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Ensemble deep learning for automated visual classification using EEG signals
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DOI：10.1016/j.patcog.2019.107147.png)
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摘要
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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.
Keyword:
Ensemble deep learning
Bagging algorithm
EEG
Automated visual classification
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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