EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917402399211520 |
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| author | Thapa, Bipul Paneru, Biplov Paneru, Bishwash Poudyal, Khem Narayan |
| author_facet | Thapa, Bipul Paneru, Biplov Paneru, Bishwash Poudyal, Khem Narayan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface Thapa, Bipul Paneru, Biplov Paneru, Bishwash Poudyal, Khem Narayan Machine Learning Artificial Intelligence Human-Computer Interaction 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. |
| title | EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.25667 |