EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals
Currently, there are some problems in the electrocorticogram (EEG) emotion recognition research, such as single feature, redundant signal, which make it impossible to achieve high-precision recognition accuracy when used a few channel signals. To solve the abovementioned problems, the authors propos...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5130184 |
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| _version_ | 1832568150459154432 |
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| author | Zhihao Qu Xiujuan Zheng |
| author_facet | Zhihao Qu Xiujuan Zheng |
| author_sort | Zhihao Qu |
| collection | DOAJ |
| description | Currently, there are some problems in the electrocorticogram (EEG) emotion recognition research, such as single feature, redundant signal, which make it impossible to achieve high-precision recognition accuracy when used a few channel signals. To solve the abovementioned problems, the authors proposed a method for emotion recognition based on long short-term memory (LSTM) neural network and convolutional neural network (CNN) combined with neurophysiological knowledge. First, the authors selected emotion-sensitive signals based on the physiological function of EEG regions and the active scenario of the band signals, and then merged temporal and spatial features extracted from sensitive signals by LSTM and CNN. Finally, merged features were classified to recognize emotion. The method was experimented on the DEAP dataset, the average accuracy in the valence and arousal dimensions were 92.87% and 93.23%, respectively. Compared with similar studies, it not only improved the recognition accuracy, but also greatly reduced the calculation channel, which proved the superiority of the method. |
| format | Article |
| id | doaj-art-767fb1e8eccf47628b55e68d133c9427 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-767fb1e8eccf47628b55e68d133c94272025-02-03T00:59:37ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/5130184EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signalsZhihao Qu0Xiujuan Zheng1College of Electrical EngineeringCollege of Electrical EngineeringCurrently, there are some problems in the electrocorticogram (EEG) emotion recognition research, such as single feature, redundant signal, which make it impossible to achieve high-precision recognition accuracy when used a few channel signals. To solve the abovementioned problems, the authors proposed a method for emotion recognition based on long short-term memory (LSTM) neural network and convolutional neural network (CNN) combined with neurophysiological knowledge. First, the authors selected emotion-sensitive signals based on the physiological function of EEG regions and the active scenario of the band signals, and then merged temporal and spatial features extracted from sensitive signals by LSTM and CNN. Finally, merged features were classified to recognize emotion. The method was experimented on the DEAP dataset, the average accuracy in the valence and arousal dimensions were 92.87% and 93.23%, respectively. Compared with similar studies, it not only improved the recognition accuracy, but also greatly reduced the calculation channel, which proved the superiority of the method.http://dx.doi.org/10.1155/2022/5130184 |
| spellingShingle | Zhihao Qu Xiujuan Zheng EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals Journal of Electrical and Computer Engineering |
| title | EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals |
| title_full | EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals |
| title_fullStr | EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals |
| title_full_unstemmed | EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals |
| title_short | EEG Emotion Recognition Based on Temporal and Spatial Features of Sensitive signals |
| title_sort | eeg emotion recognition based on temporal and spatial features of sensitive signals |
| url | http://dx.doi.org/10.1155/2022/5130184 |
| work_keys_str_mv | AT zhihaoqu eegemotionrecognitionbasedontemporalandspatialfeaturesofsensitivesignals AT xiujuanzheng eegemotionrecognitionbasedontemporalandspatialfeaturesofsensitivesignals |