{"ID":2862430,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25667","arxiv_id":"2509.25667","title":"EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.25667","url_pdf":"https://arxiv.org/pdf/2509.25667v2","authors":"[\"Bipul Thapa\",\"Biplov Paneru\",\"Bishwash Paneru\",\"Khem Narayan Poudyal\"]","published":"2025-09-30T02:06:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.HC\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}