Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has proven to be an effective paradig...

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Bibliographic Details
Main Authors: Marcos Loaiza-Arias, Andrés Marino Álvarez-Meza, David Cárdenas-Peña, Álvaro Ángel Orozco-Gutierrez, German Castellanos-Dominguez
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
deep learning
explainable models
multimodal analysis
EEG
motor imagery
Online Access:https://www.mdpi.com/2076-3417/14/23/11208
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