Enhancing Motor Imagery Electroencephalography Classification with a Correlation-Optimized Weighted Stacking Ensemble Model

Enhancing Motor Imagery Electroencephalography Classification with a Correlation-Optimized Weighted Stacking Ensemble Model

In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to i...

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Bibliographic Details
Main Authors: Hossein Ahmadi, Luca Mesin
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Electronics
Subjects:
brain–computer interface
stacked ensemble models
ensemble learning
weighted stacking ensemble
time series cross-validation
EEG signal processing
Online Access:https://www.mdpi.com/2079-9292/13/6/1033

Internet

https://www.mdpi.com/2079-9292/13/6/1033

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