Research Information System
NEUROINFORMATICS, vol.20, no.4, pp.863-877, 2022 (SCI-Expanded)
https://link.springer.com/article/10.1007/s12021-022-09579-2
https://pubmed.ncbi.nlm.nih.gov/35286574/
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.