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Apr, 2023
可解释和鲁棒的EEG系统人工智能调查
Interpretable and Robust AI in EEG Systems: A Survey
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Xinliang Zhou, Chenyu Liu, Liming Zhai, Ziyu Jia, Cuntai Guan...
TL;DR
本文第一次全面介绍了解释性与鲁棒性人工智能技术在脑电图系统中的应用及其未解决问题与发展方向。具体而言,我们首先提出了三种类别的可解释性分类方法:反向传播,扰动和内在可解释性方法,并将鲁棒性机制分类为噪声和伪迹、人类变异、数据获取不稳定性和对抗攻击四类.
Abstract
The close coupling of
artificial intelligence
(AI) and
electroencephalography
(EEG) has substantially advanced
human-computer interaction
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