Inherent Feature Connection (I-Con) Map for Liking Emotion Detection: an EEG Study

Achieving a good recognition and simulation of human emotion is becoming a hot topic in recent years. In this study, we exploited this problem in the liking emotion detection through EEG analysis. To improve the discriminant power of the extracted EEG features, a novel concept of inherent feature connection (I-Con) map was proposed, in which feature-based active channels were estimated and the hidden connections of channels in terms of features were uncovered. This method provided us an efficient and effective way to characterize EEG signals in a better way to reflect the emotion changes in the liking dimension. The performance of the proposed method was demonstrated on a well-known public emotion database.

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Author and article information

Contributors

Zhen Liang

Shigeyuki Oba

Shin Ishii

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-5

Affiliations

[0001]Kyoto University Kyoto 606-8501, Japan

[0002]Kyoto University Kyoto 606-8501, Japan ATR Cognitive Mechanisms Laboratories Kyoto 619-0288, Japan

Article

DOI: 10.14236/ewic/HCI2018.209

SO-VID: 3de0d39a-df68-41f8-841a-06c4f807a41a

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.209

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

F. BahariA. Janghorbani 2013 EEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier 2013 20th Iranian Conference on Biomedical Engineering (ICBME) 228 233
J. ChenZ. ChenZ. ChiH. Fu 2017 Facial expression recognition in video with multiple feature fusion IEEE Transactions on Affective Computing 10.1109/TAFFC.2016.2593719 2017
A.C. CruzB. BhanuN.S. Thakoor 2014 Vision and attention theory based sampling for continuous facial emotion recognition IEEE Transactions on Affective Computing 5 4 418 431
F. EybenK.R. SchererB.W. SchullerJ. SundbergE. AndréC. BussoL.Y. DevillersJ. EppsP. LaukkaS.S. NarayananK.P. Truong 2016 The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing. IEEE Transactions on Affective Computing 7 2 190 202
S. HaegensH CousijnG. WallisP.J. HarrisonA.C. Nobre 2014 Inter- and intra-individual variability in alpha peak frequency NeuroImage 92 46 55
H.H. Jasper 1958 The ten-twenty electrode system of the international federation Electroencephalography and Clinical Neurophysiology 10 371 37
R. JenkeA. PeerM. Buss 2014 Feature extraction and selection for emotion recognition from EEG IEEE Transactions on Affective Computing 5 3 327 339
I. T. Jolliffe 1986 Principal component analysis New York Springer-Verlag
Y. KimE.M. Provost 2017 ISLA: temporal segmentation and labeling for audio-visual emotion recognition. IEEE Transactions on Affective Computing 10.1109/TAFFC.2017.2702653
W. Klimesch 1999 EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews 29 2-3 169 195
S. KoelstraC. MuhlM. SoleymaniJ.S. LeeA. YazdaniT. EbrahimiT. PunA. NijholtI. Patras 2012 DEAP: a database for emotion analysis using physiological signal. IEEE Transactions on Affective Computing 3 1 18 31
X. LiP. ZhangD. SongG. YuY. HouB. Hu 2015 EEG Based Emotion Identification Using Unsupervised Deep Feature Learning SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, ID: 44132 1 2
R.J. MoranP. CampoF. MaestuR.B. ReillyR.J. DolanB.A. Strange 2010 Peak frequency in the theta and alpha bands correlates with human working memory capacity Frontiers in Human Neuroscience 4 200 1 12
D.S. NaserG. Saha 2013 Recognition of emotions induced by music videos using DT-CWPT 2013 Indian conference on Medical Informatics and Telemedicine (ICMIT) 53 57
C. ShahnazS.B. MasudS.M.S. Hasan 2016 Emotion recognition based on wavelet analysis of empirical mode decomposed EEG signals responsive to music videos 2016 IEEE Region 10 Conference (TENCON) 424 427
D. WangY. Shang 2013 Modeling physiological data with deep belief networks International Journal of Information and Education Technology (IJIET) 3 5 505 511
P. D. Welch 1967 The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms IEEE Transactions on Audio and Electroacoustic 15 2 70 73

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Comment on this article

[1] F. BahariA. Janghorbani 2013 EEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier 2013 20th Iranian Conference on Biomedical Engineering (ICBME) 228 233

[2] J. ChenZ. ChenZ. ChiH. Fu 2017 Facial expression recognition in video with multiple feature fusion IEEE Transactions on Affective Computing 10.1109/TAFFC.2016.2593719 2017

[3] A.C. CruzB. BhanuN.S. Thakoor 2014 Vision and attention theory based sampling for continuous facial emotion recognition IEEE Transactions on Affective Computing 5 4 418 431

[4] F. EybenK.R. SchererB.W. SchullerJ. SundbergE. AndréC. BussoL.Y. DevillersJ. EppsP. LaukkaS.S. NarayananK.P. Truong 2016 The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing. IEEE Transactions on Affective Computing 7 2 190 202

[5] S. HaegensH CousijnG. WallisP.J. HarrisonA.C. Nobre 2014 Inter- and intra-individual variability in alpha peak frequency NeuroImage 92 46 55

[6] H.H. Jasper 1958 The ten-twenty electrode system of the international federation Electroencephalography and Clinical Neurophysiology 10 371 37

[7] R. JenkeA. PeerM. Buss 2014 Feature extraction and selection for emotion recognition from EEG IEEE Transactions on Affective Computing 5 3 327 339

[8] I. T. Jolliffe 1986 Principal component analysis New York Springer-Verlag

[9] Y. KimE.M. Provost 2017 ISLA: temporal segmentation and labeling for audio-visual emotion recognition. IEEE Transactions on Affective Computing 10.1109/TAFFC.2017.2702653

[10] W. Klimesch 1999 EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews 29 2-3 169 195

[11] S. KoelstraC. MuhlM. SoleymaniJ.S. LeeA. YazdaniT. EbrahimiT. PunA. NijholtI. Patras 2012 DEAP: a database for emotion analysis using physiological signal. IEEE Transactions on Affective Computing 3 1 18 31

[12] X. LiP. ZhangD. SongG. YuY. HouB. Hu 2015 EEG Based Emotion Identification Using Unsupervised Deep Feature Learning SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, ID: 44132 1 2

[13] R.J. MoranP. CampoF. MaestuR.B. ReillyR.J. DolanB.A. Strange 2010 Peak frequency in the theta and alpha bands correlates with human working memory capacity Frontiers in Human Neuroscience 4 200 1 12

[14] D.S. NaserG. Saha 2013 Recognition of emotions induced by music videos using DT-CWPT 2013 Indian conference on Medical Informatics and Telemedicine (ICMIT) 53 57

[15] C. ShahnazS.B. MasudS.M.S. Hasan 2016 Emotion recognition based on wavelet analysis of empirical mode decomposed EEG signals responsive to music videos 2016 IEEE Region 10 Conference (TENCON) 424 427

[16] D. WangY. Shang 2013 Modeling physiological data with deep belief networks International Journal of Information and Education Technology (IJIET) 3 5 505 511

[17] P. D. Welch 1967 The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms IEEE Transactions on Audio and Electroacoustic 15 2 70 73

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