IMR Press / JIN / Volume 18 / Issue 3 / DOI: 10.31083/j.jin.2019.03.601
Open Access Original Research
Statistical algorithms for emotion classification via functional connectivity
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1 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
*Correspondence: (Mohammad Reza Daliri)
J. Integr. Neurosci. 2019, 18(3), 293–297;
Submitted: 29 September 2017 | Accepted: 6 September 2019 | Published: 30 September 2019
Copyright: © 2019 Jahromy et al. Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license

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.

pattern recognition
EEG signals
functional connectivity
mutual information
Pearson correlation
phase-locking value
magnitude square coherence
Figure 1.
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