| 基于固有时间尺度分解和自适应Huber损失的脑电特征学习模型（英文） |
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| 引用本文： | 杨利军,蒋淑月,魏小鸽,肖运海.基于固有时间尺度分解和自适应Huber损失的脑电特征学习模型（英文）[J].数学季刊,2022(3):281-300. |
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| 作者姓名： | 杨利军 蒋淑月 魏小鸽 肖运海 |
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| 作者单位： | 1. School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University;2. Center for Applied Mathematics of Henan Province, Henan University |
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| 基金项目： | Supported by National Natural Science Foundation of China (Grant Nos. 11701144,11971149)； |
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| 摘 要： | According to the World Health Organization, about 50 million people worldwide suffer from epilepsy. The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG) is a significant research object widely used in diagnosis and treatment of epilepsy. In this paper, an adaptive feature learning model for EEG signals is proposed, which combines Huber loss function with adaptive weight penalty term.Firstly, each EEG signal is decomposed by intrinsic time-scale decomposition. S...
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