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Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces

INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the...

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Autores principales: Cui, Jian, Yuan, Liqiang, Wang, Zhaoxiang, Li, Ruilin, Jiang, Tianzi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470463/
https://www.ncbi.nlm.nih.gov/pubmed/37663037
http://dx.doi.org/10.3389/fncom.2023.1232925
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author Cui, Jian
Yuan, Liqiang
Wang, Zhaoxiang
Li, Ruilin
Jiang, Tianzi
author_facet Cui, Jian
Yuan, Liqiang
Wang, Zhaoxiang
Li, Ruilin
Jiang, Tianzi
author_sort Cui, Jian
collection PubMed
description INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. METHODS: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. RESULTS: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. DISCUSSION: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.
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spelling pubmed-104704632023-09-01 Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces Cui, Jian Yuan, Liqiang Wang, Zhaoxiang Li, Ruilin Jiang, Tianzi Front Comput Neurosci Neuroscience INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. METHODS: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. RESULTS: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. DISCUSSION: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10470463/ /pubmed/37663037 http://dx.doi.org/10.3389/fncom.2023.1232925 Text en Copyright © 2023 Cui, Yuan, Wang, Li and Jiang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Cui, Jian
Yuan, Liqiang
Wang, Zhaoxiang
Li, Ruilin
Jiang, Tianzi
Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title_full Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title_fullStr Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title_full_unstemmed Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title_short Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
title_sort towards best practice of interpreting deep learning models for eeg-based brain computer interfaces
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470463/
https://www.ncbi.nlm.nih.gov/pubmed/37663037
http://dx.doi.org/10.3389/fncom.2023.1232925
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