EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach

Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG),offer promising solutions for assisting individuals with motor disabilities. However, challengesin reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persistdue to the complexity and variability of brain activity. Current EEG-based BMIs facelimitations in adaptability, usability, and robustness, especially in applications like virtualkeyboards, as traditional machine-learning models struggle to handle high-dimensional EEGdata effectively. To address these gaps, we developed an EEG-based BMI system capable ofaccurately identifying voluntary keystrokes, specifically leveraging right and left voluntaryhand movements. Using a publicly available EEG dataset, the signals were pre-processed withband-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential(ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state(0), 'd' key press (1), and 'l' key press (2). Our approach employs a hybrid neural networkarchitecture with BiGRU-Attention as the proposed model for interpreting EEG signals,achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support VectorMachines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers,CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integratedinto a real-time graphical user interface (GUI) to simulate and predict keystrokes from brainactivity. Our work demonstrates how deep learning can advance EEG-based BMI systems byaddressing the challenges of signal interpretation and classification. By providing acomparative analysis of multiple models and implementing a real-time application, thisresearch highlights the feasibility and reliability of BMIs in improving accessibility forindividuals with motor impairments.