Please wait a minute...
Journal of Integrative Neuroscience  2019, Vol. 18 Issue (3): 293-297    DOI: 10.31083/j.jin.2019.03.601
Original Research Previous articles | Next articles
Statistical algorithms for emotion classification via functional connectivity
Fatemeh Zareayan Jahromy1, Atena Bajoulvand1, Mohammad Reza Daliri1, *()
Neuroscience and Neuroengineering Research Lab., Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114 Tehran, Iran
Download:  PDF(529KB)  ( 606 ) Full text   ( 25 )
Export:  BibTeX | EndNote (RIS)      
Abstract  

Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to classify two dominant emotional models, based on valence and arousal. To calculate the functional connectivity between all available pairs of EEG electrodes four different measures, including Pearson’s correlation coefficient, phase-locking value, mutual information, and magnitude square coherence estimation, were used. Three kinds of classifiers were applied to categorize single trials into two emotional states in each emotional model (high/low arousal, high/low valence). This procedure resulted in decoding performance of 68.30% and 60.33% for valence and arousal respectively in test trials which were significantly higher than chance (≈ 50%, t-test, and significance level of 0.05). The results obtained using a phase-locking value approach were significantly better than previous findings on the same data set. These results illustrate that functional connectivity between distinct neural populations can be considered as a neural coding mechanism for intrinsic emotional states.

Key words:  Emotion      pattern recognition      EEG signals      functional connectivity      mutual information      Pearson correlation      phase-locking value      magnitude square coherence     
Submitted:  29 September 2017      Accepted:  06 September 2019      Published:  30 September 2019     
Fund: 
  • School of Cognitive Sciences of Institute for Research in Fundamental Sciences (IPM)
  • Cognitive Sciences and Technologies Council of Iran
*Corresponding Author(s):  Mohammad Reza Daliri     E-mail:  daliri@iust.ac.ir

Cite this article: 

Fatemeh Zareayan Jahromy, Atena Bajoulvand, Mohammad Reza Daliri. Statistical algorithms for emotion classification via functional connectivity. Journal of Integrative Neuroscience, 2019, 18(3): 293-297.

URL: 

https://jin.imrpress.com/EN/10.31083/j.jin.2019.03.601     OR     https://jin.imrpress.com/EN/Y2019/V18/I3/293

[1] Anzellotti, S. and Coutanche, M. N. (2018) Beyond functional connectivity: investigating networks of multivariate representations. Trends in Cognitive Sciences 22, 258-269.
[2] Bahari, F. and Janghorbani, A. (2013) Eeg-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier. In 2013 20th Iranian Conference on Biomedical Engineering (ICBME). IEEE, Iran, 228-233.
[3] Barkhof, E., de Sonneville, L. M., Meijer, C. J. and de Haan, L. (2015) Specificity of facial emotion recognition impairments in patients with multi-episode schizophrenia. Schizophrenia Research: Cognition 2, 12-19.
[4] Bastos, A. M. and Schoffelen, J. M. (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience 9, 175.
[5] Bendat, J. S. and Piersol, A. G. (2011) Random data: analysis and measurement procedures. John Wiley & Sons, New York.
[6] Bravais, A. (1844) Analyse mathématique sur les probabilités des erreurs de situation d'un point. Impr. Royale. Paris.
[7] Chung, S. Y. and Yoon, H. J. (2012) Affective classification using Bayesian classifier and supervised learning. In 2012 12th International Conference on Control, Automation and Systems. IEEE, Portugal, 1768-1771.
[8] Cover, T. M. and Thomas, J. A. (1991) Elements of information theory. John Wiley & Sons. New York.
[9] Davidson, R. (1979) Frontal versus perietal EEG asymmetry during positive and negative affect. Psychophysiology 16, 202-203.
[10] Galton, F. (1886) Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland 15, 246-263.
[11] Jenke, R., Peer, A. and Buss, M. (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Transactions on Affective Computing 5, 327-339.
[12] Kensinger, E. A. (2004) Remembering emotional experiences: The contribution of valence and arousal. Reviews in the Neurosciences 15, 241-252.
[13] Khosrowabadi, R., Quek, C., Ang, K. K. and Wahab, A. (2014) ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal. IEEE Transactions on Neural Networks and Learning Systems 25, 609-620.
[14] Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A. and Patras, I. (2012) Deap: a database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing 3, 18-31.
[15] Lorig, T. S. and Schwartz, G. E. (1988) Brain and odor: I. Alteration of human EEG by odor administration. Psychobiology 16, 281-284.
[16] Naji, M., Firoozabadi, M. and Azadfallah, P. (2015) Emotion classification during music listening from forehead biosignals. Signal, Image and Video Processing 9, 1365-1375.
[17] Naser, D. S. and Saha, G. (2013) Recognition of emotions induced by music videos using DT-CWPT. In 2013 Indian Conference on Medical Informatics and Telemedicine (ICMIT). IEEE, India, 53-57.
[18] Niso, G., Bruña, R., Pereda, E., Gutiérrez, R., Bajo, R., Maestú, F. and del-Pozo, F. (2013) HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics 11, 405-434.
[19] Pearson, K. (1895) Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 58, 240-242.
[20] Shannon, C. E. and Weaver, W. (1998) The mathematical theory of communication. University of Illinois Press, Champaign, Illinois.
[21] Sourina, O. and Liu, Y. (2011) A fractal-based algorithm of emotion recognition from EEG using arousal-valence model. BIOSIGNALS 2011, Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Rome, Italy, 209-214.
[22] Tass, P., Rosenblum, M., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., Schnitzler, A., Freund, H. (1998) Detection of n: m phase locking from noisy data: application to magnetoencephalography. Physical Review Letters 81, 3291-3294
[23] Torres-Valencia, C. A., Garcia-Arias, H. F., Lopez, M. A. A. and Orozco-Gutiérrez, A. A. (2014) Comparative analysis of physiological signals and electroencephalogram (eeg) for multimodal emotion recognition using generative models. In 2014 XIX Symposium on Image, Signal Processing and A.pngicial Vision. IEEE, Colombia, 1-5.
[24] Wang, X. W., Nie, D. and Lu, B. L. (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94-106.
[25] Yang, Y.H. and Chen, H.H. (2011) Prediction of the distribution of perceived music emotions using discrete samples. IEEE Transactions on Audio, Speech, and Language Processing 19, 2184-2196.
[26] Zhang, Q. and Lee, M. (2010) A hierarchical positive and negative emotion understanding system based on integrated analysis of visual and brain signals. Neurocomputing, 73, 3264-3272.
[27] Zhuang, X., Rozgić, V. and Crystal, M. (2014) Compact unsupervised EEG response representation for emotion recognition. In IEEE-EMBS international conference on Biomedical and Health Informatics (BHI). IEEE, Spain, 736-739.
[1] Rekha Sahu, Satya Ranjan Dash, Lleuvelyn A Cacha, Roman R Poznanski, Shantipriya Parida. Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques[J]. Journal of Integrative Neuroscience, 2020, 19(1): 1-9.
[2] Raheel Zafar, Abdul Qayyum, Wajid Mumtaz. Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders[J]. Journal of Integrative Neuroscience, 2019, 18(3): 217-229.
[3] Marie Charlotte Gandolphe, Jean Louis Nandrino, Gérald Delelis, Claire Ducro, Audrey Lavallee, Xavier Saloppe, Ahmed A. Moustafa, Mohamad El Haj. Positive facial expressions during retrieval of self-defining memories[J]. Journal of Integrative Neuroscience, 2018, 17(3): 281-286.
[4] Abolfazl Alipour, Sahar Seifzadeh, Hadi Aligholi, Mohammad Nami. QEEG-based neural correlates of decision making in a well-trained eight year-old chess player[J]. Journal of Integrative Neuroscience, 2018, 17(3): 297-306.
[5] Shiva Khoshnoud, Mohammad Ali Nazari, Mousa Shamsi. Functional analysis of ADHD in children using nonlinear features of EEG signals[J]. Journal of Integrative Neuroscience, 2018, 17(1): 11-18.
No Suggested Reading articles found!