Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier
Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach f...
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| Published in | Brain informatics Vol. 11; no. 1; pp. 7 - 13 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2024
Springer Springer Nature B.V SpringerOpen |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2198-4018 2198-4026 2198-4026 |
| DOI | 10.1186/s40708-024-00220-3 |
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| Abstract | Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an
F
-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter
q
= 3. In addition, the highest accuracy and
F
-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.
Highlights
Subject independent human emotion identification is studied using SEED data set.
Tsallis entropy is employed as feature and performance variation with Tsallis parameter (
q
=
2, 3, 4)
is examined.
Performance of kNN classifier is examined with Tsallis entropy feature.
Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere.
Prospects of TsEn-based real-time emotion recognition framework is canvassed. |
|---|---|
| AbstractList | Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F -score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F -score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Highlights Subject independent human emotion identification is studied using SEED data set. Tsallis entropy is employed as feature and performance variation with Tsallis parameter ( q = 2, 3, 4) is examined. Performance of kNN classifier is examined with Tsallis entropy feature. Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere. Prospects of TsEn-based real-time emotion recognition framework is canvassed. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.HighlightsSubject independent human emotion identification is studied using SEED data set.Tsallis entropy is employed as feature and performance variation with Tsallis parameter (q = 2, 3, 4) is examined.Performance of kNN classifier is examined with Tsallis entropy feature.Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere.Prospects of TsEn-based real-time emotion recognition framework is canvassed. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Subject independent human emotion identification is studied using SEED data set.Tsallis entropy is employed as feature and performance variation with Tsallis parameter (q = 2, 3, 4) is examined.Performance of kNN classifier is examined with Tsallis entropy feature.Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere.Prospects of TsEn-based real-time emotion recognition framework is canvassed. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F -score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F -score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-[theta] (4-7 Hz), alpha-[alpha] (8-15 Hz), beta- (16-31 Hz), gamma-[gamma] (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Abstract Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. |
| ArticleNumber | 7 |
| Audience | Academic |
| Author | Patel, Pragati Annavarapu, Ramesh Naidu Balasubramanian, Sivarenjani |
| Author_xml | – sequence: 1 givenname: Pragati surname: Patel fullname: Patel, Pragati organization: Department of Physics, Pondicherry University – sequence: 2 givenname: Sivarenjani surname: Balasubramanian fullname: Balasubramanian, Sivarenjani organization: Department of Physics, Pondicherry University – sequence: 3 givenname: Ramesh Naidu surname: Annavarapu fullname: Annavarapu, Ramesh Naidu email: rameshnaidu.phy@pondiuni.edu.in organization: Department of Physics, Pondicherry University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38441825$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/BF02344719 10.1016/j.jneumeth.2020.108904 10.1177/0539018405058216 10.1023/B:BRAT.0000006333.93597.9d 10.3390/s19071631 10.3389/fninf.2019.00040 10.48047/nq.2023.21.01.NQ20009 10.1016/j.measurement.2019.107003 10.1002/int.23096 10.1155/2018/5238028 10.1093/oso/9780195159769.001.0001 10.1186/s40708-021-00141-5 10.1016/S0378-4371(02)00958-5 10.1109/JSEN.2020.3027181 10.1016/j.jneumeth.2022.109483 10.1136/jnnp.74.1.9 10.1037/0003-066X.50.5.372 10.3390/sym11050683 10.1007/BF01016429 10.1007/s00521-015-2149-8 10.1007/s00521-018-3620-0 10.1186/s12991-017-0157-z 10.3390/e19030130 10.1016/j.eplepsyres.2007.08.002 10.3906/elk-1805-126 10.1016/j.knosys.2013.02.014 10.3390/s18082739 10.1016/j.jneumeth.2021.109209 10.1523/JNEUROSCI.17-03-01179.1997 10.3390/e19050196 10.1037/0022-3514.53.4.712 10.3390/e23080984 10.1016/S0013-4694(97)00111-9 10.1016/S0378-4371(98)00471-3 10.1007/s40815-018-0567-3 10.1114/1.1541013 10.1016/S0375-9601(03)00949-6 10.3390/e18060221 10.1590/S0103-97331999000100002 10.1109/TSMCB.2005.854502 10.1016/j.knosys.2015.08.004 10.1002/j.1538-7305.1948.tb01338.x 10.1007/s10111-017-0450-2 10.1142/S0129065718500387 10.1007/978-3-642-29305-4_133 10.1109/CW.2010.37 10.21437/ICSLP.2002-559 10.1109/NER.2009.5109347 10.1109/CNE.2007.369753 10.1109/ICAwST.2017.8256518 10.1109/BSEC.2010.5510813 |
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| Keywords | Emotion identification KNN classifier Brain region Tsallis entropy SEED data set EEG channel selection EEG signal Feature engineering |
| Language | English |
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| References | Zhang, Bi, Sun (CR49) 2013 Guo, Chai, Candra (CR35) 2019; 21 Shannon (CR15) 1948; 27 Herwig, Satrapi, Schönfeldt-Lecuona (CR8) 2003; 16 Gao, Wang, Potter (CR10) 2020; 346 Lotfalinezhad, Maleki (CR26) 2019; 27 Contreras, Destexhe, Sejnowski, Steriade (CR43) 1997; 17 Tong, Bezerianos, Malhotra (CR45) 2003; 314 Zheng, Hu, Zheng (CR56) 2022; 37 Acharya, Sree, Swapna (CR18) 2013; 45 Coronel, Garn, Waser (CR20) 2017; 19 Chu, Huang, Jian, Cheng (CR51) 2017; 16 Chen, Miao, Yang (CR33) 2019; 19 Lang (CR3) 1995; 50 Acharya, Fujita, Sudarshan (CR11) 2015; 88 Ekman, Friesen, O’sullivan (CR2) 1987; 53 CR4 CR6 Cai, Chen, Yin (CR31) 2019; 11 Haas (CR7) 2003; 74 Yao, Wang, Lu (CR54) 2021; 23 Tsallis (CR38) 1999; 29 CR47 CR46 Bezerianos, Tong, Thakor (CR48) 2003; 31 Mohammadi, Frounchi, Amiri (CR25) 2017; 28 Martínez-Rodrigo, García-Martínez, Zunino (CR36) 2019; 13 Capurro, Diambra, Lorenzo (CR44) 1999; 265 Martínez-Rodrigo, García-Martínez, Alcaraz (CR34) 2019; 29 Rosso, Martin, Plastino (CR42) 2002; 313 García-Martínez, Martínez-Rodrigo, Zangróniz Cantabrana (CR52) 2016; 18 Alazrai, Homoud, Alwanni, Daoud (CR30) 2018; 18 Cai, Han, Chen (CR23) 2018; 2018 Zhao, Van-Eetvelt, Goh (CR19) 2007; 1 CR12 Cherian, Kanaga (CR16) 2022; 369 Gell-Mann, Tsallis (CR40) 2004 CR55 Anderson, McOwan (CR5) 2006; 36 Tsallis (CR39) 1988; 52 CR50 Li, Ouyang, Richards (CR17) 2007; 77 Patel, Annavarapu (CR13) 2021; 8 García-Martínez, Martínez-Rodrigo, Fernández-Caballero (CR32) 2020; 32 Lu, Wang, Wu (CR53) 2020; 150 CR27 Patel, Balasubramanian, Annavarapu (CR14) 2023; 21 CR21 Yin, Liu, Liu (CR29) 2017; 19 Schaul (CR41) 1998; 106 Kim, Bang, Kim (CR9) 2004; 42 Bhattacharyya, Tripathy, Garg, Pachori (CR37) 2020; 21 Scherer (CR1) 2005; 44 Movahed, Jahromi, Shahyad, Meftahi (CR22) 2021; 358 Bos (CR24) 2006; 56 García-Martínez, Martínez-Rodrigo, Zangróniz (CR28) 2017; 19 A Martínez-Rodrigo (220_CR34) 2019; 29 C Coronel (220_CR20) 2017; 19 A Capurro (220_CR44) 1999; 265 F Zheng (220_CR56) 2022; 37 220_CR12 RA Movahed (220_CR22) 2021; 358 220_CR55 220_CR50 U Herwig (220_CR8) 2003; 16 R Alazrai (220_CR30) 2018; 18 220_CR47 220_CR46 P Patel (220_CR14) 2023; 21 A Bezerianos (220_CR48) 2003; 31 C Tsallis (220_CR39) 1988; 52 UR Acharya (220_CR18) 2013; 45 J Cai (220_CR31) 2019; 11 R Cherian (220_CR16) 2022; 369 H Cai (220_CR23) 2018; 2018 Y Gao (220_CR10) 2020; 346 A Martínez-Rodrigo (220_CR36) 2019; 13 KR Scherer (220_CR1) 2005; 44 PJ Lang (220_CR3) 1995; 50 S Tong (220_CR45) 2003; 314 L Yao (220_CR54) 2021; 23 P Zhao (220_CR19) 2007; 1 Z Mohammadi (220_CR25) 2017; 28 D-W Chen (220_CR33) 2019; 19 LF Haas (220_CR7) 2003; 74 B García-Martínez (220_CR28) 2017; 19 W-L Chu (220_CR51) 2017; 16 220_CR6 DO Bos (220_CR24) 2006; 56 C Tsallis (220_CR38) 1999; 29 KH Kim (220_CR9) 2004; 42 A Bhattacharyya (220_CR37) 2020; 21 220_CR4 P Patel (220_CR13) 2021; 8 P Ekman (220_CR2) 1987; 53 OA Rosso (220_CR42) 2002; 313 X Li (220_CR17) 2007; 77 220_CR27 N Schaul (220_CR41) 1998; 106 M Gell-Mann (220_CR40) 2004 220_CR21 CE Shannon (220_CR15) 1948; 27 Y Lu (220_CR53) 2020; 150 B García-Martínez (220_CR32) 2020; 32 Z Yin (220_CR29) 2017; 19 A Zhang (220_CR49) 2013 UR Acharya (220_CR11) 2015; 88 D Contreras (220_CR43) 1997; 17 K Anderson (220_CR5) 2006; 36 K Guo (220_CR35) 2019; 21 B García-Martínez (220_CR52) 2016; 18 H Lotfalinezhad (220_CR26) 2019; 27 |
| References_xml | – volume: 42 start-page: 419 year: 2004 end-page: 427 ident: CR9 article-title: Emotion recognition system using short-term monitoring of physiological signals publication-title: Med Biol Eng Comput doi: 10.1007/BF02344719 – volume: 346 start-page: 108904 year: 2020 ident: CR10 article-title: Single-trial EEG emotion recognition using granger causality/transfer entropy analysis publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2020.108904 – volume: 44 start-page: 695 year: 2005 end-page: 729 ident: CR1 article-title: What are emotions? And how can they be measured? publication-title: Soc Sci Inf doi: 10.1177/0539018405058216 – volume: 16 start-page: 95 year: 2003 end-page: 99 ident: CR8 article-title: Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation publication-title: Brain Topogr doi: 10.1023/B:BRAT.0000006333.93597.9d – volume: 19 start-page: 1631 year: 2019 ident: CR33 article-title: A feature extraction method based on differential entropy and linear discriminant analysis for emotion recognition publication-title: Sensors doi: 10.3390/s19071631 – volume: 13 start-page: 40 year: 2019 ident: CR36 article-title: Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition publication-title: Front Neuroinform doi: 10.3389/fninf.2019.00040 – volume: 21 start-page: 135 year: 2023 end-page: 149 ident: CR14 article-title: Tsallis entropy as biomarker to assess and identify human emotion via EEG rhythm analysis publication-title: NeuroQuantology doi: 10.48047/nq.2023.21.01.NQ20009 – ident: CR4 – volume: 150 start-page: 107003 year: 2020 ident: CR53 article-title: Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals publication-title: Measurement doi: 10.1016/j.measurement.2019.107003 – ident: CR12 – volume: 37 start-page: 12511 year: 2022 end-page: 12533 ident: CR56 article-title: Dynamic differential entropy and brain connectivity features based EEG emotion recognition publication-title: Int J Intell Syst doi: 10.1002/int.23096 – volume: 2018 start-page: 1 year: 2018 ident: CR23 article-title: A pervasive approach to EEG-based depression detection publication-title: Complexity doi: 10.1155/2018/5238028 – year: 2004 ident: CR40 publication-title: Nonextensive entropy: interdisciplinary applications doi: 10.1093/oso/9780195159769.001.0001 – volume: 8 start-page: 1 year: 2021 end-page: 13 ident: CR13 article-title: EEG-based human emotion recognition using entropy as a feature extraction measure publication-title: Brain Informatics doi: 10.1186/s40708-021-00141-5 – volume: 313 start-page: 587 year: 2002 end-page: 608 ident: CR42 article-title: Brain electrical activity analysis using wavelet-based informational tools publication-title: Phys A Stat Mech its Appl doi: 10.1016/S0378-4371(02)00958-5 – volume: 21 start-page: 3579 year: 2020 end-page: 3591 ident: CR37 article-title: A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition publication-title: IEEE Sens J doi: 10.1109/JSEN.2020.3027181 – volume: 369 start-page: 109483 year: 2022 ident: CR16 article-title: Theoretical and methodological analysis of EEG based seizure detection and prediction: an exhaustive review publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2022.109483 – volume: 74 start-page: 9 year: 2003 end-page: LP9 ident: CR7 article-title: Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography publication-title: J Neurol Neurosurg; Psychiatry doi: 10.1136/jnnp.74.1.9 – volume: 50 start-page: 372 year: 1995 ident: CR3 article-title: The emotion probe: studies of motivation and attention publication-title: Am Psychol doi: 10.1037/0003-066X.50.5.372 – ident: CR21 – volume: 11 start-page: 683 year: 2019 ident: CR31 article-title: Multiple transferable recursive feature elimination technique for emotion recognition based on EEG signals publication-title: Symmetry doi: 10.3390/sym11050683 – ident: CR46 – volume: 52 start-page: 479 year: 1988 end-page: 487 ident: CR39 article-title: Possible generalization of Boltzmann-Gibbs statistics publication-title: J Stat Phys doi: 10.1007/BF01016429 – ident: CR50 – volume: 28 start-page: 1985 year: 2017 end-page: 1990 ident: CR25 article-title: Wavelet-based emotion recognition system using EEG signal publication-title: Neural Comput Appl doi: 10.1007/s00521-015-2149-8 – volume: 32 start-page: 13221 year: 2020 end-page: 13231 ident: CR32 article-title: Nonlinear predictability analysis of brain dynamics for automatic recognition of negative stress publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3620-0 – volume: 16 start-page: 1 year: 2017 end-page: 9 ident: CR51 article-title: Analysis of EEG entropy during visual evocation of emotion in schizophrenia publication-title: Ann Gen Psychiatry doi: 10.1186/s12991-017-0157-z – volume: 19 start-page: 130 year: 2017 ident: CR20 article-title: Quantitative EEG markers of entropy and auto mutual information in relation to MMSE scores of probable Alzheimer’s disease patients publication-title: Entropy doi: 10.3390/e19030130 – volume: 77 start-page: 70 year: 2007 end-page: 74 ident: CR17 article-title: Predictability analysis of absence seizures with permutation entropy publication-title: Epilepsy Res doi: 10.1016/j.eplepsyres.2007.08.002 – volume: 27 start-page: 4070 year: 2019 end-page: 4081 ident: CR26 article-title: Application of multiscale fuzzy entropy features for multilevel subject-dependent emotion recognition publication-title: Turkish J Electr Eng Comput Sci doi: 10.3906/elk-1805-126 – volume: 45 start-page: 147 year: 2013 end-page: 165 ident: CR18 article-title: Automated EEG analysis of epilepsy: a review publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2013.02.014 – ident: CR47 – volume: 18 start-page: 2739 year: 2018 ident: CR30 article-title: EEG-based emotion recognition using quadratic time-frequency distribution publication-title: Sensors doi: 10.3390/s18082739 – volume: 358 start-page: 109209 year: 2021 ident: CR22 article-title: A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2021.109209 – volume: 17 start-page: 1179 year: 1997 end-page: 1196 ident: CR43 article-title: Spatiotemporal patterns of spindle oscillations in cortex and thalamus publication-title: J Neurosci doi: 10.1523/JNEUROSCI.17-03-01179.1997 – volume: 19 start-page: 196 year: 2017 ident: CR28 article-title: Symbolic analysis of brain dynamics detects negative stress publication-title: Entropy doi: 10.3390/e19050196 – volume: 1 start-page: 5127 year: 2007 ident: CR19 article-title: Characterization of EEGs in Alzheimer’s disease using information theoretic methods publication-title: IEEE Eng Med Biol Mag – ident: CR6 – volume: 53 start-page: 712 year: 1987 ident: CR2 article-title: Universals and cultural differences in the judgments of facial expressions of emotion publication-title: J Pers Soc Psychol doi: 10.1037/0022-3514.53.4.712 – volume: 23 start-page: 984 year: 2021 ident: CR54 article-title: EEG-based emotion recognition by exploiting fused network entropy measures of complex networks across subjects publication-title: Entropy doi: 10.3390/e23080984 – volume: 106 start-page: 101 year: 1998 end-page: 107 ident: CR41 article-title: The fundamental neural mechanisms of electroencephalography publication-title: Electroencephalogr Clin Neurophysiol doi: 10.1016/S0013-4694(97)00111-9 – volume: 265 start-page: 235 year: 1999 end-page: 254 ident: CR44 article-title: Human brain dynamics: the analysis of EEG signals with Tsallis information measure publication-title: Phys A Stat Mech its Appl doi: 10.1016/S0378-4371(98)00471-3 – ident: CR27 – volume: 21 start-page: 263 year: 2019 end-page: 273 ident: CR35 article-title: A hybrid fuzzy cognitive map/support vector machine approach for EEG-based emotion classification using compressed sensing publication-title: Int J Fuzzy Syst doi: 10.1007/s40815-018-0567-3 – volume: 56 start-page: 1 year: 2006 end-page: 17 ident: CR24 article-title: EEG-based emotion recognition publication-title: Influ Vis Audit Stimuli – volume: 31 start-page: 221 year: 2003 end-page: 232 ident: CR48 article-title: Time-dependent entropy estimation of EEG rhythm changes following brain ischemia publication-title: Ann Biomed Eng doi: 10.1114/1.1541013 – volume: 314 start-page: 354 year: 2003 end-page: 361 ident: CR45 article-title: Parameterized entropy analysis of EEG following hypoxic–ischemic brain injury publication-title: Phys Lett A doi: 10.1016/S0375-9601(03)00949-6 – volume: 18 start-page: 221 year: 2016 ident: CR52 article-title: Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings publication-title: Entropy doi: 10.3390/e18060221 – volume: 29 start-page: 1 year: 1999 end-page: 35 ident: CR38 article-title: Nonextensive statistics: theoretical, experimental and computational evidences and connections publication-title: Brazilian J Phys doi: 10.1590/S0103-97331999000100002 – volume: 36 start-page: 96 year: 2006 end-page: 105 ident: CR5 article-title: A real-time automated system for the recognition of human facial expressions publication-title: IEEE Trans Syst Man, Cybern Part B doi: 10.1109/TSMCB.2005.854502 – volume: 88 start-page: 85 year: 2015 end-page: 96 ident: CR11 article-title: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2015.08.004 – volume: 27 start-page: 379 year: 1948 end-page: 423 ident: CR15 article-title: A mathematical theory of communication publication-title: Bell Syst Tech J doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: CR55 – volume: 19 start-page: 667 year: 2017 end-page: 685 ident: CR29 article-title: Dynamical recursive feature elimination technique for neurophysiological signal-based emotion recognition publication-title: Cogn Technol Work doi: 10.1007/s10111-017-0450-2 – volume: 29 start-page: 1850038 year: 2019 ident: CR34 article-title: Multiscale entropy analysis for recognition of visually elicited negative stress from EEG recordings publication-title: Int J Neural Syst doi: 10.1142/S0129065718500387 – start-page: 505 year: 2013 end-page: 508 ident: CR49 article-title: A method for drowsiness detection based on Tsallis entropy of EEG publication-title: World congress on medical physics and biomedical engineering, May 26–31, 2012, Beijing China doi: 10.1007/978-3-642-29305-4_133 – volume: 32 start-page: 13221 year: 2020 ident: 220_CR32 publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3620-0 – volume: 88 start-page: 85 year: 2015 ident: 220_CR11 publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2015.08.004 – volume: 265 start-page: 235 year: 1999 ident: 220_CR44 publication-title: Phys A Stat Mech its Appl doi: 10.1016/S0378-4371(98)00471-3 – volume: 369 start-page: 109483 year: 2022 ident: 220_CR16 publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2022.109483 – volume: 21 start-page: 3579 year: 2020 ident: 220_CR37 publication-title: IEEE Sens J doi: 10.1109/JSEN.2020.3027181 – volume: 74 start-page: 9 year: 2003 ident: 220_CR7 publication-title: J Neurol Neurosurg; Psychiatry doi: 10.1136/jnnp.74.1.9 – volume: 21 start-page: 263 year: 2019 ident: 220_CR35 publication-title: Int J Fuzzy Syst doi: 10.1007/s40815-018-0567-3 – volume: 29 start-page: 1 year: 1999 ident: 220_CR38 publication-title: Brazilian J Phys doi: 10.1590/S0103-97331999000100002 – volume: 17 start-page: 1179 year: 1997 ident: 220_CR43 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.17-03-01179.1997 – volume: 16 start-page: 95 year: 2003 ident: 220_CR8 publication-title: Brain Topogr doi: 10.1023/B:BRAT.0000006333.93597.9d – volume: 52 start-page: 479 year: 1988 ident: 220_CR39 publication-title: J Stat Phys doi: 10.1007/BF01016429 – volume: 56 start-page: 1 year: 2006 ident: 220_CR24 publication-title: Influ Vis Audit Stimuli – start-page: 505 volume-title: World congress on medical physics and biomedical engineering, May 26–31, 2012, Beijing China year: 2013 ident: 220_CR49 doi: 10.1007/978-3-642-29305-4_133 – volume: 106 start-page: 101 year: 1998 ident: 220_CR41 publication-title: Electroencephalogr Clin Neurophysiol doi: 10.1016/S0013-4694(97)00111-9 – volume: 19 start-page: 196 year: 2017 ident: 220_CR28 publication-title: Entropy doi: 10.3390/e19050196 – volume: 44 start-page: 695 year: 2005 ident: 220_CR1 publication-title: Soc Sci Inf doi: 10.1177/0539018405058216 – ident: 220_CR12 – volume: 23 start-page: 984 year: 2021 ident: 220_CR54 publication-title: Entropy doi: 10.3390/e23080984 – ident: 220_CR4 doi: 10.1109/CW.2010.37 – volume: 27 start-page: 379 year: 1948 ident: 220_CR15 publication-title: Bell Syst Tech J doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: 220_CR6 doi: 10.21437/ICSLP.2002-559 – volume: 45 start-page: 147 year: 2013 ident: 220_CR18 publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2013.02.014 – volume: 36 start-page: 96 year: 2006 ident: 220_CR5 publication-title: IEEE Trans Syst Man, Cybern Part B doi: 10.1109/TSMCB.2005.854502 – volume: 19 start-page: 667 year: 2017 ident: 220_CR29 publication-title: Cogn Technol Work doi: 10.1007/s10111-017-0450-2 – volume: 37 start-page: 12511 year: 2022 ident: 220_CR56 publication-title: Int J Intell Syst doi: 10.1002/int.23096 – ident: 220_CR47 doi: 10.1109/NER.2009.5109347 – volume: 2018 start-page: 1 year: 2018 ident: 220_CR23 publication-title: Complexity doi: 10.1155/2018/5238028 – volume: 346 start-page: 108904 year: 2020 ident: 220_CR10 publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2020.108904 – volume: 19 start-page: 130 year: 2017 ident: 220_CR20 publication-title: Entropy doi: 10.3390/e19030130 – volume: 18 start-page: 221 year: 2016 ident: 220_CR52 publication-title: Entropy doi: 10.3390/e18060221 – volume: 29 start-page: 1850038 year: 2019 ident: 220_CR34 publication-title: Int J Neural Syst doi: 10.1142/S0129065718500387 – volume: 11 start-page: 683 year: 2019 ident: 220_CR31 publication-title: Symmetry doi: 10.3390/sym11050683 – volume: 27 start-page: 4070 year: 2019 ident: 220_CR26 publication-title: Turkish J Electr Eng Comput Sci doi: 10.3906/elk-1805-126 – volume: 77 start-page: 70 year: 2007 ident: 220_CR17 publication-title: Epilepsy Res doi: 10.1016/j.eplepsyres.2007.08.002 – ident: 220_CR55 – volume: 50 start-page: 372 year: 1995 ident: 220_CR3 publication-title: Am Psychol doi: 10.1037/0003-066X.50.5.372 – volume: 42 start-page: 419 year: 2004 ident: 220_CR9 publication-title: Med Biol Eng Comput doi: 10.1007/BF02344719 – volume: 8 start-page: 1 year: 2021 ident: 220_CR13 publication-title: Brain Informatics doi: 10.1186/s40708-021-00141-5 – volume: 28 start-page: 1985 year: 2017 ident: 220_CR25 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-2149-8 – volume: 358 start-page: 109209 year: 2021 ident: 220_CR22 publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2021.109209 – ident: 220_CR50 doi: 10.1109/CNE.2007.369753 – volume: 150 start-page: 107003 year: 2020 ident: 220_CR53 publication-title: Measurement doi: 10.1016/j.measurement.2019.107003 – ident: 220_CR46 – volume: 19 start-page: 1631 year: 2019 ident: 220_CR33 publication-title: Sensors doi: 10.3390/s19071631 – volume: 21 start-page: 135 year: 2023 ident: 220_CR14 publication-title: NeuroQuantology doi: 10.48047/nq.2023.21.01.NQ20009 – ident: 220_CR27 doi: 10.1109/ICAwST.2017.8256518 – volume-title: Nonextensive entropy: interdisciplinary applications year: 2004 ident: 220_CR40 doi: 10.1093/oso/9780195159769.001.0001 – volume: 18 start-page: 2739 year: 2018 ident: 220_CR30 publication-title: Sensors doi: 10.3390/s18082739 – volume: 13 start-page: 40 year: 2019 ident: 220_CR36 publication-title: Front Neuroinform doi: 10.3389/fninf.2019.00040 – volume: 53 start-page: 712 year: 1987 ident: 220_CR2 publication-title: J Pers Soc Psychol doi: 10.1037/0022-3514.53.4.712 – volume: 1 start-page: 5127 year: 2007 ident: 220_CR19 publication-title: IEEE Eng Med Biol Mag – volume: 314 start-page: 354 year: 2003 ident: 220_CR45 publication-title: Phys Lett A doi: 10.1016/S0375-9601(03)00949-6 – ident: 220_CR21 doi: 10.1109/BSEC.2010.5510813 – volume: 31 start-page: 221 year: 2003 ident: 220_CR48 publication-title: Ann Biomed Eng doi: 10.1114/1.1541013 – volume: 16 start-page: 1 year: 2017 ident: 220_CR51 publication-title: Ann Gen Psychiatry doi: 10.1186/s12991-017-0157-z – volume: 313 start-page: 587 year: 2002 ident: 220_CR42 publication-title: Phys A Stat Mech its Appl doi: 10.1016/S0378-4371(02)00958-5 |
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| Snippet | Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces,... Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces,... Abstract Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces,... |
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| StartPage | 7 |
| SubjectTerms | Artificial Intelligence Brain region Classifiers Cognitive Psychology Computation by Abstract Devices Computer Science Datasets EEG channel selection EEG signal Electroencephalography Emotion identification Emotion recognition Emotional factors Emotions Entropy Feature engineering Frequency ranges Health Informatics Human-computer interface Neurophysiology Neurosciences Parameters Performance enhancement Tsallis entropy |
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| Title | Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier |
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