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| Content Provider | frontiers |
|---|---|
| Author | Chen, Peiji Zou, Bochao Belkacem, Abdelkader Nasreddine Lyu, Xiangwen Zhao, Xixi Yi, Weibo Huang, Zhaoyang Liang, Jun Chen, Chao |
| Abstract | Current decoding algorithms based on one-dimensional (1D) convolutional neural network (CNN) have shown effectiveness in the automatic recognition of emotional tasks using physiological signals. However, these recognition models usually take a single modal of physiological signal as input and the inter-correlates between different modalities of physiological signals are completely ignored, which could be an important source of information for emotion recognition. Therefore, a complete end-to-end multi-input deep convolutional neural network (MI-DCNN) structure was designed in this study. The newly designed 1D-CNN structure can take full advantage of multi-modal physiological signals and automatically complete the process from feature extraction to emotion classification simultaneously. To evaluate the effectiveness of the proposed model, we designed an emotion elicitation experiment and collected a total of fifty-two participants’ physiological signals including electrocardiography (ECG), electrodermal activity (EDA), and respiratory activity (RSP) while watching emotion elicitation videos. Subsequently, traditional machine learning methods were applied as baseline comparisons, for arousal, the baseline accuracy, and f1-score of our dataset are 62.9±0.9% and 0.628±0.01, respectively, for valence, the baseline accuracy, and f1-score of our dataset are 60.3±0.8% and 0.600±0.01, respectively. Differences between MI-DCNN and single-input DCNN were also compared, and the proposed method was verified on two public datasets (DEAP, DREAMER) as well as our dataset. The computing results in our dataset showed a significant improvement in both tasks compared to traditional machine learning methods (t-test, Arousal: p = 9.7E-03 < 0.01, Valence: 6.5E-03 < 0.01) which demonstrated the strength of introducing a multi-input convolutional neural network for emotion recognition based on multi-modal physiological signals. |
| ISSN | 1662453X |
| DOI | 10.3389/fnins.2022.965871 |
| Volume Number | 16 |
| Journal | Frontiers in Neuroscience |
| Language | English |
| Publisher Date | 2022-10-04 |
| Access Restriction | Open |
| Subject Keyword | Emotion recognition Machine learning Multi-modality Convolutional Neural Network Movement imagination |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience |
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