Akademik Veri Yönetim Sistemi
Yükseköğretim Kurumları Destekli Proje, 2020 - 2021
In the present study, contrasting emotional states (happiness vs
sadness, amusement vs disgust, excitement vs calmness, calmness vs anger, fear
vs anger) are classified with Support Vector Machines with respect to emotional
brain network measures in terms of segregation (clustering coefficients (CC),
transitivity (T)), integration (global efficiency (GE), local efficiency (LE)),
and modularity (Q). Hemispheric dependency levels are estimated by applying
Pearson Correlation (PC) and Spearmann Correlation (SC) to both shorter (6 sec)
and longer (12 sec) EEG segments across the whole cortex. Binary adjacency
matrixes are obtained from dependency estimations with respect to two distinct
thresholds as (1st) 60% of maximum value in dependency matrix and (2nd) the mean
value of the dependency matrix. In addition to statistical one-way Anova tests,
step-wise logistic regression modelling has also been applied to resulting
brain network measures (CC, T, GE, LE, Q). Emotional EEG data was downloaded
from publicly available database called DREAMER. The higher classification
performances were obtained with 2nd threshold for longer segmentation. Five
brain network measures all were found to be highly included in logistic
regression modelling in comparing not only un-pleasant but distinct emotions as
fear (emotional withdrawal) vs anger (emotionally empowering), but also
negative and positive emotions as amusement vs disgust. In other contrasting
emotional states, the impact of each brain network index varied in accordance with
segmentation and method in use (PC vs SC, however the best results are provided
by PC