Emotion recognition with deep learning using GAMEEMO data set
Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. Howev...
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| Published in: | Electronics letters Vol. 56; no. 25; pp. 1364 - 1367 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
10.12.2020
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| ISSN: | 0013-5194, 1350-911X, 1350-911X |
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| Abstract | Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long-short term memory architecture in the third stage. In the last stage, the performance of the deep-learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained. |
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| AbstractList | Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long-short term memory architecture in the third stage. In the last stage, the performance of the deep-learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained. Emotion recognition is actively used in brain–computer interface, health care, security, e‐commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision‐making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long‐short term memory architecture in the third stage. In the last stage, the performance of the deep‐learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained. |
| Author | Alakus, T. B Turkoglu, I |
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| Cites_doi | 10.1186/s40708‐019‐0100‐y 10.1109/72.279188 10.1007/s11760‐013‐0591‐6 10.1109/TELFOR.2011.6143635 10.1097/00000542-200407000-00008 10.14569/IJACSA.2017.081046 10.1109/DSAA.2016.92 10.1177/1754073912439763 10.1109/BHI.2017.7897275 10.1097/JTO.0b013e3181ec173d 10.1111/j.0001‐5172.2004.00323.x 10.1109/IJCNN.2016.7727453 10.1243/09544062JMES1224 10.1007/11550907_126 10.1016/j.jksuci.2019.11.003 10.1155/2014/627892 10.1016/j.proeng.2016.05.131 10.16984/saufenbilder.501799 10.1016/j.bspc.2020.101951 10.1037/0033-295X.110.1.145 10.1016/j.ijforecast.2020.06.008 |
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| Keywords | electroencephalography education electroencephalography signals negative emotions GAMEEMO data set brain–computer interface emotion recognition abstract concept medical signal processing internal factors deep learning entertainment applications entropy emotion analysis decision making human computer interaction human–machine interaction EEG signals learning (artificial intelligence) positive emotions health care |
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| SubjectTerms | abstract concept brain–computer interface decision making deep learning education EEG signals electroencephalography electroencephalography signals emotion analysis emotion recognition entertainment applications entropy GAMEEMO data set health care human computer interaction human–machine interaction internal factors learning (artificial intelligence) medical signal processing negative emotions positive emotions Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications |
| Title | Emotion recognition with deep learning using GAMEEMO data set |
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