篇名 |
EEG Emotion Recognition Method Based on 3D Feature Map and Improved DenseNet
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並列篇名 | EEG Emotion Recognition Method Based on 3D Feature Map and Improved DenseNet |
作者 | Jing-Ran Su、Qiu-Sheng Li、Qian-Li Zhang、Jun-Yong Hu |
英文摘要 | Emotion, as a high-level function of the human brain, has a great impact on people’s mental health. To fully con-sider EEG signals’ spatial information and time-frequency information, and realize human-computer interaction better. This paper proposes an improved DenseNet emotion recognition model based on 3D feature map. By extracting the differential entropy features of the θ, α, β and γ frequency bands of the EEG signals, and combining the position mapping relationship of the EEG channel electrodes, a three-dimensional feature map is constructed, and then the improved densely connected convolutional network (DenseNet) is used for secondary feature extraction and classification. To verify the effectiveness of this method, a classification experiment including positive, neutral and negative emotions is carried out on the SEED data set. The classification accuracy rates obtained in the single-subject experiment and the all-subject experiment are 98.51% and 98.68%, respectively. The experimental results show that the method of 3D feature map combined with feature reuse can get high-precision classification results, which provides a new direction for emotion recognition.
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起訖頁 | 109-120 |
關鍵詞 | EEG、electrode mapping、3D feature map、feature reuse、multi-scale convolution kernel |
刊名 | 電腦學刊 |
期數 | 202306 (34:3期) |
DOI |
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