Akademik Veri Yönetim Sistemi
NEURAL COMPUTING & APPLICATIONS, cilt.30, ss.1341-1351, 2018 (SCI-Expanded)
https://link.springer.com/article/10.1007%2Fs00521-017-3006-8
ABSTRACT:
Four asymmetry
measurements (conventional coherence function (CCF), cross wavelet correlation
(CWC), phase lag index (PLI), and mean phase coherence (MPC)) have been
compared to each other for the first time in order to recognize emotional
states (pleasant (P), neutral (N), unpleasant (UP)) from controls in EEG
sub-bands (delta (0-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16-32 Hz),
gamma (32-64 Hz)) mediated by affective pictures from the International
Affective Picture Archiving System (IAPS). Eight emotional features, computed
as hemispheric asymmetry between eight electrode pairs (Fp1 - Fp2, F7 - F8, F3
- F4, C3 - C4, T7 - T8, P7 - P8, P3 - P4, and O1 - O2), have been classified by
using data mining methods. Results show that inter-hemispheric emotional
functions are mostly mediated by gamma. The best classification is provided by
a neural network classifier, while the best features are provided by CWC in
time-scale domain due to non-stationary nature of electroencephalographic (EEG)
series. The highest asymmetry levels are provided by pleasant pictures at
mostly anterio-frontal (F3 - F4) and central (C3 - C4) electrode pairs in
gamma. Inter-hemispheric asymmetry levels are changed by each emotional state
at all lobes. In conclusion, we can state the followings: (1) Nonlinear and
wavelet transform-based methods are more suitable for characterization of EEG;
(2) The highest difference in hemispheric asymmetry was observed among emotional
states in gamma; (3) Cortical emotional functions are not region-specific,
since all lobes are effected by emotional stimuli at different levels; and (4)
Pleasant stimuli can strongly mediate the brain in comparison to unpleasant and
neutral stimuli.
DISCUSSION AND CONCLUSION:
The results showed that the highest performance was
obtained for eight electrode pairs, which were placed on two hemispheres
symmetrically (Fp2 − Fp1, F3
− F4, C3 − C4, P3 − P4, O1
− O2, F7 − F8, T3 − T4, T5
− T6), when CWC was applied to gamma of non-averaged emotional EEG
oscillations of 6 s. Our findings were listed as follows: (1) The best phase
synchronization measurement was CWC; (2) Gamma was found to be highly sensitive
to emotional states (P, N, and UP); (3) The highest brain activation was
induced by pleasant pictures rather than other pictures in either N or UP; (4)
The highest phase coherence values produced by gamma were observed at
anterio-frontal (F3 − F4) and central (C3 − C4)
regions; (5) Two contrasting emotional states (P and UP) were distinguished
from each other clearly, whereas the emotional state of UP was not clearly
distinguished from the state of N based on both phase synchronization level and
inter-trial variability of the phase difference in gamma; (6) The level of
hemispheric phase synchronization was changed by the state of emotion over the
whole scalp; and (7) The most useful data mining method was
MultilayerPerceptron which is a NN classifier.
In
conclusion, we can propose the use of CWC to extract emotional features from
high-frequency sub-band of non-averaged EEG oscillations. Considering two
non-averaged EEG oscillations, mediated by affective pictures and recorded from
right and left hemispheric regions, MPC and PLI can measure inter-trial
variability of the phase difference, while CWC can measure phase
synchronization level. Results show that high performance for classification of
emotional states can be obtained when the level of phase synchronization is
used to estimate strong features instead of the possible inter-trial
variability of phase difference. Comparing CWC to CCF, we should consider the
basic difference between FT and WT: It is known that high resolution can not be
obtained by using FT in both time and frequency, whereas we can perfectly
determine the useful resolution by using WT in time-frequency space. Therefore,
FT-based CCF can not quantify the level of phase synchronization in comparison
to WT-based CWC to investigate functional brain connectivity induced by static
and visual emotional stimuli of 6 s. EEG oscillations can be considered as
summation of inhibitory and excitatory postsynaptic nerve action potentials,
because intrinsic cell currents are mostly activated by ionic membrane channels
as a result of ongoing interactions between the thalamus and cortex. Both
firing rate of individual neurons and dynamic interplay among neurons within
the local neuronal assembly have the crucial role in processing neural
information [54,55,56]. Considering right and
left hemispheres, neural populations can oscillate simultaneously depending on
depolarization phase initiated by nerve cell [57]. The brain is capable
of selectively integrate several neural populations during cognitive tasks in
gamma [58, 62]. Gamma is also found to
be coherent with spatiotemporal magnetic field [59, 63, 64]. In recent years, gamma
has been frequently investigated to understand the mechanism of highly ordered
cognitive functions such as emotional processes due to its high sensitivity to
emotional stimuli by means of coherence [34, 60, 61, 66]. Our new results are
compatible with these studies. Gamma has also been found to be sensitive to
phase synchronization in detecting emotional responses of patients with
alexithymia [65]. In future work, we are
going to compare controls to patients with schizophrenia with respect to the
same experimental paradigm for emotion recognition based on both single-channel
EEG complexity estimations and two-channel inter-hemispheric EEG synchronization
estimations in phase.
In
conclusion, inter-hemispheric neuronal functions, generated by long-duration
affective stimuli, can be analyzed by examining CWC in gamma. The results
support that there is a strong relationship between gamma-specific
inter-neuronal functions and emotional states at almost each brain region,
while anterio-frontal and central regions show relatively higher hemispheric
asymmetry levels in gamma.