Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency

Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed...

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Bibliographic Details
Main Authors: Donghyun Park, Hoonseok Park, Sangyeon Kim, Sanghyun Choo, Sangwon Lee, Chang S. Nam, Jae-Yoon Jung
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Brain–computer interfaces (BCI)
motor imagery (MI)
convolutional neural network (CNN)
electroencephalogram (EEG)
explainable artificial intelligence (XAI)
Online Access:https://ieeexplore.ieee.org/document/10310256/

Internet

https://ieeexplore.ieee.org/document/10310256/

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