Journal
SENSORS
Volume 19, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s19092212
Keywords
EEG signal; feature extraction; multiband feature matrix; deep learning; CapsNet; emotion recognition
Funding
- National Natural Science Foundation of China [61502150, 61403128]
- Foundation for University Key Teacher by Henan Province [2015GGJS068]
- Fundamental Research Funds for the Universities of Henan Province [NSFRF1616]
- Foundation for Scientific and Technological Project of Henan Province [172102210279]
- Key Scientific Research Projects of Universities in Henan [19A520004]
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Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.
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