Enhancing MI EEG Signal Classification With a Novel Weighted and Stacked Adaptive Integrated Ensemble Model: A Multi-Dataset Approach

Abstract

Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) are vital for various applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery (MI) tasks, remains a significant challenge. This study introduces a novel Weighted and Stacked Adaptive Integrated Ensemble Classifier (WS-AIEC), employing a comprehensive approach across six MI EEG datasets with 16 diverse Machine Learning (ML) classifiers. Through evaluations that encompass metric-based comparisons and learning curve analyses, we systematically ranked and clustered the classifiers. The WS-AIEC integrates the top-performing classifiers from each cluster and employs a unique blend of weighted and stacked ensemble techniques. Our results demonstrate the WS-AIEC’s superior performance, achieving an exceptional accuracy of 99.58% on the BNCI2014-002 dataset and an average improvement of 20.23% in accuracy over the top-performing individual classifiers across all datasets. This significant enhancement underscores the innovative approach of our WS-AIEC in EEG signal classification for BCIs, setting a new benchmark for accuracy and reliability in the field.

Keywords

• Brain-computer interface
• stacking ensemble models
• weighted ensemble techniques
• time series cross-validation
• EEG signal processing
• motor imagery EEG classification