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

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 contribu...

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Main Authors:	Cui, Jian, Yuan, Liqiang, Wang, Zhaoxiang, Li, Ruilin, Jiang, Tianzi
Other Authors:	School of Electrical and Electronic Engineering
Format:	Article
Language:	English
Published:	2023
Subjects:	Engineering::Computer science and engineering Brain-computer Interface Convolutional Neural Network
Online Access:	https://hdl.handle.net/10356/171575
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1715752023-11-03T15:41:10Z Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces Cui, Jian Yuan, Liqiang Wang, Zhaoxiang Li, Ruilin Jiang, Tianzi School of Electrical and Electronic Engineering Engineering::Computer science and engineering Brain-computer Interface Convolutional Neural Network 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. Published version This work was partially supported by Key Research Project of Zhejiang Lab, China (No. 2022KI0AC02) and Youth Foundation Project of Zhejiang Lab, China (No. K2023KI0AA01). 2023-10-31T03:35:20Z 2023-10-31T03:35:20Z 2023 Journal Article Cui, J., Yuan, L., Wang, Z., Li, R. & Jiang, T. (2023). Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces. Frontiers in Computational Neuroscience, 17, 1232925-. https://dx.doi.org/10.3389/fncom.2023.1232925 1662-5188 https://hdl.handle.net/10356/171575 10.3389/fncom.2023.1232925 37663037 2-s2.0-85169550798 17 1232925 en Frontiers in Computational Neuroscience © 2023 The Author(s). 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. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering Brain-computer Interface Convolutional Neural Network
spellingShingle	Engineering::Computer science and engineering Brain-computer Interface Convolutional Neural Network Cui, Jian Yuan, Liqiang Wang, Zhaoxiang Li, Ruilin Jiang, Tianzi Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
description	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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Cui, Jian Yuan, Liqiang Wang, Zhaoxiang Li, Ruilin Jiang, Tianzi
format	Article
author	Cui, Jian Yuan, Liqiang Wang, Zhaoxiang Li, Ruilin Jiang, Tianzi
author_sort	Cui, Jian
title	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_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_sort	towards best practice of interpreting deep learning models for eeg-based brain computer interfaces
publishDate	2023
url	https://hdl.handle.net/10356/171575
_version_	1781793920800260096

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

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