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Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity
Conference proceeding   Open access

Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity

Juan Manuel Mayor-Torres, Mirco Ravanelli, Sara E. Medina-DeVilliers, Matthew D. Lerner and Giuseppe Riccardi
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), v 2021, pp 412-415
Nov 2021
DOI: https://doi.org/10.1109/EMBC46164.2021.9630427
PMID: 34891321
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://arxiv.org/pdf/2107.10790View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Engineering Engineering, Biomedical Engineering, Electrical & Electronic Science & Technology Technology
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillatory activity. In particular, we propose a novel SincNet-based neural network for detecting emotions in ASD patients using EEG signals. The learned filters can be easily inspected to detect which part of the EEG spectrum is used for predicting emotions. We found that our system automatically learns the high-alpha (9-13 Hz) and beta(13-30 Hz) band suppression often present in individuals with ASD. This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD. The improved interpretability of SincNet is achieved without sacrificing performance in emotion recognition.

Metrics

8 Record Views
20 citations in Web of Science
24 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: SDGs in the Output

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Biomedical
Engineering, Electrical & Electronic
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