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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01238-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332968015790080 |
|---|---|
| author | Amr M. Hamed Abdel-Fattah Attia Heba El-Behery |
| author_facet | Amr M. Hamed Abdel-Fattah Attia Heba El-Behery |
| author_sort | Amr M. Hamed |
| collection | DOAJ |
| description | 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 ensemble methods, feature selection (FS), and outlier utilization. The dataset, comprising 2869 samples and 141 features, was processed using Recursive Feature Elimination (RFE) and correlation thresholds (CTs), achieving a peak accuracy of 69% with Extra Trees after FS. Notably, training on outlier-only data yielded even higher accuracy (Extra Trees: 82%), underscoring the value of outliers in enhancing class separability. Receiver Operating Characteristic–Precision Recall (ROC-PR) curve analysis confirmed that Extra Trees achieved a ROC AUC (Area Under Curve) of 0.92 and PR AUC of 0.82 for the best-classified movement command, while other models exhibited lower precision-recall (PR) balance. This approach, complemented by explainability techniques, offers a robust solution for EEG-based wheelchair control systems and paves the way for interpretable brain-computer interfaces (BCIs). |
| format | Article |
| id | doaj-art-3483fa17b4c24de0b88df81141b1e85c |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-3483fa17b4c24de0b88df81141b1e85c2025-08-20T03:46:03ZengSpringerOpenJournal of Big Data2196-11152025-07-0112113010.1186/s40537-025-01238-yOptimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AIAmr M. Hamed0Abdel-Fattah Attia1Heba El-Behery2Department of Computer Engineering and Systems, Faculty of Engineering, Kafrelsheikh UniversityDepartment of Computer Engineering and Systems, Faculty of Engineering, Kafrelsheikh UniversityDepartment of Computer Engineering and Systems, Faculty of Engineering, Kafrelsheikh UniversityAbstract 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 ensemble methods, feature selection (FS), and outlier utilization. The dataset, comprising 2869 samples and 141 features, was processed using Recursive Feature Elimination (RFE) and correlation thresholds (CTs), achieving a peak accuracy of 69% with Extra Trees after FS. Notably, training on outlier-only data yielded even higher accuracy (Extra Trees: 82%), underscoring the value of outliers in enhancing class separability. Receiver Operating Characteristic–Precision Recall (ROC-PR) curve analysis confirmed that Extra Trees achieved a ROC AUC (Area Under Curve) of 0.92 and PR AUC of 0.82 for the best-classified movement command, while other models exhibited lower precision-recall (PR) balance. This approach, complemented by explainability techniques, offers a robust solution for EEG-based wheelchair control systems and paves the way for interpretable brain-computer interfaces (BCIs).https://doi.org/10.1186/s40537-025-01238-yEEGBrain-computer interface (BCI)Wheelchair navigationMachine learningEnsemble methodsFeature selection |
| spellingShingle | Amr M. Hamed Abdel-Fattah Attia Heba El-Behery Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI Journal of Big Data EEG Brain-computer interface (BCI) Wheelchair navigation Machine learning Ensemble methods Feature selection |
| title | Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI |
| title_full | Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI |
| title_fullStr | Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI |
| title_full_unstemmed | Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI |
| title_short | Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI |
| title_sort | optimization of eeg based wheelchair control machine learning feature selection outlier management and explainable ai |
| topic | EEG Brain-computer interface (BCI) Wheelchair navigation Machine learning Ensemble methods Feature selection |
| url | https://doi.org/10.1186/s40537-025-01238-y |
| work_keys_str_mv | AT amrmhamed optimizationofeegbasedwheelchaircontrolmachinelearningfeatureselectionoutliermanagementandexplainableai AT abdelfattahattia optimizationofeegbasedwheelchaircontrolmachinelearningfeatureselectionoutliermanagementandexplainableai AT hebaelbehery optimizationofeegbasedwheelchaircontrolmachinelearningfeatureselectionoutliermanagementandexplainableai |