Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI

Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI

Abstract Classifying Electroencephalogram (EEG) signals for wheelchair navigation presents significant challenges due to high dimensionality, noise, outliers, and class imbalances. This study proposes an optimized classification framework that evaluates ten machine learning (ML) models, emphasizing...

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Bibliographic Details
Main Authors: Amr M. Hamed, Abdel-Fattah Attia, Heba El-Behery
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:Journal of Big Data
Subjects:
EEG
Brain-computer interface (BCI)
Wheelchair navigation
Machine learning
Ensemble methods
Feature selection
Online Access:https://doi.org/10.1186/s40537-025-01238-y
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https://doi.org/10.1186/s40537-025-01238-y

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