Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder
- DOI
- 10.2991/ijcis.2019.125905651How to use a DOI?
- Keywords
- Emotion recognition; Convolutional auto-encoder; Fully connected neural network; EEG signals; EP signals
- Abstract
Emotion recognition is of great significance to computational intelligence systems. In order to improve the accuracy of emotion recognition, electroencephalogram (EEG) signals and external physiological (EP) signals are adopted due to their perfect performance in reflecting the slight variations of emotions, wherein EEG signals consist of multiple channels signals and EP signals consist of multiple types of signals. In this paper, a multimodal emotion recognition method based on convolutional auto-encoder (CAE) is proposed. Firstly, a CAE is designed to obtain the fusion features of multichannel EEG signals and multitype EP signals. Secondly, a fully connected neural network classifier is constructed to achieve emotion recognition. Finally, experiment results show that the proposed method can improve the accuracy of emotion recognition obviously compared with other similar methods.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Jian Zhou AU - Xianwei Wei AU - Chunling Cheng AU - Qidong Yang AU - Qun Li PY - 2018 DA - 2018/11/01 TI - Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder JO - International Journal of Computational Intelligence Systems SP - 351 EP - 358 VL - 12 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2019.125905651 DO - 10.2991/ijcis.2019.125905651 ID - Zhou2018 ER -