可解释性
计算机科学
人工智能
瓶颈
信息瓶颈法
判别式
特征(语言学)
机器学习
脑电图
对抗制
过程(计算)
深度学习
模式识别(心理学)
心理学
相互信息
操作系统
精神科
哲学
嵌入式系统
语言学
作者
Yanna Zhao,Gaobo Zhang,Yongfeng Zhang,Tiantian Xiao,Ziwei Wang,Fangzhou Xu,Yuanjie Zheng
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2022-07-15
卷期号:19 (4): 046011-046011
被引量:3
标识
DOI:10.1088/1741-2552/ac7d0d
摘要
Abstract Objective. Significant progress has been witnessed in within-subject seizure detection from electroencephalography (EEG) signals. Consequently, more and more works have been shifted from within-subject seizure detection to cross-subject scenarios. However, the progress is hindered by inter-patient variations caused by gender, seizure type, etc. Approach. To tackle this problem, we propose a multi-view cross-object seizure detection model with information bottleneck attribution (IBA). Significance. Feature representations specific to seizures are learned from raw EEG data by adversarial deep learning. Combined with the manually designed discriminative features, the model can detect seizures across different subjects. In addition, we introduce IBA to provide insights into the decision-making of the adversarial learning process, thus enhancing the interpretability of the model. Main results. Extensive experiments are conducted on two benchmark datasets. The experimental results verify the efficacy of the model.
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