets under rigorous five-fold cross-validation, the model achieves 92.26% single-trial classification accuracy and 90.13% mean cross-validated accuracy—outperforming XGBoost, EEGNet, and Transformer baselines. Furthermore, a real-time visualization simulation interface was implemented using Tkinter, enabling intuitive wheelchair navigation via left/right hand MI commands. This work demonstrates the efficacy and practicality of attention-augmented dual-gated recurrent architectures for lightweight, deployable BCI decoding, offering a novel pathway toward real-world intelligent assistive systems.
📝 Abstract
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.