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

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ID: 320359
2026
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Abstract
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 TFormerEEG, a Transformer-driven deep learning architecture, for motor imagery EEG classification. The model achieves a test accuracy of 93.04% compared with various machine learning baseline models, including XGBoost, EEGNet, and an EEG-Deformer model. The TFormerEEG achieved a mean accuracy of 91.18% through stratified cross-validation, showcasing the effectiveness of this model.
Reference Key
openalex_W7167854284 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors B Thapa, Biplov Paneru, Bishwash Paneru, Khem N. Poudyal
Journal Biology Methods and Protocols
Year 2026
DOI
10.1093/biomethods/bpag039
URL
https://doi.org/10.1093/biomethods/bpag039
Keywords Keywords not found

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