Abstract
The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (\(31.5-60.5~Hz\)) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.
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Data availability and information sharing statement
Both EEG data and emotional music clips are openly available and are distributed along with the a data repository in OPENNEURO portal with the number of ds002721 as described in references Daly (2014, 2015)https://openneuro.org/datasets/ds002721/versions/1.0.0.
References
Aftanas LI et al (2006) Neurophysiological correlates of induced discrete emotions in humans: an individually oriented analysis. Neurosci Behav Physiol. https://doi.org/10.1007/s11055-005-0170-6
Altenmüller E, Schürmann K et al (2002) Hits to the left, flops to the right different emotions during listening to music are reflected in cortical lateralisation patterns. Neuropsychologia 40:2242–2256
American psychological association (2001) publication manual of the American, psychological association, 5th edn. APA, Washington
Arjmand HA et al (2017) Emotional responses to music: shifts in frontal brain asymmetry mark periods of musical change. Front Psychol 8:2044. https://doi.org/10.3389/fpsyg.2017.02044
Aydın S (2010) Determination of autoregressive model orders for seizure detection. T J Elect Eng Comp 18(1):23–30
Aydın S et al (2018) Cortical correlations in wavelet domain for estimation of emotional dysfunctions. Neur Comput Appl 30:1085–1094
Aydın S (2022) Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level. Cogn Neurodyn. https://doi.org/10.1007/s11571-022-09843-w
Baccala LA et al (2001) Partial directed coherence: a new conception in neural structure determination. Biol Cybern 84:463–474
Barrett LF et al (2007) The experience of emotion. Ann Rev Psychol 58:373–403
Barrett LF (2011) Was Darwin wrong about emotional expressions? Curr Dir Psychol Sci. https://doi.org/10.1177/0963721411429125
Barrett LF (2012) Emotions are real. Emotion. https://doi.org/10.1037/a0027555
Batbaatar E et al (2019) Semantic-emotion neural network for emotion recognition from text. IEEE Access 45:7111866–111878
Bekkedal MY et al (2011) Human brain EEG indices of emotions: delineating responses to affective vocalizations by measuring frontal theta event-related synchronization. Neurol Biob Rev 35(9):1959–1970
Bianchi AM et al (2013) Frequency-based approach to the study of semantic brain networks connectivity. J Neurosci Math 212(2):181–189
Bigand E et al (2005) Multidimensional scaling of emotional responses to music: the effect of musical expertise and of the duration of the excerpts. Cognit Emot 19:1113–1139
Blinowska KJ, et al. (2006) Multivariate signal analysis by parametric models: Handbook of Time Series Analysis
Blinowska KJ et al (2013) Application of directed transfer function and network formalism for the assessment of functional connectivity in working memory task. Phys Eng Sci. https://doi.org/10.1098/rsta.2011.0614
Blood AJ et al (1999) Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nat Neurosci 2:382–387
Bo H et al (2019) Music-evoked emotion recognition based on cognitive principles inspired EEG temporal and spectral features. Int J Mach Learn Cybernet 10:2439–2448
Bordier C, Nicolini C, Bifone A (2017) Graph analysis and modularity of brain functional connectivity networks: searching for the optimal threshold. Front Neurosci 11:441. https://doi.org/10.3389/fnins.2017.00441
Boucher O et al (2014) Spatiotemporal dynamics of affective picture processing revealed by intracranial high-gamma modulations. Hum Brain Mapp 36:16–28
Bröhl F, Kayser C (2021) Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes. Neuroimage 233:117958
Bueno JLO, Ramos D (2007) Musical mode and estimation of time. Percept Mot Skills 105:1087–1092
Bullmore E et al (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Rev Neuro, Nat. https://doi.org/10.1038/nrn2575
Chang C., et al.(2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, https://doi.org/10.1016/j.neuroimage.2009.12.011
Cheng J et al (2021) Emotion recognition from multi-channel EEG via deep forest. IEEE JBHI. https://doi.org/10.1109/JBHI.2020.2995767
Chen D, Miao R et al (2021) Sparse granger causality analysis model based on sensors correlation for emotion recognition classification in Electroencephalography. Front Comp Neurosci 15:874
Cohen JR et al (2016) The segregation and integration of distinct brain networks and their relationship to cognition. J Neurosci 36:12083–12094
Daly I et al (2014) Neural correlates of emotional responses to music: an EEG study. Neurosci Lett 573:52–7
Daly I et al (2015) Music-induced emotions can be predicted from a combination of brain activity and acoustic features. Brain Cognit. https://doi.org/10.1016/j.bandc.2015.08.003
Daly I et al (2020) Neural and physiological data from participants listening to affective music. Sci Data 7:177. https://doi.org/10.1038/s41597-020-0507-6
Dennis TA, Solomon B (2010) Frontal EEG and emotion regulation: electrocortical activity in response to emotional film clips is associated with reduced mood induction and attention interference effects. Biol Psychol 85:456–464
Droit-Volet S et al (2010) Time flies with music whatever its modality. Acta Psychol 135:226–236
Eerola T et al (2010) A comparison of the discrete and dimensional models of emotion in music. Psych Music 39(1):18–49
Ekman P et al (2011) What is meant by calling emotions basic. Emot Rev. https://doi.org/10.1177/1754073911410740
Fallani FDV et al (2017) A topological criterion for filtering information in complex brain networks. Plos Comp Biol 13(1):e1005305
Ferdek MA et al (2016) Depressive rumination and the emotional control circuit. Cogn, Aff Behav Neurosci 16(6):1099–1113
Finc K et al (2020) Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun 11:2435
Flores-Gutierrez EO et al (2007a) Metabolic and electric brain patterns during pleasant and unpleasant emotions induced by music masterpieces. Int J Psychophysiol 65:69–84
Flores-Gutirréez E, Díaz JL et al (2007) Metabolic and electric brain patterns during pleasant and unpleasant emotions induced by music masterpieces. Int J Psychophysiol 65:69–84. https://doi.org/10.1016/j.ijpsycho.2007.03.004.71
Fong AHC et al (2019) Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. Neuroimage 188:14–25
Fontaine JR et al (2007) The world of emotions is not two-dimensional. Psychol Sci 18(12):1050–1057
Franaszczuk PJ, Bergey GJ et al (1994) Analysis of mesial temporal seizure onset and propagation using the directed transfer function method. Electroencephalogr Clin Neurophysiol 91:413–427. https://doi.org/10.1016/0013-4694(94)90163-5
Fransson P et al (2018) Brain network segregation and integration during an epoch-related working memory fmri experiment. Neuroimage 178:147–161
Gaxiola JA et al (2018) Using the partial directed coherence to assess functional connectivity in electroencephalography data for brain-computer interfaces. IEEE Trans Cognit Dev Sys 10(3):776–783
Grefkes C et al (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. https://doi.org/10.1093/brain/awr033
Haider B et al (2006) Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J Neurosci 26:4535–4545
Hamann S (2012) Mapping discrete and dimensional emotions onto the brain: controversies and consensus. Trends Cognit Sci. https://doi.org/10.1016/j.tics.2012.07.006
Hansen PC (2007) Regularization Tools Version 4.0 for Matlab 7.3. Num Algor 46:189–194
He B et al (2011) eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Meth 195(2):261–269
Henry N, Kayser D, Egermann H (2021) Music in mood regulation and coping orientations in response to covid-19 lockdown measures within the united kingdom. Front Psychol 12:647879. https://doi.org/10.3389/fpsyg.2021.647879
Hereld DC (2019) Music as a regulator of emotion: three case studies. Music Med https://doi.org/10.47513/MMD.V11I3.644
Hilsdorf M, Bullerjahn C (2021) Modulation of negative affect predicts acceptance of music streaming services while personality does not. Front Psychol 12:659062. https://doi.org/10.3389/fpsyg.2021.659062
Hu X et al (2017) EEG correlates of ten positive emotions. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2017.00026
Huang D et al (2016) Combining partial directed coherence and graph theory to analyse effective brain networks of different mental tasks. In Hum. Neurosci, Front. https://doi.org/10.3389/fnhum.2016.00235
Jackson DC et al (2003) Now you feel it now you don’t: frontal brain electrical asymmetry and individual differences in emotion regulation. Psychol Sci 14:612–617
Jiang P et al (2019) Parallelized convolutional recurrent neural network with spectral features for speech emotion recognition. IEEE Access 7:90368–90377
Juslin PN, Sloboda JA (eds) (2010) Handbook of music and emotion: theory. research and applications. Oxford University Press, New York, NY
Juslin PN, Vastfjall D (2008) Emotional responses to music: the need to consider underlying mechanisms. Behav Brain Sci 31:559–621
Kaminski M, Blinowska KJ et al (1997) Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. Electroencephalogr Clin Neurophysiol 102:216–227. https://doi.org/10.1016/S0013-4694(96)95721-5
Karmonik C, Brandt A, et al. (2013). Graph theoretical connectivity analysis of the human brain while listening to music with emotional attachment: Feasibility study. Annual Int. Conf. of the IEEE Eng. in Med. and Bio. Society, 6526-6529. https://doi.org/10.1109/EMBC.2013.6611050
Kılıç B et al (2022) Classification of contrasting discrete emotional states indicated by EEG based Graph Theoretical network measures. Neuroinformation. https://doi.org/10.1007/s12021-022-09579-2
Koelsch S et al (2006) Investigating emotion with music: an fMRI study. Human Brain Mapp 27:239–250
Koelsch S (2018) Investigating the neural encoding of emotion with music. Neuron 98(6):1075–1079
Koelsch S, Fritz T, Schlaugh G (2008) Amygdala activity can be modulated by unexpected chord functions during music listening. Neuroreport 19:1815–1819
Korzeniewska A et al (2003) Determination of information flow direction among brain structures by a modified directed transfer function method. J Neurosci Meth 125(1):195–207
Korzeniewska A et al (2014) Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG. NeuroImage 101:96–113
Kudo M et al (1999) Multidimensional curve classification using passing-through regions. Pattern Rec Lett 20(11–13):1103–1111
LeDoux J (2000) Emotion circuits in the brain. Annual Rev Neurosci 23:155–184
Levitin DJ et al (2012) Musical rhythm spectra from Bach to Joplin obey a 1/f power law. Proc Nat Acad Sci 109:3716–3720. https://doi.org/10.1073/pnas.1113828109
Li THS et al (2019a) CNN and LSTM based facial expression analysis model for a humanoid robot. IEEE Access 7:93998–94011
Li T, Li G et al (2020) Analyzing brain connectivity in the mutual regulation of emotion-movement using bidirectional Granger Causality. Front Neurosci 6(14):369. https://doi.org/10.3389/fnins.2020.00369
Li P, Liu H et al (2019b) EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Trans on BME 66(10):2869–2881
Li Y, Zheng W et al (2018) A bi-hemisphere domain adversarial neural network model for EEG emotion recognition IEEE Trans on Affec. Comp 12(2):494–504
Ligeza TS et al (2017) Cognitive conflict increases processing of negative, task-irrelevant stimuli. Int J Psychop 12:126–135
Lindquist KA et al (2012) The brain basis of emotion: a meta-analytic review. Behav Brain Sci 35(3):121–143
Liu YJ et al (2017) Real-time movie-induced discrete emotion recognition from EEG signals. Proc IEEE Trans Aff Comp 9(4):550–562
Lynall ME et al (2010) Functional connectivity and brain networks in schizophrenia. J Neuro 30(28):9477–87
Miraglia F et al (2018) Brain electroencephalographic segregation as a biomarker of learning. Neural Netw 106:168–174
Mu J et al (2018) Abnormal interaction between cognitive control network and affective network in patients with end-stage renal disease. Brain Imag Behav 12(4):1099–1111
Murphy FC et al (2003) Functional neuroanatomy of emotions: a meta-analysis. Cognit Affect Behav Neuro 3:207–233
Neumaier A et al (2001) Estimation of parameters and eigenmodes of multivariate AR models. ACM Trans Math Soft 27(1):27–57
Okun M et al (2008) Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci 11:535–537
Panksepp J (2010) Affective consciousness in animals: perspectives on dimensional and primary process emotion approaches. Proc Biol Sci 277(1696):2905–2907
Phan KL et al (2002) Functional neuroanatomy of emotion: a metaanalysis of emotion activation studies in PET and fMRI. NeuroImage 16:331–348
Rai S et al (2015) Determining minimum spanning tree in an undirected weighted graph. Int Conf on Adv in Comp Eng and App 2015:637–642
Rubinov M et al (2009) Symbiotic relationship between brain structure and dynamics. BMC Neurosci. https://doi.org/10.1186/1471-2202-10-55
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52(3):1059–1069
Schllögl A. (2002) Time Series Analysis, A toolbox for the use with Matlab. 1996-2002
Schmidt LA, Trainor LJ (2001) Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cognit Emot 15:487–500
Schneider TA et al (2001) Algorithm 808: ARfit-A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27:58–65
Sinex D, Guzik H et al (2003) Responses of auditory nerve fibers to harmonic and mistuned complex tones. Hear Res 182:130–139
Smith K et al (2015) Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis, 37th Conf. IEEE EMBC. https://doi.org/10.1109/EMBC.2015.7318983
Sporns O et al (2004) The small world of the cerebral cortex. Neuroinformatics. https://doi.org/10.1385/NI:2:2:145
Sprent P (1988) Applied nonparametric statistical methods. Springer, Cham
Stam C.J., et al.(2007). Graph theoretical analysis of complex networks in the brain. Nonl Biomed Phys. doi: https://doi.org/10.1186/1753-4631-1-3
Stam CJ et al (2007) Graph theoretical analysis of complex networks in the brain. Biomed Phys Nonl. https://doi.org/10.1186/1753-4631-1-3
Stam CJ et al (2007) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex. https://doi.org/10.1093/cercor/bhj127
Sun S et al (2019a) Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. Eng, IEEE Trans Neural Syst Reh. https://doi.org/10.1109/TNSRE.2019.2894423
Sun S, Li X et al (2019b) Graph theory analysis of functional connectivity in major depression disorder With high-density resting state EEG data. IEEE Trans Neural Syst Rehabil Eng 27(3):429–439. https://doi.org/10.1109/TNSRE.2019.2894423
Supriya S et al (2016) Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4:6554–6566
Tao W et al (2020) EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans Aff Comp. https://doi.org/10.1109/TAFFC.2020.3025777
Tian Y et al (2019) A complementary method of PCC for the construction of scalp resting-state EEG connectome. IEEE Access. https://doi.org/10.1109/access.2019.2897908
Tijms BM et al (2013) Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging 34(8):2023–36
Tsai ML, Wang CC et al (2022) Resting-State EEG functional connectivity in children with rolandic spikes with or without clinical seizures. Biomedicines 10(7):1553. https://doi.org/10.3390/biomedicines10071553
van Wijk BC et al (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5(10):e13701
Vytal K et al (2010) Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis. J Cognit Neurol 22:2864–2885
Wang F et al (2018) EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network. RSC Adv. https://doi.org/10.1039/c8ra04846k
Wilke C et al (2010) Neocortical seizure foci localization by means of a directed transfer function method. Epilepsia 51(4):564–572
Yang K et al (2020) High gamma band EEG closely related to emotion: Evidence from functional network. Front Hum Neurosci 14:89
Yin Z et al (2017) Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Signal Proc Cont, Biomed. https://doi.org/10.1016/j.bspc.2016.08.013
Zhao G et al (2018) Frontal EEG asymmetry and middle line power difference in discrete emotions. Front Behav Neurosci 12:225
Zheng WL, Zhu JY et al (2017) Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans Affect Comp. https://doi.org/10.1109/TAFFC.2017.2712143
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Aydın, S., Onbaşı, L. Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger. Cogn Neurodyn 18, 49–66 (2024). https://doi.org/10.1007/s11571-023-09931-5
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DOI: https://doi.org/10.1007/s11571-023-09931-5