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EEG-Based Emotion Recognition in Music Listening
Abstract
Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Feature extraction : foundations and applications
Guyon, I., Gunn, S. R., Nikravesh, M., & zadeh, L. N. (2006) · Cited by 1,658
Emotion recognition based on physiological changes in music listening
Kim, J., & Andre, E. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.26 · Cited by 1,082
What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research
Davidson, R. J. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.008 · Cited by 992
Combining SVMs with Various Feature Selection Strategies
Chen, Y.-W., & Lin, C.-J. (2008) · Studies in Fuzziness and Soft Computing · doi:10.1007/978-3-540-35488-8_13 · Cited by 980
Emotion recognition system using short-term monitoring of physiological signals
Kim, K. H., Bang, S. W., & Kim, S. R. (2004) · Medical & Biological Engineering & Computing · doi:10.1007/bf02344719 · Cited by 944
Issues and assumptions on the road from raw signals to metrics of frontal EEG asymmetry in emotion
Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.007 · Cited by 645
Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions
Schmidt, L. A., & Trainor, L. J. (2001) · Cognition and Emotion · doi:10.1080/0269993004200187 · Cited by 617
Sammler, D., Grigutsch, M., Fritz, T., & Koelsch, S. (2007) · Psychophysiology · doi:10.1111/j.1469-8986.2007.00497.x · Cited by 599
FEELTRACE: an instrument for recording perceived emotion in real time
Cowie, R., Douglas‐Cowie, E., Savvidou, S. M., & McMahon, E. R. (2000) · Research Portal (Queen's University Belfast) · Cited by 539
Neuropsychological mechanisms of individual differences in emotion, personality, and arousal.
Heller, W. (1993) · Neuropsychology · doi:10.1037//0894-4105.7.4.476 · Cited by 526
Emotion Assessment: Arousal Evaluation Using EEG’s and Peripheral Physiological Signals
Chanel, G., Kronegg, J., Grandjean, D., & Pun, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11848035_70 · Cited by 382
Short-term emotion assessment in a recall paradigm
Chanel, G., Kierkels, J. J. M., Soleymani, M., & Pun, T. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.005 · Cited by 367
Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004) · Neuroscience and Behavioral Physiology · doi:10.1023/b:neab.0000038139.39812.eb · Cited by 247
Altenmüller, E. (2002) · Neuropsychologia · doi:10.1016/s0028-3932(02)00107-0 · Cited by 228
BALCONI, M., & LUCCHIARI, C. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2007.10.002 · Cited by 205
Brain regions involved in the recognition of happiness and sadness in music
Khalfa, S. P., Schön, D., Anton, J., & Liégeois‐Chauvel, C. (2005) · Neuroreport · doi:10.1097/00001756-200512190-00002 · Cited by 163
Oscillatory brain theory: a new trend in neuroscience
Basar, E., Basar-Eroglu, C., Karakas, S., & Schurmann, M. (1999) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.765190 · Cited by 162
Prefrontal brain electrical asymmetry predicts the evaluation of affective stimuli
Sutton, S. K., & Davidson, R. J. (2000) · Neuropsychologia · doi:10.1016/s0028-3932(00)00076-2 · Cited by 161
Bhattacharya, J., & Petsche, H. (2005) · Signal Processing · doi:10.1016/j.sigpro.2005.07.007 · Cited by 149
Emotion recognition using EEG signals with relative power values and Bayesian network
Ko, K.-E., Yang, H.-C., & Sim, K.-B. (2009) · International Journal of Control Automation and Systems · doi:10.1007/s12555-009-0521-0 · Cited by 140
Utilizing Gamma Band to Improve Mental Task Based Brain-Computer Interface Design
Palaniappan, R. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.881539 · Cited by 107
Schutter, D. J. L. G., Putman, P., Hermans, E. J., & van Honk, J. (2001) · Neuroscience Letters · doi:10.1016/s0304-3940(01)02246-7 · Cited by 103
Support vector machine for EEG signal classification during listening to emotional music
Lin, Y., Wang, C., Wu, T.-L., Jeng, S., & Chen, J. (2008) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2008.4665061 · Cited by 87
Self-citationChanges in EEG alpha power to different disgust elicitors: the specificity of mutilations
Sarlo, M., Buodo, G., Poli, S., & Palomba, D. (2005) · Neuroscience Letters · doi:10.1016/j.neulet.2005.03.037 · Cited by 86
Analysis of positive and negative emotions in natural scene using brain activity and GIST
Zhang, Q., & Lee, M. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.11.007 · Cited by 83
Frontal EEG Responses as a Function of Affective Musical Features
Tsang, C. D., Trainor, L. J., Santesso, D. L., Tasker, S. L., & Schmidt, L. A. (2001) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2001.tb05764.x · Cited by 77
Wu, D., Li, C., & Yao, D. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005915 · Cited by 62
Multilayer perceptron for EEG signal classification during listening to emotional music
Lin, Y., Wang, C., Wu, T.-L., Jeng, S., & Chen, J. (2007) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2007.4428831 · Cited by 56
Self-citationEmotion Recognition Using Physiological and Speech Signal in Short-Term Observation
Kim, J., & André, E. (2006) · Lecture notes in computer science · doi:10.1007/11768029_6 · Cited by 51
Andrade, P. E., & Bhattacharya, J. (2003) · Journal of the Royal Society of Medicine · doi:10.1177/014107680309600607 · Cited by 51
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Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276
Emotions Recognition Using EEG Signals: A Survey
Alarcao, S. M., & Fonseca, M. J. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2714671 · Cited by 1,201
Feature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & Buss, M. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,081
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Zheng, W.-L., Zhu, J.-Y., & Lu, B.-L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2712143 · Cited by 990
A review of emotion recognition using physiological signals
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018) · Sensors · doi:10.3390/s18072074 · Cited by 923
Metadata
- Authors
- Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., & Chen, J.-H.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2010-07
- Volume
- 57
- Issue
- 7
- Pages
- 1798-1806
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2132889650
- Cited by
- 1,021
- References
- 36
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- closed
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