research-article

Emotion recognition using brain activity

Authors:

Robert Horlings,

Leon J. M. RothkrantzAuthors Info & Claims

CompSysTech '08: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

Article No.: 6, Pages II.1 - 1

https://doi.org/10.1145/1500879.1500888

Published: 12 June 2008 Publication History

Abstract

Our project focused on recognizing emotion from human brain activity, measured by EEG signals. We have proposed a system to analyze EEG signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice. In order to perform this assessment, we have gathered a dataset with EEG signals. This was done by measuring EEG signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion. We found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classification rates of 32% for recognizing the valence dimension from EEG signals and 37% for the arousal dimension. Much better classification rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.

References

[1]

Aftanas, L., N. Reva, A. Varlamov, S. Pavlov, V. Makhnev. Analysis of evoked eeg synchronization and desynchronization in conditions of emotional activation in humans: Temporal and topographic characteristics, Neuroscience and Behavioral Physiology, vol. 34, no. 8, pp. 859--867, 2004.

[2]

Bradley, M. M., P. J. Lang. Measuring emotion: The self-assessment manikin (sam) and the semantic differential, Journal of Experimental Psychiatry & Behavior Therapy, vol. 25, no. 1, pp. 49--59, 1994.

[3]

Bush, G., P. Luu, M. Posner. Cognitive and emotional influences in anterior cingulate cortex, Trends in cognitive sciences, vol. 4, pp. 215--222, 2000.

[4]

Busso, C., Z. Deng, S. Yildirim, M. Bulut, C. Lee, A. Kazemzadeh, S. Lee, U. Neumann, S. Narayanan. Analysis of emotion recognition using facial expressions, speech and multimodal information, in ICMI '04: Proceedings of the 6th international conference on Multimodal interfaces. New York, NY, USA: ACM Press, 2004.

Digital Library

[5]

Chanel, G., J. Kronegg, D. Grandjean, T. Pun. Emotion assessment: Arousal evaluation using eeg's and peripheral physiological signals, Computer Vision Group, Computing Science Center, University of Geneva, Tech. Rep., 2005.

[6]

Choppin, A. Eeg-based human interface for disabled individuals: Emotion expression with neural networks, Master's thesis, Tokyo Institure of Technology, 2000.

[7]

Dellaert, F., T. Polzin, A. Waibel. Recognizing emotion in speech, in ICSLP 96. Proceedings, vol. 3, October 1996, pp. 1970--1973.

[8]

Ekman, P. Basic emotions, in Handbook of cognition and emotion, 1999.

[9]

Hagemann, D., E. Naumann, J. Thayer. The quest for the eeg reference revisited: A glance from brain asymmetry research, Psychophysiology, 2001.

[10]

Hjorth, B. Eeg analysis based on time domain properties, Electroencephalography and Clinical Neurophysiology, vol. 29, no. 3, pp. 306--310, 1970.

[11]

Lang, P., M. Bradley, B. Cuthbert. International affective picture system (iaps): Affective ratings of pictures and instruction manual, University of Florida, Gainesville, FL, Tech. Rep. Technical Report A-6, 2005.

[12]

Pantic, M., L. Rothkrantz. Automatic analysis of facial expressions: The state of the art," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.

Digital Library

[13]

Peng, H., F. Long, C. Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance and minredundancy, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, 2005.

Digital Library

[14]

Reeves, B., C. Nass. The media equation; how people treat computers, television, and new media like real people and places. Stanford: CSLI, 1996.

Digital Library

[15]

Savran, A., K. Ciftci, G. Chanel, J. Mota, L. Viet, B. Sankur, L. Akarun, A. Caplier, M. Rombaut. Emotion detection in the loop from brain signals and facial images, http://www.enterface.net/results/, 2006, visited on September, 26, 2007.

[16]

Schiffer, F., M. Teicher, C. Anderson, A. Tomoda, A. Polcari, C. Navalta, S. Andersen. Determination of hemispheric emotional valence in individual subjects: A new approach with research and therapeutic implications, Behavioral and Brain Functions, 2007.

[17]

Shemyakina, N., S. Danko. Influence of the emotional perception of a signal on the electroencephalographic correlates of creative activity, Human Physiology, 2004.

Cited By

Vo HNguyen TVan Huynh T(2024)Classification of Emotions via EEG Signals by Deep Learning ApproachProceedings of the International Conference on Intelligent Systems and Networks10.1007/978-981-97-5504-2_79(688-696)Online publication date: 1-Sep-2024
https://doi.org/10.1007/978-981-97-5504-2_79
Sen OSheehan ARaman PKhara KKhalifa AChatterjee B(2023)Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A ReviewSensors10.3390/s2312557523:12(5575)Online publication date: 14-Jun-2023
https://doi.org/10.3390/s23125575
Kargarnovin SHernandez CFarahani FKarwowski W(2023)Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic ReviewBrain Sciences10.3390/brainsci1305081313:5(813)Online publication date: 17-May-2023
https://doi.org/10.3390/brainsci13050813
Show More Cited By

Emotion recognition using brain activity
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

CompSysTech '08: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

June 2008

598 pages

ISBN:9789549641523

DOI:10.1145/1500879

Editors:
Boris Rachev
Technical University of Varna, Bulgaria
,
Anger Smrikarov
University of Rousse, Bulgaria

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 June 2008

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Cited By

Vo HNguyen TVan Huynh T(2024)Classification of Emotions via EEG Signals by Deep Learning ApproachProceedings of the International Conference on Intelligent Systems and Networks10.1007/978-981-97-5504-2_79(688-696)Online publication date: 1-Sep-2024
https://doi.org/10.1007/978-981-97-5504-2_79
Sen OSheehan ARaman PKhara KKhalifa AChatterjee B(2023)Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A ReviewSensors10.3390/s2312557523:12(5575)Online publication date: 14-Jun-2023
https://doi.org/10.3390/s23125575
Kargarnovin SHernandez CFarahani FKarwowski W(2023)Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic ReviewBrain Sciences10.3390/brainsci1305081313:5(813)Online publication date: 17-May-2023
https://doi.org/10.3390/brainsci13050813
Meegahapola LDroz WKun Pde Götzen ANutakki CDiwakar SCorrea SSong DXu HBidoglia MGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DHume AZarza JCernuzzi LBison IBritez MBusso MChenu-Abente RGünel CGiunchiglia FSchelenz LGatica-Perez D(2023)Generalization and Personalization of Mobile Sensing-Based Mood Inference ModelsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694836:4(1-32)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569483
Liu DDai WZhang HJin XCao JKong W(2023)Brain-Machine Coupled Learning Method for Facial Emotion RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.325784645:9(10703-10717)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TPAMI.2023.3257846
Amrani HMicucci DNalin MNapoletano P(2023)Emotion Personalization with Machine Learning using EEG Signals and Dry Electrodes2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE58569.2023.10405681(132-137)Online publication date: 25-Oct-2023
https://doi.org/10.1109/MetroXRAINE58569.2023.10405681
Wang QYang LSun HZhang QTang C(2023)A two-stream channel reconstruction and feature attention network for EEG emotion recognition2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385459(4840-4847)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385459
Bhatnagar SKhandelwal SJain SVyawahare H(2023)A deep learning approach for assessing stress levels in patients using electroencephalogram signalsDecision Analytics Journal10.1016/j.dajour.2023.1002117(100211)Online publication date: Jun-2023
https://doi.org/10.1016/j.dajour.2023.100211
Chatterjee DGavas RSaha S(2023)Detection of mental stress using novel spatio-temporal distribution of brain activationsBiomedical Signal Processing and Control10.1016/j.bspc.2022.10452682(104526)Online publication date: Apr-2023
https://doi.org/10.1016/j.bspc.2022.104526
Liu DCui JPan ZZhang HCao JKong W(2023)Machine to brain: facial expression recognition using brain machine generative adversarial networksCognitive Neurodynamics10.1007/s11571-023-09946-y18:3(863-875)Online publication date: 22-Feb-2023
https://doi.org/10.1007/s11571-023-09946-y
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