Ensemble deep learning for automated visual classification using EEG signals

作     者：Zheng, Xiao Chen, Wanzhong You, Yang Jiang, Yun Li, Mingyang Zhang, Tao 

作者机构：Jilin Univ Coll Commun Engn Ren Min St 5988 Changchun 130012 Peoples R China 

出 版 物：《PATTERN RECOGNITION》 (图形识别)

年 卷 期：2020年第102卷第0期

页      面：107147-000页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Department of Science and Technology of Jilin Province, China [20190302034GX] Fundamental Research Funds for the Central Universities of China 

主　　题：Ensemble deep learning Bagging algorithm EEG Automated visual classification 

摘      要：This paper proposes an automated visual classification framework in which a novel analysis method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the improvement of classification performance. The method, called LSTMS-B, combines deep learning and ensemble learning to extract the category-dependent representations of EEG signals. Specifically, it introduces Swish activation function into traditional LSTM which reduces the effect of vanishing gradient and optimize the training process. Besides, the Bagging theory is applied to increase the generalization. The LSTMS-B method reaches the average precision of 97.13% for learning EEG visual presentations, which greatly outperforms traditional LSTM network and other contrast models. Then, to verify its application value, a ResNet-based regression is trained using original images and relevant EEG representations learned before. We use the output of the regression as the features to classify the images, and finally obtain the average classification accuracy of 90.16%. (C) 2019 Elsevier Ltd. All rights reserved.