ORIGINAL RESEARCH
published: 11 January 2018
doi: 10.3389/fnhum.2017.00659
Positive Classification Advantage:
Tracing the Time Course Based on
Brain Oscillation
Tianyi Yan 1 *, Xiaonan Dong 1 , Nan Mu 1 , Tiantian Liu 1 , Duanduan Chen 1 , Li Deng 2 ,
Changming Wang 3 and Lun Zhao 4
1
School of Life Science, Beijing Institute of Technology, Beijing, China, 2 Beijing Key Laboratory of Network System
Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China, 3 Beijing Key Laboratory
of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, 4 Institute of Brain Research, Beijing
Yiran Sunny Technology Co., Ltd., Beijing, China
The present study aimed to explore the modulation of frequency bands (alpha,
beta, theta) underlying the positive facial expressions classification advantage within
different post-stimulus time intervals (100–200 ms, 200–300 ms, 300–400 ms). For
this purpose, we recorded electroencephalogram (EEG) activity during an emotion
discrimination task for happy, sad and neutral faces. The correlation between the
non-phase-locked power of frequency bands and reaction times (RTs) was assessed.
The results revealed that beta played a major role in positive classification advantage
(PCA) within the 100–200 and 300–400 ms intervals, whereas theta was important
within the 200–300 ms interval. We propose that the beta band modulated the neutral
and emotional face classification process, and that the theta band modulated for happy
and sad face classification.
Keywords: positive classification, reaction times, brain oscillation, correlation, time intervals
Edited by:
Daniela Iacoviello,
Sapienza Università di Roma, Italy
Reviewed by:
Gonzalo Alarcon,
King’s College London,
United Kingdom
Yingchun Zhang,
University of Houston, United States
*Correspondence:
Tianyi Yan
yantianyi@bit.edu.cn
Received: 22 November 2016
Accepted: 22 December 2017
Published: 11 January 2018
Citation:
Yan T, Dong X, Mu N, Liu T, Chen D,
Deng L, Wang C and Zhao L
(2018) Positive Classification
Advantage: Tracing the Time Course
Based on Brain Oscillation.
Front. Hum. Neurosci. 11:659.
doi: 10.3389/fnhum.2017.00659
INTRODUCTION
Facial expressions play an important role in social life. The information is valuable for interpreting
how others feel and their behavioral tendencies. Ekman (1994) classified emotional facial
expressions to six basic categories (happiness, sadness, anger, disgust, fear and surprise). So far,
methods such as single cell recordings, functional brain imaging and event-related potentials
(ERPs) have been used to investigate brain activity involving perception, emotion, behavior, etc.
Thus, studies have shown the probable neural network of emotionally salient stimuli (Eimer and
Holmes, 2007). As shown by previous studies, brain activities related to emotional events, including
those in the higher order sensory cortex, amygdala, orbitofrontal cortex and ventral striatum, share
complex interconnected structural network. However, much more research needed to understand
the brain mechanisms underlying emotion.
The recognition speed of facial expressions of emotion is very easy to obtain. Abundant data
from research has revealed that the recognition speed of happy faces is faster than sad faces (Crews
and Harrison, 1994; Leppänen and Hietanen, 2004; Calvo and Beltrán, 2013; Liu et al., 2013),
angry faces (Billings et al., 1993; Hugdahl et al., 1993; Calvo and Beltrán, 2013), disgusted faces
(Stalans and Wedding, 1985) and neutral faces (Hugdahl et al., 1993; Leppänen and Hietanen, 2004;
Calvo and Beltrán, 2013; Liu et al., 2013). However, behavioral experiments can only use simple
measures such as performance accuracy and reaction times (RTs). In addition, the RTs of multiple
expressions classification tasks are usually 1 s or above (e.g., Calder et al., 2000; Palermo and
Coltheart, 2004; Calvo and Lundqvist, 2008), which, in terms of advance chronometry, are very large
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Positive Classification Advantage
Engel and Fries, 2010). Some researchers have reported enhanced
beta activities in response to affective stimuli compared
with neutral stimuli (Woodruff et al., 2011). Güntekin and
Başar (2010) also reported higher beta activity in response
to negative images than positive images in frontal, central
and parietal electrodes upon presentation of IAPS images
(Güntekin and Başar, 2010). In addition, Schutter et al.
(2001) conducted a spontaneous EEG study and found a
significant relationship in response to angry facial stimuli
between asymmetry in parietal beta power and the attentional
response (Schutter et al., 2001). Güntekin and Basar (2007)
also reported increased beta power in response to angry
facial stimuli compared with happy stimuli at F3 and Cz
(Güntekin and Basar, 2007). However, Zhang et al. (2013) found
greater beta oscillation activity for positive facial expressions
than for negative expressions. Emotion processing mechanisms
are rather complicated to reveal; thus, more studies are
needed to complete our knowledge on these related brain
structures.
As mentioned above, the results on the brain oscillations
produced upon the processing of emotional faces have been
controversial. Thus, in the present study, we will trace the time
course of PCA based on brain oscillations. Our goal is to find
the modulation of PCA on frequency bands within different
time intervals. In prior research, Liu et al. (2013) found the
N170 (150–170 ms) component, posterior N2 (250–290 ms)
component and P3 (350–450 ms) component using schematic
face stimuli (Liu et al., 2013). Moreover, they found that
neutral faces elicited a shorter N170 latency compared with
happy and sad faces. Meanwhile, they found that happy faces
elicited more negative N2 activity compared with neutral and
sad faces. Additionally, happy and neutral faces elicited higher
P3 amplitudes and shorter P3 latencies compared with sad faces.
Based on these results, we assumed that when a subject makes
a decision, he/she first classifies the neutral face (time window
1: 100–200 ms) and then discriminates between happy and sad
expressions (time window 2: 200–300 ms and time window 3:
100–200 ms). Thus, the time course of PCA is time window
2 and 3. We will mainly focus on these two time windows.
To test our hypothesis, we used schematic face stimuli based
on Liu et al. (2013). Schematic faces allow us to control physical
features as carefully as possible, to minimize influence from
additional information related to facial identity (e.g., gender,
race) and to exclude the confounding effects of general arousal
as well as valence, per se (Boucsein et al., 2001; Eger et al.,
2003; Krombholz et al., 2007; Babiloni et al., 2010). Moreover,
Sagiv and Bentin (2001) have proved that even schematic
faces (only made from simple line fragments) could trigger
face-sensitive N170, and that this effect was not attributable
to an artifact arising from facilitated recognition of a single
feature (Sagiv and Bentin, 2001; Leppänen and Hietanen, 2004).
In addition, schematic face stimuli are reported to be able to
provide emotional stimuli; significant increase of fMRI signal
can be found in the amygdala, hippocampus and prefrontal
cortex in response to emotional vs. neutral schematic faces
(Wright et al., 2002), indicating the feasibility for applying the
schematic faces to study PCA.
time scales. For exploring more precise time processes, previous
studies have explored the neural mechanism of facial expression
classification using ERPs (e.g., Eimer and Holmes, 2007; Lynn
and Salisbury, 2008). Some ERP research has revealed the
phenomenon of positive classification advantage (PCA), which
means that positive facial emotional expressions are recognized
faster than negative ones (Leppänen and Hietanen, 2004). PCA
is strongly linked to late perceptual processing, the differences
between fearful and happy faces were shown over occipital
regions as early as 80 ms post-stimuli, and those between happy
and sad faces between 90 ms and 110 ms. Thus, our research
focuses on this time course of PCA processing.
Many previous studies have revealed that ERP components
are strongly linked to the categorization of facial emotion
expression. Studies have shown that PCA is related to late
components (Liu et al., 2013). However, information on the time
course of facial emotion categorization has not been revealed.
Brain oscillations, which could better track the activities of
neurons, could be an efficient way to explore the time course
of facial emotional categorizations. Oscillation activities could
provide key physiologically information of brain dynamics.
Furthermore, we can use neurofeedback to train different
oscillation activities. Thus, we can discriminate between positive
stimuli more quickly. Thus, the electroencephalogram (EEG)
dynamics of face perception and facial expression have recently
been analyzed through oscillation dynamics.
Event-related theta oscillations (4–7 Hz) have been reported
to play an important role in cognitive processes such as
memory, attention and cognition (Klimesch et al., 1997;
Kahana et al., 2001; Khader et al., 2010; Sauseng et al.,
2010). Balconi and Lucchiari (2006) reported enhanced frontal
theta synchronization to emotional facial expressions as
compared, with neutral expressions. Additionally, higher theta
synchronization to fearful facial expressions than neutral
expressions was observed. Similarly, Knyazev et al. (2009)
reported that theta synchronization was higher in response to
emotional faces (angry and happy faces) than neutral faces
(Knyazev et al., 2009).
Alpha oscillations (8–15 Hz), which are pronounced due
to their asymmetric effect, have been studied for many years
(Davidson, 2003, 2004; Coan and Allen, 2004; Herrmann et al.,
2004). Despite these obvious features on emotional processing,
Güntekin and Basar (2007) found that in comparison with happy
expressions, angry expressions elicited higher alpha responses at
T5, P3 and O2 electrodes. Additionally, in an MEG experiment
by Onoda et al., they found that event-related alpha power in
the occipital region is higher in negative conditions than in other
conditions (neutral and positive; Onoda et al., 2007). However,
Balconi and Mazza (2009) reported that compared with neutral
stimuli, positive and negative emotions trigger decreased alpha
power responses. Furthermore, they also found that alpha
oscillation was associated with an increase in left hemisphere
activity (Balconi and Mazza, 2010). Thus, the modulation of
alpha oscillations on emotional processes is still not clear.
Beta oscillations (16–30 Hz) have been thought to have a
strong link with sensorimotor functions and could be reduced by
voluntary movements and motor imagery (Neuper et al., 2009;
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MATERIALS AND METHODS
center of a cathode ray tube video monitor and were viewed
from a distance of 100 cm at a visual angle of approximately
7.27◦ × 6.06◦ .
Following electrode application, the participants were
seated in a dimly lit and sound-attenuated cabin. They
were instructed to classify each face by the expression it
represented (happy, neutral, or sad) and to respond by
pressing correspondingly labeled buttons on the keyboard
with the left index finger (Z key), right index finger (N key),
or right middle finger (M key). Speed and accuracy were
equally emphasized (Liu et al., 2013). All of the 324 stimuli
(3 facial expressions × 108 faces) were randomly presented
in a mixed design, with three blocks, each of which possess
108 stimuli and a short break in between. To offset the
difference between the fingers, the labels of the response buttons
(happy–neutral–sad/sad–happy–neutral/neutral–sad–happy)
counterbalanced across the participants. Each face was presented
for 300 ms with an inter-trial interval ranging randomly between
600 ms and 800 ms, starting after response.
The participants completed one practice sequence of
18 stimuli (six from each type, equally representing the three
facial expressions). These stimuli were not used in the main
experiment, which lasted approximately 15 min.
Subjects
This study was carried out in accordance with the
recommendations of ‘‘School of Life Science Ethics Committee,
Beijing Institute of Technology’’ with written informed consent
from all subjects. All subjects gave written informed consent
in accordance with the Declaration of Helsinki. The protocol
was approved by the ‘‘School of Life Science Ethics Committee,
Beijing Institute of Technology’’. Eighteen young healthy
individuals participated in our study (10 females; 20–25 years
of age; mean: 22.6 years). All participants were right-handed,
had normal or corrected-to-normal visual acuity and were free
of a neurological or psychiatric history. They received payments
for their participation and gave their written informed consent
before the experiment (Liu et al., 2013); however, the study only
examined PCA in the classic time-amplitude domain and no
time-frequency analyses have been previously reported.
Stimuli and Procedure
To avoid the low-level processing of facial features, as well as
boredom by the excessive repetition of one single model, each
facial expression category consisted of 18 different schematic
face models by manipulating the distance among facial features
and by manipulating the shape of the facial features, particularly
the mouths. Figure 1 illustrates examples of schematic face
expressions we used as stimuli. All stimuli were presented at the
EEG Recording
An EEG was recorded continuously using an electrode cap
with 64 sintered Ag/AgCI electrodes mounted according to the
FIGURE 1 | Example stimuli of schematic facial expressions.
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extended international 10–20 system and referenced to the tip
of the nose. An electrooculogram (EOG) was recorded via two
pairs of additional electrodes, with one placed above and below
the left eye and the other placed to the external canthi of both
eyes. The EEG and EOG were amplified and digitized by the
NeuroLab Amplifier (Yiran Sunny Technology Co. Ltd., Beijing,
China) with a bandpass of 0.05–100 Hz and a sampling rate of
500 Hz. Electrode impedance was kept below 5 kΩ throughout
the experiment.
than either happy faces (93.7%, p = 0.002) or sad faces (94.2%,
p = 0.007), with no differences between the latter conditions
(p > 0.9). For each participant, incorrect responses or responses
with RTs more than ±2 SDs from the mean in each condition
were excluded for RT analysis. On average, 8.7% of the responses
were removed. The RTs were analyzed by using the same
statistical model as that for percentages of correct responses.
There was a significant main effect of expression, F (2,34) = 95.2,
p < 0.001, partial η2 = 0.849, showing that neutral face
categorization was faster (551 ms) than happy face categorization
(602 ms, p < 0.001), which was quicker than classifying sad
faces (656 ms, p < 0.001). To investigate the possible source
of the PCA, a Pearson correlation analysis was conducted. This
comparison showed that there was an overall significant positive
correlation between the RT to negative face stimuli and the size
of the PCA, r = 0.66, p < 0.005 (two tailed), but not between the
RTs to positive face stimuli and the PCA, r = 0.17, p > 0.05.
Data Analysis
Data analysis was performed using MATLAB R2013a
(Mathworks Inc., Natick, MA, USA) with the open source
toolboxes EEGLAB (Swartz Center for Computational
Neuroscience, La Jolla, CA, USA)1 . The artifacts (e.g., eye
artifacts, muscle artifacts and electrocardiographic activity) of
all channels were removed by independent component analysis
(ICA). After the artifact correction of EEF data, epochs (600 ms
pre- to 900 ms post-stimulus onset) were sorted according
to stimulus condition to create a plot of time-frequency
representations (TFRs). Total frequency band responses were
analyzed via a Morlet wavelet using the MATLAB wavelet
toolbox (MathWorks). Morlet c was set to 7, and the final power
was µV2 . The TFRs of the theta band power of each participant
were calculated; these ranged from 4 Hz to 7 Hz, whereas the
alpha band ranged from 8 Hz to 15 Hz, and the beta band from
16 Hz to 30 Hz. We calculated the synchrony among the medial,
right, and left electrodes and subtracted the frequency-specific
baseline (−300 to 0 ms pre-stimulus). Wavelet activity was
individually returned by wavelet decomposition for each trail.
Changes in the amplitude of activity were measured every
100 ms from 100 ms to 400 ms post-stimuli (e.g., 100–200 ms
post-stimuli, 200–300 ms post-stimuli, 300–400 ms post-stimuli
. . .) to cover a whole cycle of the high frequencies.
Accuracy rates and RTs (from the stimulus onset) were
recorded and analyzed using a one-way ANOVA design, with
expression (happy, neutral and sad) as the within- subjects
factor. Based on previous studies, for each EEG frequency band
the measurements were analyzed using a repeated-measures
ANOVA treating facial expressions (happy, neutral and sad),
hemisphere (left, right), and site (P7/8, PO7/8, P9/10) as withinsubject factors. For factors with more than two levels, the
degrees of freedom were corrected using the Greenhouse-Geisser
procedure (for simplicity, the uncorrected degrees of freedom
are presented). Post hoc comparisons were performed with the
Bonferroni procedure.
Time-Frequency Analysis
For each time window and each frequency, a repeated-measures
ANOVA with facial expression (happy, neutral and sad), and
hemisphere (left, right) was used to examine the overall effects
for different oscillation (theta, alpha, beta), respectively. We
conducted four ANOVAs, one for each time interval. Based on
previous research, we conducted a repeated-measures ANOVA
with within-subject factors as facial expression (happy, neutral
and sad), hemisphere (left, right) and site (P7/8, PO7/8 and
P9/10), and different frequency bands, were used at lateral
posterior sites (left, P7, PO7 and P9; right, P8, PO8 and P10).
For all the ANOVAs, the degrees of freedom were GreenhouseGeisser corrected where appropriate. Figure 2 shows the spectral
power for happy, sad and neutral expressions on P9, P10, PO7,
PO8, P7, P8, respectively.
Oscillation Activities
In the 100–200 ms time window, the main effects for expressions
on theta band revealed that sad faces (2.757 µV2 ) elicited
lower power than happy (3.126 µV2 , p < 0.01) and neutral
faces (3.148 µV2 , p < 0.02), with no difference between latter
two conditions, and that the power elicited by sad expressions
was lower than happy (p < 0.01) and neutral expressions
(p < 0.001) on alpha band (F (2,34) = 11.294, p < 0.001, partial
η2 = 0.399) and beta band (F (2,34) = 14.699, p < 0.001, partial
η2 = 0.464) with no difference between the latter two conditions.
The main effect of hemisphere was also significant on theta
(F (1,17) = 10.643, p < 0.01, partial η2 = 0.385) and beta band
(F (1,17) = 8.056, p < 0.02, partial η2 = 0.322), revealing a right
hemisphere dominance (3.634 µV2 and 2.587 µV2 for left and
right hemisphere, respectively) on the theta band, while power at
left occipital sites (1.029 µV2 ) were larger than at right occipital
sites (0.97 µV2 ) on beta band. Figure 3 shows the power for
happy, sad and neutral expressions on difference time windows
and different frequencies. There was no significant two-way
interaction between factors (expression, hemisphere, site) on
difference time windows and different frequencies (please refer
to Supplementary Table S1).
RESULTS
Performance
A one-way ANOVA analysis was conducted for the percentage of
correct responses. The main effect of expression was significant,
F (2,34) = 7.95, p = 0.003, partial η2 = 0.319. Post hoc comparisons
showed that neutral faces were identified more correctly (97.4%)
1 http://sccn.ucsd.edu/eeglab/
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FIGURE 2 | Spectral power for happy, sad and neutral expressions on different sites (P9, P10, PO7, PO8, P7 and P8).
FIGURE 3 | Left: spectral power for happy, sad and neutral expressions on time window 1 (100–20 ms post-stimuli), time window 2 (200–300 ms post-stimuli) and
time window 3 (300–400 ms post-stimuli), respectively; Right: power topography for happy, sad and neutral expressions on theta, alpha and beta band and time
window 1 (100–200 ms post-stimuli), time window 2 (200–300 ms post-stimuli) and time window 3 (300–400 ms post–stimuli), respectively.
expressions (F (2,34) = 9.681, P < 0.001, partial η2 = 0.363),
revealing that sad faces elicited lower activity (1.171 µV2 ) than
the happy (1.509 µV2 , p < 0.01) and neutral faces (1.458 µV2 ,
p < 0.01), but that there were no differences between the latter
two types of faces (p = 0.948). There was a significant main effect
of expression on beta band (F (2,34) = 14.324, P < 0.001, partial
η2 = 0.457), showing less sad activity (0.876 µV2 ) than neutral
activity (0.999 µV2 , p < 0.001) and happy activity (0.965 µV2 ,
p < 0.01), and no difference between the latter two conditions.
For the time window from 200 ms to 300 ms, theta
band showed a significant difference in facial expressions
(F (2,34) = 37.96, P < 0.001, partial η2 = 0.691), revealing a higher
power for happy faces (3.102 µV2 ) than neutral faces (2.86 µV2 ,
p < 0.05) and sad faces (2.231 µV2 , p < 0.01). The effect of
hemisphere for theta band also showed a significant difference
(F (1,17) = 8.473, P < 0.01, partial η2 = 0.322), with the hemisphere
being more prominent (2.965 µV2 ) than the left (2.497 µV2 ).
Alpha oscillations also showed a significant difference in facial
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FIGURE 4 | Scatter distribution of power of different frequencies and reaction time (RTs) during time window 1 (100–200 ms post-stimuli), time window 2
(200–300 ms post stimuli) and time window 3 (300–400 ms post-stimuli), respectively.
The Pearson correlations between RTs and the power
of frequency bands showed significant negative correlations
between RTs and beta power during the time window of
100–200 ms (r = −0.378, p < 0.02) and 300–400 ms (r = 0.336,
p < 0.03); that is, the longer the RTs, the lower the beta power
within time window 1 and 3. Interestingly, power on theta band
was also negatively linked to RTs, but the time window was
200–300 ms (r = −0.301, p < 0.05). Furthermore, there were
no significant correlations between the RTs and power of other
oscillation bands (i.e., alpha) modulated by facial expressions
(ps > 0.06).
Hemisphere also showed a significant difference in beta band
(F (1,17) = 5.977, P < 0.05, partial η2 = 0.26), revealing a higher
power at the left hemisphere (0.987 µV2 ) than that at the right
(0.907 µV2 ).
The time window from 300 ms to 400 ms showed significant
main differences in facial expression for theta (F (2,34) = 49.263,
p < 0.001, partial η2 = 0.743), alpha (F (2,34) = 20.695, p < 0.001,
partial η2 = 0.495), beta (F (2,34) = 48.283, p < 0.001, partial
η2 = 0.74), revealing that neutral and happy expressions elicited
higher power than sad expressions (p < 0.001), while there
was no difference between neutral and happy expressions. A
significant main effect of hemisphere also found on alpha
(F (1,17) = 7.936, p < 0.02, partial η2 = 0.318) and beta band
(F (1,17) = 15.907, p < 0.001, partial η2 = 0.483), showing a left
hemisphere dominance (1.204 µV2 and 1.064 µV2 for the left
and right hemisphere on alpha band, 0.911 µV2 and 0.817 µV2
on beta band, respectively) on both alpha and beta bands.
DISCUSSION
Below is a summary of the results of the present research.
Through facial expression classification experiments, we will
discuss the phenomenon of happy face classification advantage
using oscillation characteristics as well as time course. In
line with previous studies, we found that face expression
classification is quicker for happy expressions than for sad
expressions (e.g., Crews and Harrison, 1994; Leppänen and
Hietanen, 2004), and faster for neutral faces than happy faces.
Moreover, happy faces elicited a higher spectral power than
sad face. In accordance with other studies (Balconi and Mazza,
2009), the results showed a left hemisphere dominance, revealing
Pearson Correlations
In addition to the ANOVAs, because we want to explore how
the oscillation activities reflect modulations of the stimulus
evaluation and decision processes, Pearson correlations between
the RTs and power of all frequency bands (theta, alpha and
beta, respectively) were conducted. Figure 4 shows scatterplot
diagrams and linear fitting of the scatter-plots.
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ERP component during 140–180 ms post-stimuli at occipitotemporal electrodes), which is thought to be an indicator of
inverted-face recognition, shows significant differences to fearful
faces than neural faces at 160 ms post-stimuli (Holmes et al.,
2005). Compared with time window 1, we concluded that
face categorization was pre-attended during this time window.
For posterior N2, a negativity peaking between 200 ms and
300 ms may be modulated by factors influencing visual stimuli
categorization such as mutual information level, determined by
gross similarity between the fragment and image in an image
patch (Harel et al., 2007). In addition, as a generic name for
relatively late positive component with a distribution at centroparietal or centro-frontal midline area, P3 is considered in
conjunction with facial emotion categorization (Polich, 2007). It
has shown higher amplitudes and shorter latencies in response
to both happy and neutral stimuli than sad stimuli, while RT
displayed a significant correlation with amplitude and latency of
the P3. During time windows 2 and 3, which are linked to the
N2 and P3 components, emotion categorization is completed.
Thus, the time-division can help us to better analyze the process
of emotion categorization.
Though there were significant differences in theta, alpha and
beta oscillations for different emotional stimuli (happy, neutral
and sad), only theta and beta band significantly were correlated
with RTs. However, the correlation between beta oscillation
and RTs was as early as 100 ms after stimuli onset. That is,
beta oscillation may modulate the categorization of neutral
faces. In our results, the beta band had a major impact upon
face discrimination. Also, the categorization occurred as early
as 100 ms after stimulus onset, which has important effects
for our social life. Furthermore, researchers have revealed that
beta oscillation contributed to classification of known faces and
unknown faces within 100–200 ms (Ozgören et al., 2005). Based
on these results, we propose that in time window 1, subjects
can discriminate between neutral and emotional faces, and that
beta band is related to the process. This suggests that neutral
face may be classified the fastest; and is modulated by beta
band.
As for time windows 2 and 3, previous studies revealed that
theta power was related to the P3 component (see a review Polich,
2007) and that P3 was related to PCA (Liu et al., 2013). That
is, theta (time window 2) and beta (time window 3) oscillations
modulate the PCA. These results are consistent with other
research that focused on negative stimuli (Cohen et al., 2013).
This research demonstrated that the theta band contributed to
early emotion selection (200–300 ms) and that the beta band was
a late response (400–600 ms). Thus, our hypothesis was verified,
meaning that the theta band and beta band had a major impact
on PCA.
In conclusion, our research used schematic emotional faces
and focused on the time course of the recognition advantage
of happy faces. The results demonstrate that the categorization
process was mainly associated with beta oscillations from the
beginning, while theta oscillations participated during the later
period. Thus, we could detect PCA as early as 200 ms post-stimuli
through theta oscillations, earlier than the other findings on time
domain (usually 300 ms). Moreover, we can train theta bands
the lateralization effect of positive expression classification.
Although facial expressions elicited different activity on theta,
alpha and beta frequencies, only beta and theta showed
significant negative correlation with RT. Moreover, beta was
strongly correlated with PCA in time window 1 and 3, whereas
theta was correlated with time window 2. Additionally, the
greater spectral power was linked to a shorter RT in all the three
time windows and three frequency bands. Thus, quick responses
require more brain activity, which was not obvious in the alpha
bands.
In the present task, the theta oscillations showed that happy
faces elicit higher power than sad faces, but the power elicited
by neutral faces was higher than emotional faces (happy and sad
faces). Previous studies with IAPS affective pictures as stimuli
showed that emotional stimuli always elicited higher power than
neutral stimuli (Balconi and Lucchiari, 2006; Zhang et al., 2013),
which is in contrast with our results, but the stimuli in these
experiments are not facial expressions. Studies using emotional
video clips showed that power elicited by positive stimuli is
higher than that by negative stimuli in the post-occipital area
(Aftanas et al., 1998), which is similar to our results. Moreover,
previous study using real emotional expressions suggested that
happy expressions has faster results and higher accuracy than
neutral expressions in discriminating tasks (Dasilva et al., 2016),
which indicated the PCA.
As previously shown, alpha power showed an increase with
positive stimuli in comparison with neutral stimuli after 100 ms
post-stimuli, and although this difference is not significant,
it is in line with previous studies that emotional stimuli
elicits higher power than neutral stimuli. However, we found
that in comparison with happy and neutral stimuli, power
elicited by sad stimuli was the lowest. Interestingly, a study by
Baumgartner et al. (2006) found that, when presenting IAPS
pictures of fear, happiness and sadness, there were no differences
in alpha power, but decreased alpha power was found when
stimuli were emotional pictures accompanied with emotional
music. For studies that found that negative stimuli elicits
higher alpha responses, because negative stimuli in these studies
were always angry pictures or affective pictures, the decreased
alpha oscillations may be attributed to the facial classification
mechanisms; however, whether real facial expressions would
have same results as schematic facial expressions requires further
study.
Previous studies on application of IAPS images found
that negative images elicit greater beta responses compared
with positive images in frontal, central and parietal electrodes
(Güntekin and Başar, 2010); however, the stimuli they used
were not facial expression pictures but affective pictures. A
study by Zhang et al. (2013) with Chinese affective pictures as
stimuli indicated that adolescents at the age of 12 exhibit more
beta event-related synchronization (ERS) for positive vs. neutral
stimuli. Other studies also verified that higher beta responses
elicited both positive and negative stimuli than neutral stimuli
(Miskovic and Schmidt, 2010; Cohen et al., 2013).
The ERP results have shown components (i.e., P1, N1,
N170, P2, N2, P3) that have a strong relationship with facial
emotion classification tasks. First, enhanced N170 (a negative
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Positive Classification Advantage
ACKNOWLEDGMENTS
in time window 2 and beta bands in time window 3, so that
people can discriminate happy faces more quickly. This may have
good implications for depressed patients. Therefore, advanced
research on facial emotional processing is clearly needed.
This study was financially supported by the National Natural
Science Foundation of China (grant numbers 81671776,
81471752), the Beijing Municipal Science & Technology
Commission (grant number Z161100002616020), Beijing Nova
Program (grant number Z171100001117057).
AUTHOR CONTRIBUTIONS
TY contributed to the conception of the study. TL and XD
performed the data analyses and wrote the manuscript. NM
and DC contributed significantly to analysis and manuscript
preparation. DC and LZ helped perform the analysis with
constructive discussions. LD and CW provided data processing
ideas and creative methods.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fnhum.2017.006
59/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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