Exploration of effective electroencephalography features for the recognition of different valence emotions
Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this manuscript explores the effective el...
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Published in | Frontiers in neuroscience Vol. 16; p. 1010951 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Abstract | Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this manuscript explores the effective electroencephalography (EEG) features for the recognition of different valence emotions. First, 110 EEG features were extracted from the time domain, frequency domain, time-frequency domain, spatial domain, and brain network, including all the current mainly used features. Then, the classification performance, computing time, and important electrodes of each feature were systematically compared and analyzed on the self-built dataset involving 40 subjects and the public dataset DEAP. The experimental results show that the first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in the recognition of different valence emotions. Also, the time-domain features, especially the first-order difference features and second-order difference features, have less computing time, so they are suitable for real-time emotion recognition applications. Besides, the features extracted from the frontal, temporal, and occipital lobes are more effective than others for the recognition of different valence emotions. Especially, when the number of electrodes is reduced by 3/4, the classification accuracy of using features from 16 electrodes located in these brain regions is 91.8%, which is only about 2% lower than that of using all electrodes. The study results can provide an important reference for feature extraction and selection in emotion recognition based on EEG. |
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AbstractList | Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this manuscript explores the effective electroencephalography (EEG) features for the recognition of different valence emotions. First, 110 EEG features were extracted from the time domain, frequency domain, time-frequency domain, spatial domain, and brain network, including all the current mainly used features. Then, the classification performance, computing time, and important electrodes of each feature were systematically compared and analyzed on the self-built dataset involving 40 subjects and the public dataset DEAP. The experimental results show that the first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in the recognition of different valence emotions. Also, the time-domain features, especially the first-order difference features and second-order difference features, have less computing time, so they are suitable for real-time emotion recognition applications. Besides, the features extracted from the frontal, temporal, and occipital lobes are more effective than others for the recognition of different valence emotions. Especially, when the number of electrodes is reduced by 3/4, the classification accuracy of using features from 16 electrodes located in these brain regions is 91.8%, which is only about 2% lower than that of using all electrodes. The study results can provide an important reference for feature extraction and selection in emotion recognition based on EEG. Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this paper explores the effective electroencephalography (EEG) features for the recognition of different valence emotions. First, 110 EEG features were extracted from the time domain, frequency domain, time-frequency domain, spatial domain, and brain network, including all the current mainly used features. Then, the classification performance, computing time, and important electrodes of each feature were systematically compared and analyzed on the self-built dataset involving 40 subjects and the public dataset DEAP. The experimental results show that the first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in the recognition of different valence emotions. Also, the time-domain features, especially the first-order difference features and second-order difference features, have less computing time, so they are suitable for real-time emotion recognition applications. Besides, the features extracted from the frontal, temporal, and occipital lobes are more effective than others for the recognition of different valence emotions. Especially, when the number of electrodes is reduced by 3/4, the classification accuracy of using features from 16 electrodes located in these brain regions is 91.8%, which is only about 2% lower than that of using all electrodes. The study results can provide an important reference for feature extraction and selection in emotion recognition based on EEG. |
Author | Yang, Kai Zeng, Ying Yan, Bin Zhang, Rongkai Gao, Yuanlong Lu, Runnan Tong, Li |
AuthorAffiliation | Henan Key Laboratory of Imaging and Intelligent Processing, People’s Liberation Army (PLA), Strategy Support Force Information Engineering University , Zhengzhou , China |
AuthorAffiliation_xml | – name: Henan Key Laboratory of Imaging and Intelligent Processing, People’s Liberation Army (PLA), Strategy Support Force Information Engineering University , Zhengzhou , China |
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Cites_doi | 10.26599/BSA.2019.9050005 10.1109/TNSRE.2016.2523678 10.1109/TAFFC.2014.2339834 10.3389/fnhum.2020.00089 10.1109/CVPR.2019.00065 10.1088/1741-2552/ac49a7 10.1097/01.wnr.0000234744.50442.2b 10.1016/j.cogr.2021.04.001 10.1016/j.ins.2021.02.034 10.1142/S0219720005001004 10.1093/occmed/kqv087 10.1016/j.eswa.2011.06.043 10.1016/j.patcog.2007.04.009 10.1007/978-3-642-34478-7_57 10.1016/j.euroneuro.2012.10.010 10.1016/j.enbuild.2020.109789 10.1109/TSMC.1985.6313426 10.1609/aaai.v33i01.330110019 10.1109/CVPR.2016.319 10.1016/j.neuropsychologia.2007.04.018 10.3389/fnhum.2018.00267 10.1016/j.cosrev.2009.03.005 10.1016/j.mcm.2011.10.045 10.3390/s18030841 10.3389/fncom.2021.743426 10.3233/THC-174836 10.1016/j.neulet.2009.08.064 10.1016/j.neucom.2005.12.126 10.1109/TNNLS.2020.3008938 10.1109/T-AFFC.2011.15 10.1023/A:1018647011077 10.1109/TNSRE.2020.3048106 10.1109/TAFFC.2020.2994159 10.1109/ISBI45749.2020.9098708 10.1109/TAMD.2015.2431497 10.1006/brcg.1997.0895 10.1109/NER.2011.5910636 10.1109/TCYB.2018.2797176 10.5539/mas.v3n5p118 10.1016/j.measurement.2020.108047 10.1007/s00371-015-1183-y 10.1016/j.neuroimage.2006.11.048 10.1109/TBME.2019.2897651 |
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SubjectTerms | Asymmetry Brain research Classification Cognition & reasoning Cognitive ability Datasets EEG Electrodes Electroencephalography emotion recognition emotion valence Emotions Entropy Experiments feature extraction feature selection Frequency dependence Memory Neuroscience |
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Title | Exploration of effective electroencephalography features for the recognition of different valence emotions |
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