EEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Machine Learning Mechanism with Right and Left Voluntary Hand Movement

• URL: http://arxiv.org/abs/2410.09763v1
• Date: Sun, 13 Oct 2024 07:41:37 GMT
• Title: EEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Machine Learning Mechanism with Right and Left Voluntary Hand Movement
• Authors: Biplov Paneru, Bishwash Paneru, Khem Narayan Poudyal,
• Abstract summary: The system is designed to simulate wheelchair navigation based on voluntary right and left-hand movements. Various machine learning models, including Support Vector Machines (SVM), XGBoost, random forest, and a Bi-directional Long Short-Term Memory (Bi-LSTM) attention-based model, were developed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a voluntary Right Left Hand Movement mechanism for control. The system is designed to simulate wheelchair navigation based on voluntary 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 200Hz in the laboratory experiment. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. Various machine learning models, including Support Vector Machines (SVM), XGBoost, random forest, and a Bi-directional Long Short-Term Memory (Bi-LSTM) attention-based model, were developed. The random forest model obtained 79% accuracy. Great performance was seen on the Logistic Regression model which outperforms other models with 92% accuracy and 91% accuracy on the Multi-Layer Perceptron (MLP) model. The Bi-LSTM attention-based model achieved a mean accuracy of 86% through cross-validation, showcasing the potential of attention mechanisms in BCI applications.

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