Akademik Veri Yönetim Sistemi
NEUROINFORMATICS, cilt.20, sa.4, ss.863-877, 2022 (SCI-Expanded)
https://link.springer.com/article/10.1007/s12021-022-09579-2
https://pubmed.ncbi.nlm.nih.gov/35286574/
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.
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.