Efficient approach for EEG-based emotion recognition
Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept...
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| Veröffentlicht in: | Electronics letters Jg. 56; H. 25; S. 1361 - 1364 |
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The Institution of Engineering and Technology
10.12.2020
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| Abstract | Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept for automatic identification of human emotion from EEG signals. In the proposed framework, the signals are pre-processing for removing noises by low-pass filtering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre-trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most efficient features and finally, long short term memory method is employed for classification of selected features. The proposed methodology is tested on ‘DEAP EEG data set’ (publicly available). This study considers two emotions namely ‘Valence’ and ‘Arousal’ for classification. The experimental results demonstrate that the proposed approach produced accuracies of 96.1% for low/high valence and 99.6% for low/high arousal classification. A further comparison of the proposed method is also carried out and it is seen that the proposed method outperforms other compared methods. |
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| AbstractList | Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept for automatic identification of human emotion from EEG signals. In the proposed framework, the signals are pre‐processing for removing noises by low‐pass filtering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre‐trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most efficient features and finally, long short term memory method is employed for classification of selected features. The proposed methodology is tested on ‘DEAP EEG data set’ (publicly available). This study considers two emotions namely ‘Valence’ and ‘Arousal’ for classification. The experimental results demonstrate that the proposed approach produced accuracies of 96.1% for low/high valence and 99.6% for low/high arousal classification. A further comparison of the proposed method is also carried out and it is seen that the proposed method outperforms other compared methods. |
| Author | Şengür, D Siuly, S |
| Author_xml | – sequence: 1 givenname: D surname: Şengür fullname: Şengür, D email: dsengur@firat.edu.tr organization: Education Faculty, Educational Science Department, Firat University, Elazig, Turkey – sequence: 2 givenname: S surname: Siuly fullname: Siuly, S organization: Institute for Sustainable Industries & Liveable Cities, Victoria University, Australia |
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| Cites_doi | 10.1146/annurev‐psych‐010213‐115043 10.1007/s11280‐012‐0181‐5 10.3390/s18082739 10.1109/JSEN.2019.2917850 10.1007/s13755‐018‐0057‐x 10.1109/ICASSP.2013.6637858 10.1162/neco.1997.9.8.1735 10.3390/s19092212 10.1126/science.1076358 10.1016/j.neulet.2016.09.037 10.1109/EMBC.2015.7320065 10.1609/aaai.v31i2.19105 10.1109/JSEN.2018.2883497 10.1186/s40708‐020‐00111‐3 10.1109/ACCESS.2019.2908285 10.1155/2017/8317357 10.1109/T‐AFFC.2011.15 10.1023/A:1025667309714 10.1109/CVPR.2018.00474 |
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| Keywords | wavelet transforms diverse insight emotional states continuous wavelet electroencephalogram signals pre-trained convolutional neural networks model emotion recognition delta rhythm feature extraction EEG signals low-pass filtering learning (artificial intelligence) deep feature selection electroencephalography efficient features EEG rhythm images deep features health monitoring application medical signal processing long short term memory method deep learning concept EEG-based emotion recognition human emotion extracted rhythm signals automatic identification neural nets |
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| SubjectTerms | automatic identification continuous wavelet deep feature selection deep features deep learning concept delta rhythm diverse insight EEG rhythm images EEG signals EEG‐based emotion recognition efficient features electroencephalogram signals electroencephalography emotion recognition emotional states extracted rhythm signals feature extraction health monitoring application human emotion learning (artificial intelligence) long short term memory method low‐pass filtering medical signal processing neural nets pre‐trained convolutional neural networks model Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications wavelet transforms |
| Title | Efficient approach for EEG-based emotion recognition |
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