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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
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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     
  • 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:

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.

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