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.
Discussion and Conclusion:
In the present study, contrasting discrete emotional states have been classified with SVMs in accordance with two different kernels (RBFs vs GKs) with respect to methodological variables such as dependency methods (PC vs SC), EEG segmentation largeness (6 sec vs 12 sec), threshold definition in transforming the dependency levels into binary adjacency data (60%max and the mean). The group differences are also obtained using statistical one-way Anova tests and logistic regression modeling. The most useful results are provided by PC in larger segments in accordance with the specified threshold as the mean when RBFs are used as the kernels in SVMs. The main result of the study is to show the close correlations between emotional arousal of affective video film clips and Graph Theoretical complex network measures in terms of both segregation and integration.
Methodologically, superior performance of PC can be explained by following items: (1) PC is based on the linear relationship, while SC is based on the monotonic relationships between two variables, (2) PC can work with un-processed variables, while SC can work with rank-ordered variables. Thus, PC can be proposed for estimation of statistical dependency between EEG segments across the cortex in classifying emotions based on Graph Theoretical network analysis.
SVMs have been frequently used to classify EEG based emotional groups Torres-Valencia (2017); Doma and Pirouz (2020); Saeidi and Karwowski (2021). Since, architecture of SVMs is based on statistical learning theory that provides finding the best decision function, kernel specification affects its performance Debnath and Takahashi (2002). In the present study, RBFs are found to be superior to GKs as reported in references Bajaj and Pachori (2013); Kai (2014); Aydin (2019). In conclusion, classification performance of SVM depends on both feature space, i.e. spectral distribution of the features and kernels.
Regarding the identical dataset DREAMER, the best classification performance was obtained in analysis of shorter EEG segments (6 sec) identified by single-channel phase domain local complexity estimations in Aydin (2020), however, the recent useful results are provided in larger EEG segments (12 sec) represented by global brain network indices across the whole cortex. In Table 4, large variety of window length is given in emotion research based on EEG analysis: Due to decisive differences in both emotional stimulus types (visual, auditory, audio-visual) and experimental paradigms (inter-stimulus-inter duration, stimulus display/presentation time, recording equipment (number of recording electrodes)) as well as state definition (pleasantness in accordance with valance scores, negativity in accordance with arousal scores, discrete emotional state), the proposed segmentation length differs from each other. Moreover, emotional features have been extracted from EEG measurements by examining several methods depending on the goal such as observing neural activities at EEG recording placements (local analysis), quantifying inter-hemispheric neural communications (regional EEG analysis) and understanding the brain network mechanism across the whole cortex (global EEG analysis).
While defining this state can be based on the valence scores of the stimuli (pleasant-unpleasant), researchers have also combine the emotional states, placed in the identical quarter of circumplex emotion model (Fig. 4), in a single category (negative-positive). However, each discrete emotional states have been considered as a different state in accordance with discrete emotion model (Fig. 4) and then each discrete emotional state is identified by EEG based Graph Theoretical network measures in the present study, since both nerve action potentials superimposed by post-synaptic potentials including excitatory and inhibitory neurotransmitter activities are embedded in EEG series. Apart from brain-computer interfaces, it is crucial to assign each emotion as a separate discrete state in accordance with discrete emotion model (see Fig. 4), even if they have similar arousal-valance scores, in not only understanding the functional brain mechanism but also recognizing particular neuropsychiatric diseases characterized by perceptional deficit in computational and behavioural neuroscience. Several emotions (such as fear and anger) are considered as members of a single group in accordance with the identical quarter of arousal-valance dimensions in accordance with circumplex emotion model (see Fig. 4), although neurotransmitter activities embedded in EEG series are quite different in every emotional state.
Therefore, emotions are categorized into basic emotions and their derivatives mentioned as mixed emotions. It’s known that neurotransmitters have a great impact on emotion forming, behaviour and psychiatric disorders Ruhé et al. (2007); Liu et al. (2018); Wang et al. (2020). Three neurotransmitters of serotonin, dopamine and norephinephrine are presented as the most important neurotransmitters in psychopharmacology Schatzberg and Nemeroff (2017). The brief role of them is to change (increase/decrease) post-synaptic potentials of pyramidal nerve cells in the brain. Once temporal and spatial summation of synchronized post-synaptic potentials exceeds the threshold level, nerve action potential is generated and then propagated along with secondary neurons. Both generation and propagation of action potentials provide neural information flow across the cortex. Therefore, external stimulus type (auditory, acoustic, visual, audio-visual, somatosensorial, etc.), duration (time-locked, short duration, long duration), intensity (low-moderate-high) and content (emotional/affective, attentional, working memory, recalling memory, etc.) are all sources in releasing the specified neurotransmitters. Therefore, time-varying post-synaptic potentials including both excitatory and inhibitory actions driven by particular neurotransmitters are embedded in EEG segments. The current findings reveal the close association between emotional arousal score of external video stimuli and functional brain connectivity mechanism due to varying neurotransmitter release at pyramidal nerves.