计算机科学
脑电图
人工智能
学习迁移
遗忘
分类器(UML)
边距(机器学习)
机器学习
元学习(计算机科学)
特征学习
适应(眼睛)
模式识别(心理学)
特征(语言学)
心理学
认知心理学
哲学
语言学
经济
神经科学
管理
精神科
任务(项目管理)
作者
Tiehang Duan,Mihir Chauhan,Mohammad Abuzar Shaikh,Jun Chu,Sargur N. Srihari
出处
期刊:Cornell University - arXiv
日期:2020-01-01
被引量:11
标识
DOI:10.48550/arxiv.2003.06113
摘要
The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation. We propose an efficient transfer learning method, named Meta UPdate Strategy (MUPS-EEG), for continuous EEG classification across different subjects. The model learns effective representations with meta update which accelerates adaptation on new subject and mitigate forgetting of knowledge on previous subjects at the same time. The proposed mechanism originates from meta learning and works to 1) find feature representation that is broadly suitable for different subjects, 2) maximizes sensitivity of loss function for fast adaptation on new subject. The method can be applied to all deep learning oriented models. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed model, outperforming current state of the arts by a large margin in terms of both adapting on new subject and retain knowledge of learned subjects.
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