Neural Comput & Applic (2018) 30:1085–1094
https://doi.org/10.1007/s00521-016-2731-8
ORIGINAL ARTICLE
Cortical correlations in wavelet domain for estimation
of emotional dysfunctions
Serap Aydın1
•
Serdar Demirtaş 2 • Sinan Yetkin3
Received: 8 August 2016 / Accepted: 19 November 2016 / Published online: 3 December 2016
Ó The Natural Computing Applications Forum 2016
Abstract In the present study, the level of nonlinear interhemispheric synchronization has been estimated by using
wavelet correlation (WC) method for detection of emotional dysfunctions. Due to non-stationary nature of EEG
series in addition to the assumption that the high-frequency
band is possibly associated with emotional activation, WC
has been applied to five distinct frequency band activities
(fba) (Delta: 0:5 4 Hz, Theta: 4 8 Hz, Alpha: 8 16 Hz,
Beta: 16 32 Hz, Gamma: 32 64 Hz) embedded in nonaveraged single-trial EEG series mediated by convenient
affective pictures from International Affective Picture System. Experimental data were collected from both healthy
controls and patients, diagnosed with first-episode psychosis, through a 16-channel EEG cap. WC estimations,
which are computed for eight electrode pairs (pre-frontal,
anterio-frontal, central, parietal, occipital, posterio-frontal,
anterio-temporal, posterio-temporal), in accordance with
each specified fba and emotional state (pleasant, unpleasant, neutral) have been classified by using Least
Squares Support Vector Machines with tenfold cross-validation to distinguish controls from patients. Results show
that the highest classification accuracies of 88.06, 86.39,
83.89% are obtained in Gamma with respect to neutral, unpleasant, and pleasant stimuli, respectively. In each group
& Serap Aydın
drserapaydin@hotmail.com; serap.aydin@eng.bau.edu.tr
1
Department of Biomedical Engineering, Faculty of
Engineering and Natural Sciences, University of Bahçeşehir,
34353 Beşiktaş, Istanbul, Turkey
2
Department of Biophysics, Faculty of Medicine, University
of Health Sciences, Ankara, Turkey
3
Department of Psychiatry, Faculty of Medicine, University of
Health Sciences, Ankara, Turkey
(controls and patients), the largest WCs are observed at
anterio-frontal and central lobes; however, controls generate the high WC in response to pleasant stimuli, whereas
the patients generate the high WC in response to neutral
stimuli in Gamma. In conclusion, fronto-central lobes are
the most activated brain regions during emotional stimulation by means of inter-hemispheric correlation. Gamma is
the most sensitive fba to visual affective pictures. Emotional dysfunctions are found to be characterized by
decreased WC in pleasant state, increased WC in neutral
state in Gamma.
Keywords Emotion Hemispheric asymmetry Wavelet
correlation Gamma
1 Introduction
Wavelet coherence is a time-frequency method which
estimates the phase lag between two non-stationary time
series in terms of wavelet transform (WT) [1, 2]. Due to
requirement of Fourier transform (FT) on stationary
assumption about EEG, this method has been used as better
alternative to conventional coherence function based on FT
to estimate the degree of cortical information flow between
two brain regions [3–7]. WT has also been used in several
algorithms for emotion recognition based on single-channel EEG analysis [8, 9]. Different from those studies,
Wavelet correlation (WC) has been applied to five frequency band activities (fba) of EEG series, mediated by
affective pictures from IAPS [10], for classification of
controls and patients diagnosed with first-episode psychosis (FEP) in the present study.
Schizophrenia has been considered as the most devastating psychiatric disorder. Therefore, extraction of
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Neural Comput & Applic (2018) 30:1085–1094
quantitative bio-markers for early detection of this important disorder has become attractive field. As well, EEG
analysis could provide the appropriate and individual
treatment of beginning phase of schizophrenia so named
FEP. Instead of neuro-imaging modalities, EEG measurement provides the more sensitive analysis to understand
cognitive brain functions during working memory activated by both auditory [11–15] and visual stimuli [16] as
well as mental task [17]. The affective pictures from
international database IAPS have been used for the first
time in order to detect beginning phase of schizophrenia in
the present study. Spectral power in Alpha has been frequently obtained to differ discrete emotions to each other
[18–20]. Mostly, emotion recognition has been studied on
EEG analysis including event-related potential synchronization in Gamma power [21–23].
Although, electro-myo-graphy (EMG) [29] and neuroimaging modalities [30] were used for emotion recognition,
EEG analysis was commonly found to be the most useful
nervous system parameters [24–28]. In fact, cognition and
emotion are commonly integrated by autonomic control
system for both generation and representation of visceral
changes in the human body. In detail, emotion is defined as
neurological inter-correlations between thalamus, cortex,
and limbic system [31–35]. In studies including interhemispheric asymmetry estimation in fba, experimental
paradigm (memory working, attention, task-related, etc)
and stimulus type (auditory, visual, audio-visual, static vs
dynamic) are different from each other [25, 36, 37].
However, it was commonly stated that WT-based nonlinear
methods are convenient due to non-stationary nature of
EEG series. Therefore, WC has been applied to five fba of
single-trial non-averaged EEG series mediated by affective
pictures to obtain high-resolution phase coherence in both
time and frequency in the present study.
2 Methods
2.1 Data acquisition
2.1.1 Participants
In the present study, surface EEG series were collected
from 10 healthy male volunteers (aged from 19 to 24 with
mean age of 20:90 2:6 years) and 10 young male volunteers who had been diagnosed with first-episode psychosis (aged from 19 to 23 with mean age of
20:77 2:31 years). In controls, the inclusion criteria are
non-smoking, right-hand use, lacking a history of epilepsy
and stroke, and not to use medication. Experimental data
were collected by volunteers in Department of Biophysics
at Gülhane Research and Education Hospital (GREH) at
Table 1 Clinical test scores of patients (dm: duration in month)
Subject
dm
-n
-p
-si
BPRS
1
4
35
21
51
48
2
1
25
19
42
36
3
0.5
16
28
46
45
4
4
14
11
33
29
5
2
17
19
44
40
6
1
17
13
34
30
7
1
32
15
59
58
8
5
34
24
47
55
9
1
21
18
36
34
10
2
21
18
36
34
University of Health Sciences, Ankara, Turkey. Committee
of GREH approved the research protocol of this study on
November 4, 2014. In patients, the inclusion criteria are
stated by an expert physician with respect to international
clinical tests [Positive and Negative Syndrome Scale
(PANSS) and Brief Psychiatric Rating Scale (BPRS)]
which were presented by American Psychiatric Association
in references [38–40]. The quantitative test results of
patients are listed in Table 1. In this table, PANSS scores
are given in terms of negative (-n), positive (-p), and
psycho-pathology sub-index (-si).
2.1.2 Emotional stimuli and experimental protocol
Emotional stimuli were 56 static and colored affective
pictures (18 were pleasant, 18 were un-pleasant, and 20
were neutral) from a database so called IAPS which is
described in [10]. The categorization of pictures was done
by a rating policy such that a 9-point scale refers the level
of arousal (1 and 9 refer calm and excited, respectively)
and another 9-point scale refers the level of valence (1, 5,
and 9 refer unpleasant, neutral, and, pleasant, respectively).
The IAPS database includes 956 images with a wide range
of subjects such as happy human faces, happy couples, cute
babies, animals, household objects, car accidents, and war.
Each image was rated with two parameters (valence and
arousal) by a large group of participants. The request form
of images can be found on this link http://csea.phhp.ufl.
edu. The scoring policy on the pictures was verified by the
other physiological parameters such as skin conductance,
startle reflex, and heart rate in references [41, 42].
The average arousal and valence scores of selected 18
pleasant pictures were 7.5 and higher scores. The average
arousal and valence scores of selected 18 un-pleasant pictures were 3.5 and lower scores. The average arousal and
valence scores of selected 20 neutral pictures were between
4.5 and 5.5. The number of those pictures is listed in
‘‘Appendix’’.
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Neural Comput & Applic (2018) 30:1085–1094
In each individual experimental session, emotional
visual stimuli were presented on 17-inch computer screen
with a refresh rate of 60 Hz. During data collection, volunteers were placed in a recording room (band cut filter of
0:5500 MHz, attenuation of 40 dB, temperature of 22 C) in
a sitting position on a comfortable chair across the screen
with distance of 1:5 m. The experiments did not include
either working memory or oddball task. At he beginning of
each experiment, two different neutral pictures were presented for 6 s followed by a 6-s inter-trial interval. Just
after showing these adaptation stimuli, the other pictures
were also displayed on that screen of 6 s with a visual
angle of 16 horizontally and 12 vertically in the following order: 6 pleasant, 6 neutral, 6 unpleasant, 6 neutral,
6 unpleasant, 6 pleasant, 6 unpleasant, 6 neutral, 6 pleasant.
Each picture was shown once in a single experimental
session. Following each affective picture, white blank
screen was shown of 6- to 12-s inter-trial interval. The
volunteers viewed the same 56 pictures in the same order in
individual session. They were not familiar with the affective pictures selected.
2.1.3 EEG measurements
Each affective stimulus was shown once in every individual session. The recording systems were 16-channel
Glonner Neurosys system-2000 (Glonner, Munich, Germany). Prefrontal (Fp1, Fp2), frontal (F3, F4, F7, F8),
central (C3, C4), parietal (P3, P4), temporal (T3, T4, T5,
T6), and occipital (O1, O2) scalp activities were recorded
by using Ag/AgCl surface electrodes with respect to
international 10–20 electrode placement system. The
electrical impedances of electrodes were kept less than
5 kX. Analog signals were sampled by sampling frequency
of 250 Hz and were converted to digital sequences by using
16-bit analog-to-digital converter. The temperature of the
light-controlled recording room was set during experiments. Both band-pass filter (0:318 70 Hz) and notch filter
(50 Hz) were applied to raw data. Single-trial raw EEG
measurements were also passed through an artifact detection algorithm described in [43].
2.2 Extraction of EEG frequency band activities
Five well-known EEG fba can be mentioned as follows:
Delta (0:5 4 Hz) observed during deep sleep with large
amplitudes about 75 200 lV, Theta (4 8 Hz) observed in
drowsiness, Alpha (8 16 Hz) observed eyes opened awake
states and attenuated by not only visual attention but also
mental effort, Beta (16 32 Hz) observed during working
memory when alertness is increased, and, Gamma
(32 64 Hz), observed during both processing and
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recognition of sensory stimuli as well as voluntary movements [44, 45].
Since waveform of Daubechies wavelet is highly similar
to the waveform of limited duration of EEG sequences
[46–48], db-8 was used in sixth-level wavelet decomposition for estimation of those FBAs from non-averaged EEG
series mediated by pictures. The final approximation (A6)
and last four details (D6, D5, D4, D3) were assumed to be
equivalent to individual sub-bands so called Delta
(0 4 Hz), Theta (4 8 Hz), Beta (8 16 Hz), Alpha
(16 32 Hz) and Gamma (32 64 Hz), respectively, with
respect to resulting sixth-level decomposition for each
single trial of 6 s.
2.3 Estimation of inter-hemispheric correlation
In the present study, 16-channel emotional surface EEG
series were collected from volunteers mediated by 18 3
times (18 pleasant pictures, 18 un-pleasant pictures, 18
neutral pictures). Assuming x refers a particular EEG subband in non-averaged single-trial emotional EEG series,
recorded from right hemisphere, while y refers the same
frequency range of another non-averaged single-trial
emotional EEG series which is simultaneously recorded
from left hemisphere, WT representations of x and y are
defined by following equations,
Z þ1
1
t s
ð1Þ
WTx ðs; sÞ ¼ pffiffi
ds
xðtÞW
s
s 1
Z þ1
1
t s
ð2Þ
WTy ðs; sÞ ¼ pffiffi
ds
yðtÞW
s
s 1
2
where WðtÞ ¼ p 1=4 ejwt e 0:5t named as mother wavelet
function. Here, s, s and denote the scale, translation, and
complex conjugation. The parameters of translation and
dilation ðs; sÞ correspond to time and temporal period,
respectively. Adjusting the scale provide to obtain different
frequency components of x and y. By using Eqs. 1 and 2,
WC is computed to quantify the level of similarity between
x and y by using the following equation,
WCx;y ðs; sÞ ¼ WTx ðs; sÞWTy ðs; sÞ
ð3Þ
Here, WC is computed in units of normalized variance
[49]. WC was used to observe statistical differences
between controls and patients with autonomic failure in
estimating the possible the relationship between low-frequency oscillations in near-infrared spectroscopy and mean
arterial blood pressure [50]. WC does not depend upon the
power of sub-bands [51]. The stronger synchronization
between two non-stationary time series produces the higher
WC [51]. The more detailed explanation of this method can
be found in reference [52].
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Although the WC method has been applied in diverse
fields from engineering to physiology [53, 54] and neuroscience [55, 56], it has been adopted to analyze fba
embedded in non-averaged EEG series mediated by static
pictures for detection of emotional dysfunctions in the
present study.
2.4 Classification of emotional features
SVMs are supervised machine learning derived methods
based on statistical learning theory of Vapnik Chervonenkis dimension theory [57]. Least squares (LS)-SVM is
modified version of SVM leading a cost function to obtain
a linear set of equations in dual space [58–61]. Therefore,
LS-SVMs have been frequently used to classify EEG features in detecting various disorders [62–66]. In the present
study, two-class problem was solved to classify controls
(labeled by 0) and patients (labeled by 1).
Controls and patients were classified with respect to
eight emotional features for both pleasant and un-pleasant
as well as neutral states in each fba. The number of participants was 10 in each group. Multi-channel emotional
surface EEG series were collected from participants 18
times to obtain electrophysiological waves mediated by
every specified emotional state. Inter-hemispheric phase
coherence was computed individually for eight electrode
pairs (Fp1–Fp2, F3–F4, C3–C4, P3–P4, O1–O2, F7–F8,
T3–T4, T5–T6). Accordingly, we obtained eight emotional
features 180 times (the number of participant the number
of trials ¼ 10 18 ¼ 180) in both pleasant and un-pleasant
as well as neutral states for each fba (Delta, Theta, Alpha,
Beta, and Gamma). Tenfold cross-validation was performed in classification steps. Gaussian radial basis function was used as kernel function in training the data where
the box constraint and scaling factor were 0.05 and 1,
respectively.
It is known that spontaneous EEG series are not stationary, and amplitude-latency parameters of evoked
potential vary from sweep to sweep [67–70]. In studies
including emotion recognition, the same brain functions are
observed controls when the same emotional stimulus is
received by individuals [71–73]. Therefore, estimation of
two-channel inter-hemispheric correlation between two
separate non-averaged EEG series has been aimed in the
present study. Regarding the applications in both brain–
computer interface studies [74, 75] and emotion recognition papers [76–78], the number of individuals is not
classified; indeed, the number of instances, i.e., the number
of EEG segments mediated by the identical stimuli, is
classified. To perform tenfold cross-validation (CV), the
features are divided into two spaces to firstly train a model
and then to validate this model in LS-SVM applications
where onefold of the feature set is held out for validation,
while the remaining ninefold are used for learning within
each iteration.
3 Results
The classification performance statistics [Sensitivity
(SNS), Specificity (SPC), and classification accuracy (CA)]
were computed in classifying controls and patients with
respect to each specified EEG fba. In addition, three feature
sets are organized as follows: The feature set so named
Lower Bands (LB) includes the features obtained from low
fba (Delta and Theta), the feature set so named Higher
Bands (HB) includes the features estimated from high fba
(Alpha, Beta, and Gamma), and the final feature set so
named All Bands (AB) includes the features estimated for
each fba. The classification performance results are given
in Table 2.
Regarding this table, it can be said that the best classification performance is provided by the highest EEG band,
Gamma, such that controls can be classified with the CA
values of 88.06, 86.39, and 83.89% considering neutral, unpleasant, and pleasant pictures, respectively. The relatively
lower performance is obtained by using the features generated by Beta in neutral, un-pleasant, and pleasant states
with the CA values of 88.06, 82.22, and 78.33%, respectively. The useful classification performance can not be
obtained when the features are estimated from other separate fba (Alpha, Theta, Delta). The LB provided the lower
performance than HB in each emotional state; moreover,
AB produced the lowest classification performance in
comparison with both LB and HB as well as individual fba
in each emotional state. However, the highest classification
performance was obtained for the features which were
estimated from Gamma in each emotional state.
The statistical spectra (mean std) of WC values which
were estimated for relatively high fba (Alpha, Beta, and
Gamma) in accordance with emotional states are shown in
figures for both patients and controls. Figure 1 shows that
patients produced the lower WCs at three lobes (anteriofrontal, central, parietal) in response to pleasant pictures in
Alpha. In controls, the highest WCs were observed at prefrontal lobe in each emotional state.
In addition, controls and patients commonly provide the
higher WCs for all states at pre-frontal lobe in Alpha.
Figure 2 shows that both controls and patients produce
the lower WCs at posterio-frontal, occipital, and temporal
electrode pairs in comparison with other electrode pairs
including anterio-frontal, central, and parietal lobes for
each emotional state in Alpha.
In addition, patients produced the lower WCs at posterio-temporal region for each emotional state in Alpha.
Figure 3 shows that the highest WCs were produced by
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Neural Comput & Applic (2018) 30:1085–1094
Table 2 Statistical results of
classifications
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Delta
Theta
Alpha
Beta
Gamma
LB
HB
AB
SNS
67.22
66.11
76.11
78.89
96.67
73.06
80.00
43.56
SPC
70.00
66.11
81.11
77.78
71.11
46.11
74.26
67.44
PPV
69.14
66.11
80.12
78.02
76.99
57.55
75.66
57.23
NPV
68.11
66.11
77.25
78.65
95.52
63.12
78.78
54.44
CA
68.61
66.11
78.61
78.33
83.89
59.58
77.13
55.50
SNS
65.00
65.56
75.56
82.78
92.78
64.17
79.81
43.89
SPC
66.11
61.11
81.11
81.67
80.00
56.67
73.89
67.89
PPV
65.73
62.77
80.00
81.87
82.27
59.69
75.35
57.75
NPV
65.38
63.95
76.84
82.58
91.72
61.26
78.54
54.75
CA
65.56
63.33
78.33
82.22
86.39
60.42
76.85
55.89
SNS
74.44
72.78
78.33
88.89
96.67
66.94
81.85
44.78
SPC
PPV
73.89
74.03
65.56
67.88
83.33
82.46
87.22
87.43
79.44
82.46
55.83
60.25
74.63
76.34
70.44
60.24
NPV
74.30
70.66
79.37
88.70
95.97
62.81
80.44
56.06
CA
74.17
69.17
80.83
88.06
88.06
61.39
78.24
57.61
P
UP
N
Fig. 1 Error Bars in Alpha at anterio-frontal, central, and parietal
regions
Fig. 2 Error Bars in Alpha at posterio-frontal, occipital, and temporal
regions
controls at anterio-frontal and central regions for pleasant
state, while patients produce the lower level of WC at
anterio-frontal and central lobes for neutral state in Beta.
Regarding Fig. 4, patients produce the higher level of WCs
at temporal lobes for neutral state in Beta.
Figure 5 shows that the highest WCs were produced by
controls at anterio-frontal and central regions for pleasant
state, while the patients produce the higher-level WCs at
the same regions for neutral state in Gamma. Regarding
Fig. 6, patients produce the higher level of WCs at temporal lobes for neutral state in Gamma. By comparing all
figures, it can be said that the largest WCs were obtained
from Gamma at anterio-frontal and central electrode pairs
in pleasant state.
We also used analysis of variance (ANOVA) test to
study the contribution of each pair of EEG channels,
located on scalp symmetrically, in discriminating controls
from patients with FEP in accordance with specified
emotional state. The most useful differences between
controls and patients were observed in Gamma. Therefore,
the related results (p values) are listed in Table 3.
Regarding Table 3, the highest statistically meaningful differences between controls and patients were observed at both
pre-frontal and occipital regions of the brain for un-pleasant
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Fig. 3 Error Bars in Beta at anterio-frontal, central, and parietal
regions
Fig. 4 Error Bars in Beta at posterio-frontal, occipital, and temporal
regions
ðp 0:0001Þ and neutral ðp 0:0001Þ pictures in studying
contribution of each electrode pair. When the groups were
compared to each other with respect to eight electrode pairs, the
clear difference between groups could be observed for both
unpleasant ðp 0:0001Þ and neutral ðp\0:001Þ pictures.
4 Discussion and conclusion
The possible dependency of emotional activities on interhemispheric correlation has been studied to classify healthy
controls and patients diagnosed with FEP. The results show
that patients provide the high level of inter-hemispheric
correlation in response to neutral pictures, whereas controls
provided the high level of inter-hemispheric correlation in
response to pleasant pictures at commonly anterio-frontal
and central lobes in Gamma.
Fig. 5 Error Bars in Gamma at anterio-frontal, central, and parietal
regions
Fig. 6 Error Bars in Gamma at posterio-frontal, occipital, and
temporal regions
The best classification performance was obtained in
Gamma. Although, the useful results could be observed in
other high fba (Alpha, Beta) to classify the data mediated
by neutral and un-pleasant pictures, combination of the
separate features extracted from each sub-band produced
the poor performance.
In conclusion, emotional functions of the brain could
be observed in relatively higher fba (16 32 and
32 64 Hz). The lower fba (0:5 4 and 4:5 8 Hz) do not
reflect the emotional functions in detail. Visual and static
pictures activated mostly anterio-frontal and central lobes
in Gamma. The largest level of inter-hemispheric correlation was observed at mostly frontal lobe in Gamma in
response to pleasant pictures in controls, while the largest
level of inter-hemispheric correlation was observed at
mostly frontal lobe in Gamma in response to neutral
pictures in patients. In Alpha, the lowest level of inter-
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Neural Comput & Applic (2018) 30:1085–1094
Table 3 Statistical results
(p values) of ANOVA in
studying the contribution of
each electrode pair in addition
to all pairs with respect to
emotional states in Gamma
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Electrode pair
For pleasant stimuli
For unpleasant stimuli
Fp1–Fp2
0.7196
F3–F4
0.4637
1:85 10
0.0723
0.1170
C3–C4
0.4170
0.0921
0.2183
P3–P4
0.4407
0.0045
15
For neutral stimuli
2:66 10
0.2471
13
O1–O2
0.1940
F7–F8
0.3751
0.1573
0.2234
T3–T4
0.0806
0.0881
0.2598
T5–T6
0.2131
0.4880
All pairs
3:90 10
0.0818
hemispheric correlation in addition to the narrower
interval of correlations (mean std) was observed at
temporal lobes (T3–T4, T5–T6) in response to un-pleasant and neutral pictures in controls and patients, respectively. In Gamma, the lowest level of inter-hemispheric
correlation was observed at parieto-central lobes (P3–P4,
O1–O2) in response to un-pleasant pictures in both controls and patients.
Our results are compatible with the previous findings,
including three main statements as follows: decreased
hippocampal volume closely linked with emotional dysfunctions [79], functional insufficiency at mostly the right
hemisphere in depression [80], and increased emotional
coherence in controls in Gamma (low Gamma: 30 50 Hz
and high Gamma: 50 80 Hz) [80]. In addition, the relatively decreased cortical activities at right and left hemispheres were found to be related to withdrawal and
approach motivations, respectively, in MDD [81]. In detail,
the relatively higher EEG asymmetry was reported as
associated with motivational system and affect at anteriofrontal (F3–F4) and posterio-frontal (F7–F8) regions in
Alpha in depression before treatment [82]. Depression was
also reported as connected with not only increased restingstate activities at left hemisphere but also decreased resting-state activities at right hemisphere in Alpha [83–85].
Commonly, depressive inter-hemispheric emotional dysfunctions were correlated with relatively higher fba in the
literature.
The amygdala having the main role in existing an
emotion, includes perceptual pathways (from primary
visual cortex to inferior temporal cortex) and reciprocal
connections (between prefrontal cortex and orbito-frontal
cortex) in humans. The orbito-frontal cortex, located at the
base of the frontal lobes, receives direct neural inputs such
as emotional stimuli from medial thalamus. Then, sensory
information is received and strengthen by amygdala. In
summary, once the brain is mediated by an affective picture, the thalamus and cortex interact to each other through
not only firing of individual neurons but also transient
functional integrations of local neuronal assemblies across
1:07 10
12
2:37 10
14
0.1391
7
0.0065
right and left brain regions. Therefore, our results support
that cortical EEG series can be analyzed by means of
neuronal inter-hemispheric correlation in wavelet domain
in high fba (16 32 Hz and 32 64 Hz) for early detection
of FEP.
In future work, both POMS scale [86] and theta coherence analysis [87] would be examined to re-analyze the
emotional data explained in the present study.
Acknowledgements Authors thank to Prof. Dr. Cüneyt Göksoy and
his staff (in Department of Biophysics) and Psychiatrist Taner Öznur
(in Department of Mental Health and Disease) at Faculty of Medicine
in University of Health Sciences, for providing experimental data and
selecting affective pictures as visual stimuli.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Appendix
In the present study, several pictures were selected from
IAPS as emotional stimuli as follows: Adaptation (Neutral)
pictures: 2745 and 2191. Pleasant pictures: 1440, 1460,
1610, 1710, 1920, 2035, 2071, 2311, 2347, 2550, 4626,
5210, 5621, 5760, 5780, 5833, 7330, 8170. Unpleasant
pictures: 1111, 3185, 3195, 3213, 3550.1, 6312, 6313,
6520, 7359, 8230, 9043, 9075, 9291, 9300, 9413, 9560,
9600, 9940. Neutral pictures: 2026, 2102, 2273, 2377,
2411, 2512, 7001, 7002, 7004,7009, 7014, 7019, 7032,
7050, 7052, 7081, 7179, 7211.
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