Top

Neuroinformatics

Published in:

14-03-2022 | Original Article

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

Authors: Berke Kılıç, Serap Aydın

Published in: Neuroinformatics | Issue 4/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

previous article ERP Analysis Using a Multi-Channel Matching Pursuit Algorithm

next article Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Aftanas, L. I., Lotova, N. V., et al. (1997). Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponent. Neuroscience Letters, 226(1), 13–16.CrossRef

Alavash, M., Doebler, P., et al. (2015). Is functional integration of resting state brain networks an unspecific biomarker for working memory performance? NeuroImage. https://doi.org/10.1016/j.neuroimage.2014.12.046CrossRefPubMed

Aydin, S., et al. (2016). Emotion recognition with eigen features of frequency band activities embedded in induced brain oscillations mediated by affective pictures. International Journal of Neural Systems. https://doi.org/10.1142/S0129065716500131

Aydin, S., Demirtaş, M., & Tunga, A. (2018). Comparison of hemispheric asymmetry measurements for emotional recordings from controls. Neural Computing and Applications. https://doi.org/10.1007/s00521-017-3006-8CrossRef

Aydin, S., et al. (2019). The impact of musical experience on neural sound encoding performance. Neuroscience Letters. https://doi.org/10.1016/j.neulet.2018.11.034CrossRefPubMed

Aydin, S. (2020). Deep Learning Classification of neuro-emotional phase domain complexity levels induced by affective video film clips. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2019.2959843CrossRefPubMed

Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology. https://doi.org/10.1097/WCO.0b013e32832d93ddCrossRefPubMedPubMedCentral

Bajaj, V., & Pachori, R. B. (2013). Automatic classification of sleep stages based on the time-frequency image of EEG signals. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2013.07.006

Beaty, R. E., Kaufman, S. B., et al. (2016). Personality and complex brain networks: the role of openness to experience in default network efficiency: openness and the default network. Human Brain Mapping. https://doi.org/10.1002/hbm.23065CrossRefPubMed

Candra, H. et al. (2015) Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine, 37th Annual International Conference of IEEE EMBC. https://doi.org/10.1109/EMBC.2015.7320065

Chakraborty, A., & Konar, A. (2009). Brain imaging and psycho-pathological studies on self-regulation of emotion. In: Emotional Intelligence. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-540-68609-5

Chowdary, M. K., et al. (2021). Deep learning-based facial emotion recognition for human-computer interaction applications. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06012-8

Collin, G., van den Heuvel, M. P., et al. (2016). Brain network analysis reveals affected connectome structure in bipolar I disorder. Human Brain Mapping. https://doi.org/10.1002/hbm.23017CrossRefPubMed

Colombetti, G. (2009). From affect programs to dynamical discrete emotions. Philosophy of Psychology. https://doi.org/10.1080/09515080903153600CrossRef

Debnath, R., & Takahashi, H. (2002). Learning capability: classical RBF network vs. SVM with Gaussian kernel, T. Hendtlass, M. Ali (Eds.), Develop. in Application Of Artificial Intelligence Lecture Notes in Computational Science, vol. 2358, Springer.

Doma, V., & Pirouz, M. (2020). A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. Journal of Big Data. https://doi.org/10.1186/s40537-020-00289-7CrossRef

Franciotti, R., Falasca, N. W., Arnaldi, D., et al. (2019). Cortical network topology in prodromal and mild dementia due to Alzheimer’s Disease: Graph Theory applied to resting state EEG. Brain Topography. https://doi.org/10.1007/s10548-018-0674-3CrossRefPubMed

Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience. https://doi.org/10.1038/nrn3901CrossRefPubMed

Greco, A., Valenza, G., & Scilingo, E. P. (2018). Brain Dynamics During Arousal-Dependent Pleasant/Unpleasant Visual Elicitation: An Electroencephalographic Study on the Circumplex Model of Affect. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2018.2879343CrossRef

Gabert-Quillen, C. A., et al. (2015). Ratings for emotion film clips. Behavior Research Methods, 47(3), 773–787.CrossRef

Hatlestad-Hall, C., Bruña, R., et al. (2021). Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy. Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2021.04.008CrossRefPubMed

Haider, B., Duque, A., et al. (2006). Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.5297-05.2006CrossRefPubMed

Kai F., et al. (2014) Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2014.03.007

Katsigiannis, S., & Ramzan, N. (2018). DREAMER, A Database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. of BHI. https://doi.org/10.1109/JBHI.2017.2688239CrossRef

Kim, J., Lee, W. G., et al. (2020). Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy? Clinical Neurology and Neurosurgery. https://doi.org/10.1016/j.clineuro.2020.106119CrossRefPubMedPubMedCentral

Kong, T., Shao, J., et al. (2021). EEG-based emotion recognition using an improved weighted horizontal visibility graph. Sensors. https://doi.org/10.3390/s21051870CrossRefPubMedPubMedCentral

Liao, X., & Vasilakos, A. V. Y. (2017). Small-world human brain networks: Perspectives and challenges. Neuroscience & Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2017.03.018

Liu, Q., & Liu, H. (2021). Criminal psychological emotion recognition based on deep learning and EEG signals. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05024-0CrossRefPubMed

Liu, Y., Zhao, J., & Guo, W. (2018). Emotional roles of mono-aminergic neurotransmitters in major depressive disorder and anxiety disorders. Frontiers in Psychology, 9, 2201. https://doi.org/10.3389/fpsyg.2018.02201CrossRefPubMedPubMedCentral

Lin, Y. P., Duann, J. R., et al. (2010). Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components. NeuroImage, 21, 410–415.

Liu, J., Wu, G., et al. (2020). EEG-based emotion classification using a deep neural network and sparse autoencoder. Frontiers in Systems Neuroscience. https://doi.org/10.3389/fnsys.2020.00043CrossRefPubMedPubMedCentral

Manelis, A., Almeida, J. R. C., et al. (2016). Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach. Brain. https://doi.org/10.1093/brain/aww157CrossRefPubMedPubMedCentral

Meng, C., Brandl, F., Tahmasian, M., et al. (2014). Aberrant topology of striatum’s connectivity is associated with the number of episodes in depression. Brain. https://doi.org/10.1093/brain/awt290CrossRefPubMed

Mehraram, R., Kaiser, M., et al. (2020). Weighted network measures reveal differences between dementia types: An EEG study. Human Brain Mapping. https://doi.org/10.1002/hbm.24896CrossRefPubMed

Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cognit: Sci. https://doi.org/10.1016/j.tics.2011.08.003CrossRef

Miraglia, F., et al. (2018). Brain electroencephalographic segregation as a biomarker of learning. Neural Networks. https://doi.org/10.1016/j.neunet.2018.07.005CrossRefPubMed

Mohammad, A. Z., et al. (2019). Alteration of perceived emotion and brain functional connectivity by changing the musical rhythmic pattern. Experimental Brain Research. https://doi.org/10.1007/s00221-019-05616-wCrossRef

Morris, J. D. (1995). Observations, Sam, The self-assessment manikin; an efficient cross-cultural measurement of emotional response. Journal of Advertising, 35(8), 63–38.

Ostojic, S., Brunel, N., & Hakim, V. (2009). How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. The Journal of Neuroscience, 29, 10234–10253.CrossRef

Okun, M., & Lampl, I. (2008). Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nature Neuroscience. https://doi.org/10.1038/nn.2105CrossRefPubMed

Padial, E. R., & Ibanez-Molina, A. J. (2018). Fractal dimension of EEG signals and heart dynamics in discrete emotional states. Biol: Psych. https://doi.org/10.1016/j.biopsycho.2018.06.008CrossRef

Park, Ch., Lee, H. K., Kweon, Y. S., et al. (2016). Emotion-induced topological changes in functional brain networks. Brain Topography. https://doi.org/10.1007/s10548-015-0449-zCrossRefPubMed

Pereira, M. G., de Oliveira, L., Erthal, F. S., et al. (2010). Emotion affects action: Midcingulate cortex as a pivotal node of interaction between negative emotion and motor signals. Cognitive, Affective, & Behavioral Neuroscience,. https://doi.org/10.3758/CABN.10.1.94CrossRef

Popoli, M., Yan, Z., et al. (2011). The stressed synapse: the impact of stress and glucocorticoids on glutamate transmission. Nature Reviews Neuroscience. https://doi.org/10.1038/nrn3138CrossRefPubMedPubMedCentral

Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopath. https://doi.org/10.1017/S0954579405050340CrossRef

Ren, Y., Cong, F., Ristaniemi, T., et al. (2019). Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. Journal of Neurology. https://doi.org/10.1007/s00415-019-09204-4CrossRefPubMed

Roos, A., et al. (2017). Brain network connectivity in women exposed to intimate partner violence: a graph theory analysis study. Brain Image Behaviour. https://doi.org/10.1007/s11682-016-9644-0

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.10.003CrossRefPubMed

Ruhé, H. G., Mason, N. S., & Schene, A. H. (2007). Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studies. Molecular Psychiatry. https://doi.org/10.1038/sj.mp.4001949CrossRefPubMed

Rule, N. O., Freeman, J. B., & Ambady, N. (2011). Brain, Behavior, and Culture: Insights from Cognition, Perception, and Emotion. In: Han S., Pöppel E. (eds) Culture and Neural Frames of Cognition and Communication. On Thinking. Springer, https://doi.org/10.1007/978-3-642-15423-2

Saeidi, M., Karwowski, W., et al. (2021). Neural decoding of EEG signals with machine learning: A systematic review. Brain Sciences. https://doi.org/10.3390/brainsci11111525CrossRefPubMedPubMedCentral

Schaefer, A., Nils, F., et al. (2010). Assessing the effectiveness of a large database of emotion-eliciting films, A new tool for emotion researchers. Cognition and Emotion, 24(7), 1153–1172.CrossRef

Schmidt, B., & Hanslmayr, S. (2009). Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli. Neuroscience Letters, 460, 237–240.CrossRef

Scholvinck, M. L., Friston, K. J., & Rees, G. (2011). The influence of spontaneous activity on stimulus processing in primary visual cortex. NeuroImage, 80, 297–306.

Shi, Y., Lv, Z., et al. (2020). An improved SIFT algorithm for robust emotion recognition under various face poses and illuminations. Neural Computing and Applications, 32, 9267–9281.CrossRef

Sorinas, J., Grima, M.D., et al. (2019). Identifying suitable brain regions and trial size segmentation for positive/negative emotion recognition. International Journal of Neural Systems. https://doi.org/10.1142/S0129065718500442

Schatzberg, A. F., & Nemeroff, C. B. (2017). The American Psychiatric Publishing Textbook of Psychopharmacology, 5th Edition.

Schurz, M., Maliske, L., & Kanske, P. (2020). Cross-network interactions in social cognition: A review of findings on task related brain activation and connectivity. Cortex. https://doi.org/10.1016/j.cortex.2020.05.006CrossRefPubMed

Shine, J. M., & Poldrack, R. A. (2018). Principles of dynamic network reconfiguration across diverse brain states. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.08.010CrossRefPubMed

Sporns O. (2018). Graph theory methods: applications in brain networks. Dialogues in Clinical Neuroscience. https://doi.org/10.31887/DCNS.2018.20.2/osporns

Stanley, M. L., Simpson, S. L., et al. (2015). Changes in brain network efficiency and working memory performance in aging. PLoS ONE. https://doi.org/10.1371/journal.pone.0123950CrossRefPubMedPubMedCentral

Tamburro, G., di Fronso, S., et al. (2020). Modulation of brain functional connectivity and efficiency during an endurance cycling task: A source-level EEG and Graph Theory approach. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2020.00243CrossRefPubMedPubMedCentral

Tonoyan, Y., Looney, D., Mandic, D.P., et al. (2016). Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach. International Journal of Neural Systems. https://doi.org/10.1142/S0129065716500052

Torres-Valencia, C., et al. (2017) SVM-based feature selection methods for emotion recognition from multimodal data. Journal in Multimple User Interface. https://doi.org/10.1007/s12193-016-0222-y

van den Heuvel, M. P., et al. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29, 7619–7624. https://doi.org/10.1523/jneurosci.1443-09.2009CrossRefPubMed

Vecchioa, F., Tomino, C., et al. (2019). Cortical connectivity from EEG data in acute stroke: A study via graph theory as a potential biomarker for functional recovery. Int: J. of Psycho. https://doi.org/10.1016/j.ijpsycho.2019.09.012CrossRef

Wang, J., Zuo, X., & He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience. https://doi.org/10.3389/fnsys.2010.00016CrossRefPubMedPubMedCentral

Yan, C. et al. (2022) Task-adaptive attention for image captioning, IEEE Transactions on Circuits and Systems for Video Technology, 32(1), 43-51.

Yan, C., et al. (2022). Age-invariant face recognition by multi-feature fusion and decomposition with self-attention. AACM Transactions on Multimedia Computing, Communications, and Applications. https://doi.org/10.1145/3472810

Yuvaraj, R., et al. (2015). Inter-hemispheric EEG coherence analysis in Parkinson’s disease: Assessing brain activity during emotion processing. Journal of Neural Transmission. https://doi.org/10.1007/s00702-014-1249-4CrossRefPubMed

Yan, C., et al. (2021). Deep multi-view enhancement hashing for image retrieval, IEEE Trans on Patt. Analysis and Machine Intelligence, 43(4), 1445–1451.CrossRef

Yan, C. et al. (2020) Depth image denoising using nuclear norm and learning graph model. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(4), 1-17.

Yan, C. et al. (2021) Precise no-reference image quality evaluation based on distortion identification. ACM Transactions on Multimedia Computing, Communications, and Applications. https://doi.org/10.1145/3468872

Wang, F., Yang, J., et al. (2020). Editorial: Neurotransmitters and Emotions. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2020.00021CrossRefPubMedPubMedCentral

Wang, H., Wu, X., & Yao, L. (2020) Identifying cortical brain directed connectivity networks from high-density EEG for emotion recognition. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2020.3006847

Wang, Z., et al. (2022). Functional integration and separation of brain network based on phase locking value during emotion processing. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2020.3001642CrossRef

Zalesky, A., Fornito, A., & Bullmore, E. (2012). On the use of correlation as a measure of network connectivity. NeuroImage, 60(4), 2096–2106.CrossRef

Zuberer, A., et al. (2021). Integration and segregation across large-scale intrinsic brain networks as a marker of sustained attention and task-unrelated thought. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.117610CrossRefPubMed

Title: Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures
Authors: Berke Kılıç
Serap Aydın
Publication date: 14-03-2022
Publisher: Springer US
Published in: Neuroinformatics / Issue 4/2022
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-022-09579-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2022

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

How Machine Learning is Powering Neuroimaging to Improve Brain Health

A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI

Super-resolution Segmentation Network for Reconstruction of Packed Neurites

Auto-encoded Latent Representations of White Matter Streamlines for Quantitative Distance Analysis

Three-Dimensional Spatial Analyses of Cholinergic Neuronal Distributions Across The Mouse Septum, Nucleus Basalis, Globus Pallidus, Nucleus Accumbens, and Caudate-Putamen

Premium Partner