Author's Accepted Manuscript

A Review of Brain Oscillations in Perception ofFaces and Emotional Pictures

Bahar Güntekin, Erol Başar

PII: S0028-3932(14)00099-2DOI: http://dx.doi.org/10.1016/j.neuropsychologia.2014.03.014Reference: NSY5135

To appear in: Neuropsychologia

Cite this article as: Bahar Güntekin, Erol Başar, A Review of Brain Oscillationsin Perception of Faces and Emotional Pictures, Neuropsychologia, http://dx.doi.org/10.1016/j.neuropsychologia.2014.03.014

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A REVIEW OF BRAIN OSCILLATIONS IN PERCEPTION OF FACES AND

EMOTIONAL PICTURES

Bahar Güntekin* and Erol Ba�ar

BBrain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kültür University, Istanbul 34156, Turkey *Corresponding author. Tel.: +0090 212 498 4393; fax: +0090 212 498 4546. Email: b.guntekin@iku.edu.tr Email: bahar.guntekin@hotmail.com Abstract

The differentiation of faces, facial expressions and affective pictures involves processes of

higher mental activity that have considerable applications in the psychology of moods and

emotions. At present, the search for functional correlates of brain oscillations is an

important trend in neuroscience. Furthermore, analyses of oscillatory responses provide

key knowledge on the physiology of brain dynamics. Studies analysing oscillatory

dynamics in face perception and emotional pictures have increased in recent years;

however, the literature lacks a review of the current state of the art. This study provides a

comprehensive review of the delta, theta, alpha, beta and gamma oscillatory responses on

presentation of faces, facial expressions and affective pictures (International Affective

Picture System, IAPS). The reviewed literature revealed that the brain is more sensitive to

emotional stimuli than neutral stimuli. A common and reliable finding from all reviewed

studies was the increased brain responsiveness towards negative emotional pictures (face

expression or IAPS).

2

Highlights

� This study reviews delta, theta, alpha, beta, and gamma oscillatory responses.

� Studies on presentation of faces, facial expressions, and IAPS pictures were reviewed.

� The brain is more sensitive to emotional stimuli than neutral stimuli.

� Studies show increased brain responsiveness toward negative emotional pictures.

Keywords

EEG, Event-Related Oscillations, Evoked Oscillations, Emotion, Face, Face Expression,

International Affective Picture System, Delta, Theta, Alpha, Beta, Gamma

1. Introduction

1.1. Aim of the review

This study provides a comprehensive review of delta, theta, alpha, beta and

gamma oscillatory responses on presentation of faces, facial expressions and International

Affective Picture System (IAPS) pictures. The aim of this study was to attempt to provide

details on the current knowledge on emotional processes by applying the method of

oscillatory dynamics. Researchers frequently analyse emotional states via facial expression

paradigms; to better understand this, one must know the physiology of facial recognition.

Therefore, facial recognition studies are detailed in this review. The review primarily

focussed on the studies that employed visual emotional paradigms; because studies on

auditory emotional paradigms are limited and inconclusive, they were excluded from this

review.

The event-related potential (ERP) research on face/face expression and affective

pictures began in the 1960s (Olofsson et al., 2008). As reviewed earlier (Eimer and

Holmes, 2007; Olofsson et al., 2008; Palermo and Rhodes, 2007), numerous studies have

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been conducted in this field of research. ERP studies focussed on the time, negativity and

positivity of the evoked response. However, another key factor is the frequency of the

signals; besides evoked response, it is also possible to notice the non-phase-locked but

time-locked-induced activity in the analysis of oscillatory dynamics.

A majority of studies on emotional processes using electroencephalography

(EEG) employed ERP; the present review excluded the ERP-based studies. For

information on ERP research studies, refer the reviews by Eimer and Holmes (2007),

Olofsson et al. (2008) and Palermo and Rhodes (2007). Numerous variants, such as power

changes, phase-locking properties, coherence between different electrode sites in different

frequency bands, time–frequency composition of the evoked/event-related/induced

responses, cross-frequency couplings and the relationship between pre-stimulus and post-

stimulus activities, have been introduced in oscillatory response methodology. Because

ERP represents an average evoked response and excludes induced activities, it seemed

impossible to comprehend the EEG dynamics by analysing only the ERP components

(Ba�ar, 1999, 1998, 1980). Analysis of oscillatory responses provides key information on

the physiology of the brain dynamics. In recent years, oscillatory dynamics have been used

to analyse the EEG dynamics of face perception, facial expression and IAPS pictures.

Because the number of such studies has increased in recent years, an up-to-date review of

the methods and principles is lacking.

1.2. How emotion research is performed in the literature

Several techniques have been used in the literature to study emotional processes.

Animal studies monitored recordings from various stimuli, while recording from

intracranial electrodes over different regions of the brain (Ghazanfar et al., 2008; Kalin et

al., 2004; LeDoux, 2000; LeDoux et al., 1988), thus providing advantages of monitoring

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stimulations from deep brain regions. Although animal studies can often be extended to

humans, it is not possible to learn directly about human emotions exclusively using animal

models. In human studies, EEG, magnetoencephalography (MEG) or functional magnetic

resonance imaging (fMRI) have been commonly used to analyse faces, facial expressions

and affective pictures. fMRI offers excellent spatial resolution; however, it has poor

temporal resolution; by contrast, EEG and MEG have the advantage of high temporal

resolution, but the spatial resolution is low. To overcome this, some studies used fMRI

together with EEG (Allen et al., 2000; Babiloni et al., 2005; Bayram et al., 2011; Menon et

al., 2007). However, it is cumbersome to combine these two methods due to technical and

financial difficulties. As shown in classic ERP and event-related oscillation (ERO) studies,

these complex stimulations elicit responses during very early time windows (as early as 80

ms; Palermo and Rhodes, 2007). fMRI fails to detect this early response; EEG and MEG

are necessary to record these rapid brain responses during complex and emotional

stimulations.

In classical ERP research, the N100 response represents detection of a face

(Palermo and Rhodes, 2007; Pegna et al., 2004) and the N170 response represents

recognition of the face (Liu et al., 2002; Palermo and Rhodes, 2007). Differences between

fearful and happy faces over occipital regions were reported to occur as early as 80 ms

(Pizzagali et al., 1999) and those between happy and sad faces in the time window of 90–

110 ms (Halgren et al., 2000; Pourtois et al., 2004). Eimer and Holmes (2002) and Holmes

et al. (2003) showed that frontal regions differentiate fearful faces from neutral faces at

100 ms.

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1.3. Stimuli and paradigms used in electrophysiological research

of face/face expression and affective picture processing

Several visual or auditory stimuli and paradigms were used in

electrophysiological research to assess emotional states. In this review, we primarily focus

on visual stimuli and paradigms. This topic is sufficiently large to be the subject of another

independent review. We will attempt to briefly present these stimuli and paradigms.

Presentation of faces:

The research on presentation of faces primarily focussed on identification and/or

recognition of faces. For identification of faces, researchers used face versus non-face

stimuli, scrambled versus non-scrambled faces and upright versus inverted faces. In

addition, studies used familiar faces, unknown faces or the subject’s own face and famous

versus ordinary; the corresponding pictures were presented via passive viewing, oddball or

Sternberg paradigms. Passive viewing experiments used random or block designs.

Presentation of facial expressions:

While presenting facial expressions, standard picture groups were used: the

‘Pictures of Facial Affect’ by Ekman and Friesen (1976), which includes facial expressions

of ‘happiness’, ‘sadness’, ‘angry’, ‘fear’, ‘surprise’, ‘disgust’ and ‘neutral facial

expression’, was the most commonly used picture group. Based on their interests,

researches used all or some of these facial expressions. Angry, happiness and neutral facial

expressions were the most widely used expressions, to identify the valence (negative,

positive and neutral) or arousal (high arousing versus low arousing). In addition to face

paradigms, passive viewing or oddball paradigm was used. In passive viewing, it was

possible to use random or block designs.

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Presentation of affective pictures:

In presentation of affective pictures, standard picture groups, such as the

‘International Affective Picture System’ (Lang et al., 1999), were commonly used. As

mentioned by Olofsson et al. (2008), different paradigms such as passive viewing (random

or block) and oddball paradigms can be used.

Combined auditory–visual paradigm approaches were also used in a few studies

(Baumgartner et al., 2006; Chen et al., 2010). Baumgartner et al. (2006) reported that the

emotional experience was more profound when visual presentations were combined with

auditory stimuli, intermediate under visual stimuli and minimum during auditory stimuli.

Chen et al. (2010) also reported that subjects recognised audiovisual and only visual

information more accurately than only audio. The results of fMRI studies also showed that

the visual and auditory modalities together could improve recognition of emotions (Ethofer

et al., 2006a, 2006b; Kreifelts et al., 2007). Ghazanfar et al. (2008) showed that monkeys,

when together presented with face and voice stimuli, demonstrated increased strength of

the gamma band activity compared with unimodal (only visual or auditory) conditions.

1.4. The methodology of evoked/event-related oscillatory

responses

Several types of oscillatory activities were reported in the brain:

1) Spontaneous EEG oscillations: Oscillations without any external physical

stimulation.

2) Evoked oscillations: Oscillations evoked by sensory stimulation, for example,

visual or auditory stimulation.

3) Event-related oscillations: Oscillations in response to a trigger such as a task or

strategy, for example, oddball P300 response.

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4) Coherences: Coherences can be measured between two structures in a spontaneous

activity, on sensory stimulation or stimulations with a cognitive task.

An electrical signal within the brain can be coherent in time or space. Hence, event-related

coherences are also called spectral coherences and/or special coherences to separate them

from time coherences.

Evoked/event-related spectra and digital filtering of EPs and ERPs:

Several mathematical tools are available to analyse the evoked/event-related

spectra. One of the most commonly used methods to analyse the average potential (evoked

potential (EP) and/or ERP) is the fast Fourier transform (FFT). Moreover, FFT can be

applied to evoke potential for generating frequency information of the evoked response.

After identifying the cut-off frequencies of evoked power spectra, digital filtering can be

applied to EPs or ERPs (e.g., for delta 0.5–3.5 Hz; for theta 4–7 Hz; for alpha 8–13 Hz; for

beta 14–30 Hz; for gamma 28–48 Hz filters are applied). Furthermore, a grand average is

applied by performing averaging across subjects. It is possible to conduct statistical

analysis of the evoked/event-related power measures and to analyse the peak-to-peak

amplitudes of the filtered oscillatory responses (Ba�ar, 1999, 1998). When FFT or any

other mathematical tool is applied to EPs or ERPs, typically the phase-locked activity is

represented in the transform. However, oscillatory responses could instead include time-

locked activity, also known as the induced activity (Ba�ar, 1999, 1998). To generate

induced activity, FFT or any other mathematical tool (e.g., wavelet transform) can be

applied to the single epochs, following which the evoked and induced activities (total

power) are analysed by averaging FFTs. To obtain only the induced power, evoked power

is excluded from the total power (Herrmann et al., 2004).

8

Continuous wavelet transform is also a commonly used method to analyse

evoked/event-related or induced activity. Several software packages such as Brain Vision

Analyzer and EEGLAB are available to analyse evoked/event-related and/or induced

power by FFT or wavelet transform. Caution is necessary in these analyses; depending on

the software, it is possible to use different methodologies. For example, it is possible to

analyse the evoked power without subtracting the pre-stimulus activity and to subtract the

pre-stimulus activity to view an enhanced representation of the oscillatory activity. The

analysis of event-related desynchronisation (ERD) represents another example of the non-

standardised methods. In EEGLAB (Delorme and Makeig, 2004), ERD is calculated by

subtracting the pre-stimulus activity; the evoked power component remains in the analysed

signal. In another ERD analysis method, evoked power was subtracted before determining

the EEG power by means of subtracting the phase-locked component (Kalcher and

Pfurtscheller, 1995). Both methods are commonly applied in ERD; however, caution is

needed because the first method includes both the phase-locked and the non-phase-locked

activities; however, in the second method, the phase-locked activity is excluded. Hence,

although both methods are referred to as ERD, they do not calculate the same oscillatory

responses. These are examples of the lack of standardisation in the analysis of oscillatory

responses; there are several different methods of analysis in the literature, but standard

application methods are yet to be developed.

Phase-locking across trials:

Event-related phase consistency across the trial is an important method allowing

researchers to observe how phase information varies between the trials. Kolev et al. (1997)

have used single-sweep wave identification (SSWI) histograms to analyse phase locking.

Tallon-Baudry et al. (1996) defined a method known as phase-locking factor, which was

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later known as intertrial phase coherence (ITPC) method (Delorme and Makeig, 2004).

Lauchaux et al. (1999) defined the phase-locking statistics method, in which responses to

repeated stimuli were used to identify latencies at which the phase difference between the

signals varies little across the trials and between the two electrode sites.

2. Event-related Oscillations on Presentation of

Faces/Facial expressions and IAPS Pictures

2.1. Delta oscillatory responses

It was suggested that EEG delta oscillations are generated by summation of long-

lasting afterhyperpolarisations produced by pyramidal neurons (Streiade, 1993; Streiade

and Buzsaki, 1990; Streiade et al., 1990). Recent studies have also suggested that glial cells

modulate the neuronal excitability and contribute to the pacing of slow oscillation

(Amzica, 2002; Amzica and Steriade, 2000). According to Steriade (1993), delta

oscillations are generated by the interplay between the intrinsic currents from the

thalamocortical cells. Delta oscillations have also been recorded from nucleus accumbens

(Laung and Yim, 1993), dopaminergic neurons in the ventral tegmental area (Grace, 1995),

ventral pallidum (Lavin and Grace, 1996) and the brain stem (Lambertz and Langhorst,

1998).

Delta response oscillations play an important role in cognitive processes. One of

the paradigms used to analyse cognitive processes is the oddball paradigm. P300 response

is an important component analysed in these studies. Prolongations of theta, delta and

alpha oscillations were described for the target stimuli (Ba�ar-Ero�lu et al., 1992; Demiralp

and Ademo�lu, 2001; Öniz and Ba�ar, 2009; Stampfer and Ba�ar, 1985; Yordanova and

Kolev, 1998). A series of studies by our group and other groups indicate that the delta and

theta oscillations are the major operating rhythms of P300 (Ba�ar et al., 2001; Ba�ar-

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Ero�lu et al., 1992; Demiralp et al., 1999; Karaka� et al., 2000; Kolev et al., 1997; Spencer

and Polich, 1999; Yordanova et al., 2000). Furthermore, the delta response oscillations are

higher in response to target stimuli compared with non-target and simple sensory stimuli.

Güntekin and Ba�ar (2010a) analysed the long-distance intra-hemispheric event-related

coherence and evoked coherence by applying an auditory oddball paradigm and simple

sound, respectively; the results showed that the delta coherence values in response to target

stimuli were higher than non-target and simple auditory response coherence values. The

delta oscillations are the primary component of P300 response in long-range connections

because they were in local circuits. Studies on cognitively impaired subjects also support

the idea that delta response oscillations are important in cognitive processes, such as

attention, signal detection, recognition and decision making. One of the common findings

in several neurological disorders, such as schizophrenia (Bates et al., 2009; Doege et al.,

2010b; Ergen et al., 2008; Ford et al., 2008) and Alzheimer’s disease (AD) (Yener et al.,

2012, 2008), was decreased delta activity on cognitive load. Güntekin et al. (2008)

investigated the event-related coherence on patients with AD using a visual oddball

paradigm. Healthy subjects showed a higher evoked coherence in the delta frequency band

compared with the untreated and treated subjects with AD. Another study, with a large

group of patients (Ba�ar et al., 2010), reported that the healthy control group demonstrates

a significantly higher event-related coherence in the delta frequency range than the

untreated and treated AD groups. However, simple light stimulus did not induce any

changes in patients with AD compared with the healthy controls.

The increased delta response oscillations on cognitive paradigms are mostly

reported in frontal–central and parietal locations. However, in emotional paradigms,

fronto-central–parietal regions as well as occipital regions play important roles, which will

be discussed in detail in the following section.

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2.1.1. Delta oscillatory responses on presentation of

face, facial expression and IAPS pictures

Ba�ar et al. (2007) showed that occipital delta responses were high when presented with

anonymous or grandmother faces (familiar face) compared with those with light

stimulation. Ba�ar et al. (2008) analysed the effect of feelings of love on face perception.

These authors presented pictures of a ‘loved person’ to female subjects and compared the

elicited responses to pictures showing faces of a ‘known and appreciated person’ or an

‘unknown person’ during EEG recordings. Their results showed that in frontal regions, the

delta response to the picture of the ‘loved person’ showed significantly higher amplitude

values, in comparison with the ‘unknown person’ as well as with the picture of the

‘appreciated person’. Sakihara et al. (2012) showed that a familiar face elicited a higher

delta response over parietal and left temporal regions than an unfamiliar face. Thatcherized

faces (in ‘thatcherized’ faces the eyes and mouth regions are turned upside down) were

compared with non-thatcherized faces which also elicited higher delta response

(Gersenowies et al., 2010). Wilkinson et al. (2012) analysed the effect of galvanic

vestibular stimulation on delta oscillatory responses during face perception. These authors

applied sub-sensory (0.4 mA) and supersensory (1–1.2 mA) galvanic stimulations to the

healthy subjects while recording EEG and during face perception. They reported maximal

delta band activity during supersensory face stimulation in comparison with baseline.

Furthermore, Ba�ar et al. (2008) reported enhanced delta responses over posterior locations

on presentation of various face pictures (beloved, known and appreciated and unknown

persons) and compared with light stimulus. Presentation of facial expression paradigms

(angry, happy and neutral) elicited higher occipital delta responses than frontal delta

responses (Balconi and Lucchiari, 2006; Güntekin and Ba�ar, 2009). According to these

studies, it can be concluded that the increase in occipital delta response is a consequence of

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face processing, but not due to recognition of faces or facial expressions. However, caution

is needed before presenting strong conclusions. Is the increased delta response to faces and

facial expression stimuli unique for only face/facial expression paradigms? Or is delta

response also a result of increase in recognition of a different object? This question should

be addressed in future studies.

Kynazev et al. (2009) analysed delta response oscillations on presentation of

angry, happy and neutral faces. They showed that delta synchronisation is stronger on

presentation of ‘emotional’ than ‘neutral’ stimuli in more sensitive and emotional subjects.

Furthermore, they reported that delta synchronisation was pronounced during unconscious

processing periods (before 300 ms post-stimulus); on the other hand, when the subject was

asked to evaluate the emotional content of the stimulus, the synchronisation was

pronounced at a later processing stage, which was presumably associated with the

conscious processing. Their findings also showed that more sensitive or emotional subjects

show greater delta synchronisation. Therefore, it can be concluded that interindividual

differences play an important role in studies investigating emotional states. The use of

psychological tests or questionnaires investigating the subjects’ emotional sensitivity

combined with analysis of event-related oscillations could provide more conclusive

answers. Consistent with the findings from Kynazev et al. (2009), studies by Klados et al.

(2009) and Balconi et al. (2009a, 2009b) showed that high-arousal pictures elicit greater

event-related delta synchronisation compared with the low-arousal ones. Furthermore,

Klados et al. (2009) showed that female subjects show greater event-related delta

synchronisation than the male subjects. On the other hand, we previously reported higher

delta response among female subjects compared with male subjects on application of

simple visual stimuli (Güntekin and Ba�ar, 2007c); therefore, it is possible that the delta

response among female subjects is a common finding for all types of visual stimuli,

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including a simple visual stimulus. This possibility should be investigated in future studies.

Accordingly, more conclusive data could be achieved by applying various visual

paradigms in both genders.

The P300 and positive slow wave activities between 400 and 900 ms on

application of emotional paradigm are termed ‘late positive potential’ (Cuthbert et al.,

2000; Olofsson et al., 2008). As mentioned in the previous section, the P300 and the slow

wave responses primarily consist of delta frequency range. The results on delta response

oscillations and slow wave activity on emotional stimuli are in good agreement. Delta

synchronisation is stronger on presentation of ‘emotional’ than ‘neutral’ stimuli (Balconi et

al., 2009a, 2009b; Klados et al., 2009; Kynazev et al., 2009). ERP studies also showed

more prominent slow wave activity on presentation of ‘emotional’ than ‘neutral’ stimuli

(Amrhein et al., 2004; Codispoti et al., 2006a, 2006b; Delplanque et al., 2005; Dolcos and

Cabeza, 2002; Keil et al., 2002; Mini et al., 1996; Olofsson and Polich, 2007; Palomba et

al., 1997; Pollatos et al., 2005; Schupp et al., 2000; Sabatinelli et al., 2007; Schupp et al.,

2007b; Wood and Kisley, 2006; Olofsson et al., 2008). On the other hand, some studies

reported higher slow wave activity in response to negative emotions than positive emotions

(Johnston et al., 1986; Ito et al., 1998). Gasbarri et al. (2006) reported that females show

higher P300 response than males to high-arousing emotional stimuli. Accordingly, Klados

et al. (2009) showed that female subjects have greater event-related delta synchronisation

than males. Based on these studies, we conclude that in emotional paradigms (as in

cognitive paradigms), slow wave activity of ERP primarily consists of delta (0.5–3.5 Hz)

oscillations. Further research should address how various emotional stimuli affect delta

responses on presentation of emotional stimuli. There could be variations in sub-delta

frequency bands on presentation of different emotional stimuli (between paradigms: face,

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face expression, IAPS pictures; and within paradigms: known, unknown faces, angry,

happy, neutral facial expressions; unpleasant, pleasant, neutral pictures).

Figure 1 summarises the studies mentioned above.

As mentioned in previous sections, cognitive load causes reductions in delta

response oscillations and coherences in patients with schizophrenia (Bates et al., 2009;

Doege et al., 2010b; Ergen et al., 2008; Ford et al., 2008) and AD (Ba�ar et al., 2010;

Güntekin et al., 2008; Yener et al., 2012, 2008). The analysis of event-related delta

oscillations on presentation of faces and facial expressions and IAPS pictures in patients

with AD could be important for understanding the face perception and emotional processes

in patients with AD. It can be hypothesised that delta response oscillations would be

reduced in patients with AD compared with healthy subjects on presentation of emotional

paradigms, as observed in cognitive paradigms. Event-related delta oscillations in patients

with schizophrenia and bipolar (BP) disorder should also be analysed on emotional stimuli,

to better understand the emotional states among these patient groups.

2.2. Theta oscillatory responses

Spontaneous theta frequency band oscillations were first described in rabbit

hippocampus by Saul and Davis (1933), and later by Green and Arduini (1954). Theta

rhythm has been considered to be the fingerprint of all limbic structures; it is most

prominent in hippocampal formation (Lopes da Silva, 1992, 1990). Theta oscillations are

the dominant rhythms of the frontal cortex (Ba�ar, 1998; Westphal et al., 1990). On the

other hand, synchronised occipital theta oscillations were reported on presentation of

15

emotional paradigms (Aftanas et al., 2002, 2001a; Ba�ar et al., 2006; Güntekin and Ba�ar,

2009).

Ba�ar-Ero�lu et al. (1991a, 1991b) performed a series of experiments with freely

moving cats using a passive P300 paradigm. These authors showed that the P300 potential

generates from several cortical and subcortical regions, including reticular formation,

hippocampus and auditory cortex. Furthermore, P300 potential exhibits the largest

amplitude in CA3 of hippocampus, and the hippocampal P300 potential manifests an

enhancement of theta activity of the field potential (Ba�ar-Ero�lu et al., 1991a, 1991b).

Theta oscillations are primarily generated from the pyramidal and granule cells

(Buzsaki, 2006, 2002). Event-related theta oscillations are suggested to play a role in

memory, attention and cognition-related processes (Ba�ar, 1999, 1998; Klimesch, 1999;

Klimesch et al., 1997; Kahana et al., 2001; Khader et al., 2010; Sauseng et al., 2010).

Gruber and Müller (2006) reported an increase in induced theta amplitudes in response to

repeated familiar stimuli as opposed to those that were initially presented. The amplitude

of theta response was higher for familiar items than for foreign or past items (Burgess and

Gruzelier, 2000, 1997; Düzel et al., 2005; Doppelmayr et al., 2000; Klimesch et al., 1997;

Osipova et al., 2006; Weiss and Rappelsberger, 2000). The amplitude of theta oscillations

was described for target stimuli on presentation of an oddball paradigm (Ba�ar-Ero�lu et

al., 1992; Stampfer and Ba�ar, 1985).

Reduced theta oscillatory activity on cognitive load was common among several

diseases, including schizophrenia (Bates et al., 2009; Doege et al., 2010a, 2010b; Ford et

al., 2008; Haenschel et al., 2009; Pachou et al., 2008; Shmiedt et al., 2005b), AD

(Caravaglios et al., 2010; Cummins et al., 2008; Deiber et al., 2009; Missonnier et al.,

2006; Yener et al., 2007), attention deficit hyperactivity disorder (ADHD) (Groom et al.,

2010), Parkinson’s (Schmiedt et al., 2005a) and BP disorder (Atagün et al., 2013).

16

Furthermore, evoked theta responses were reduced in visual steady-state responses among

patients with schizophrenia when compared with healthy controls (Riecansky et al., 2010).

2.2.1. Theta oscillatory responses on presentation of face and

facial expression pictures

Zion-Golumbic et al. (2010) reported higher theta activity for known faces

compared with unknown faces. Crespo-Garcia et al. (2012) showed that elderly subjects

have lower theta power than young adults on presentation of face stimulation. Ba�ar et al.

(2007) showed that familiar faces elicit higher and quicker-theta (6–8 Hz) response than

unknown faces at frontal electrodes. Miyakoshi et al. (2010) analysed event-related theta

oscillations on presentation of subject’s own face, familiar face and unfamiliar face. The

authors reported a decrease in theta-phase locking in response to own face in comparison

with familiar and unfamiliar faces. Lindsen et al. (2010) asked subjects to indicate their

preference between the two different faces. Increased frontal theta response was noticed

among subjects who preferred the second face.

Facial expression paradigms showed increased theta oscillations in response to

negative expressions. Balconi and Lucchiari (2006) observed an increase in frontal theta

synchronisation on presentation of an emotional facial expression compared with neutral

expression. Zhang et al. (2012) showed that fearful facial expressions elicit higher theta

synchronisation than neutral expressions. Knyazev et al. (2009) also reported stronger theta

synchronisation on presentation of emotional (angry and happy) faces than neutral faces.

These authors also reported stronger theta synchronisation in subjects with sensitive or

emotional experiences. Knyazev et al. (2009) employed two different tasks and two

different samples: in the explicit task, subjects were asked to recognise the emotion and in

the implicit task, to differentiate between genders. The results showed that in the implicit

17

experiment, synchronisation peaked at an early processing state (before 250 ms); however,

in the explicit experiment, synchronisation peaked at a late processing state (after 250 ms).

Knyazev et al. (2010) also showed that the early processing stage of theta synchronisation

was more pronounced in men, whereas late processing stage was more pronounced in

women. The same group also showed that anger scores among subjects with low anxiety

were positively related to the extent of theta synchronisation on face presentation

(Bocharov and Knyazev, 2011).

Gonzalez-Roldan et al. (2011) analysed theta response on presentation of faces

expressing pain and anger along with neutral expression. Faces with painful expression

were judged to be more unpleasant and arousing than angry and neutral faces. In addition,

increased theta activity in the latency of 200–400 ms was observed to be more intense in

comparison to low-intensity faces.

Figure 2 summarises the above-mentioned studies.

2.2.2. Theta oscillatory responses on presentation of

emotional film clips and IAPS pictures

Pioneering studies in this region were conducted (Aftanas et al., 1998a, 1996a,

1996b) by examining event-related theta oscillations on presentation of emotional

paradigms. Aftanas et al. (2005a, 2005b, 1998a, 1998b) analysed event-related oscillations

on presentation of film clips of different emotional valence (neutral, positive, negative) and

of IAPS pictures (Aftanas et al., 2003a, 2003b, 2002, 2001). These studies included

healthy subjects, alexithymic versus non-alexithymic subjects (Aftanas et al., 2003b),

subjects with high- and low-anxiety traits (Aftanas et al., 2005b, 2003a) and Sahaja Yoga

meditators versus control subjects (Aftanas et al., 2005a).

18

Aftanas et al. (1998a) analysed the oscillatory spectra from 76 healthy subjects on

presentation of emotionally negative, positive and neutral film clips. In comparison with

the control neutral condition, presentation of the affective film categories yielded a power

decrease in response to negative films. On the other hand, theta power was increased in

response to positive affective induction. Aftanas et al. (1998b) subsequently analysed the

evoked coherence among healthy subjects on presentation of negative, positive and neutral

video clips; the theta frequency band between emotionally negative and neutral film

categories resulted in a significant decrease in the intra- and inter-hemispheric coherences

between the left-prefrontal and frontal, central and parietal regions. By contrast, these

authors reported a significant increase in coherence between paired posterior sites.

Although negative emotional video clips elicited decreased theta power, the authors

observed increased theta response by unpleasant IAPS pictures. Greater right-hemisphere

theta power for unpleasant IAPS pictures and greater left-hemisphere theta power for

pleasant IAPS pictures were reported by Aftanas et al. (2001). Furthermore, the same study

showed that in the posterior brain regions, affective valence, but not neutral stimuli,

triggers the theta power. Sun et al. (2012) subsequently showed that harmful cues elicit

higher theta responses than neutral cues in the posterior region on presentation of IAPS

pictures. Similar results were noticed on presentation of facial expression stimuli (Ba�ar et

al., 2006; Güntekin and Ba�ar, 2009). Studies showed that theta oscillations are dominant

in frontal cortex. Several authors reported elevated frontal theta response on cognitive load

(Ba�ar-Ero�lu et al., 1992; Mazaheri and Picton, 2005; Stampfer and Ba�ar, 1985).

However, increased parietal–occipital theta responses were found on presentation of

emotional stimuli (Aftanas et al., 2001; Ba�ar et al., 2006; Güntekin and Ba�ar, 2009;

Gonzalez-Roldan et al., 2011). The findings of Aftanas et al. (2002) and Balconi et al.

(2009b, 2009c) showed that arousal discrimination is associated with increased theta power

19

on presentation of IAPS pictures. These authors showed that in theta band, high-arousal

stimuli induce larger synchronisation in comparison with low-arousal stimuli. In contrast,

Miskovic and Schmidt (2010) reported a decrease in theta coherence in right-hemispheric

networks in response to affective IAPS pictures.

2.3 Alpha oscillatory responses Sensory alpha responses in animals have been described by several authors,

starting with the works of Ba�ar et al. (1975a, 1975b, 1975c), Ba�ar (1972), Spekreijse et

al. (1972) and Ba�ar (1980); in human brains Ba�ar (1976), Dinse et al. (1997) and Dudkin

et al. (1978) described visually evoked oscillations at the cellular level. Ba�ar and Stampfer

(1985) first described the association between alpha activity and working memory. Alpha

oscillations were found to be prolonged by oddball paradigm stimulus (Ba�ar and

Stampfer, 1985; Kolev et al., 1999; Stampfer and Ba�ar, 1985; Öniz and Ba�ar, 2009).

Klimesch’s group conducted a series of studies to analyse the difference between the good

and bad memory by means of oscillations (Doppelmayr et al., 2005, 2000; Klimesch et al.,

2007, 1997). Recently, Klimesch et al. (2007) launched a hypothesis known as ‘the

inhibition–timing hypothesis’. According to these authors, alpha ERD reflects gradual

release of inhibition related to activation, whereas event-related synchronisation plays an

important role in inhibitory control and cortical processing. Alpha desynchronisation has

been shown to correlate with working memory and intelligence (Doppelmayr et al., 2005,

2000; Klimesch et al., 1997; Vogt et al., 1998). In recent studies, Ba�ar (2012) and Ba�ar

and Güntekin (2012) reviewed alpha activity and discussed that for now, it does not seem

possible to launch a general hypothesis for alpha activity. Furthermore, these authors stated

that “alpha activity has important functional correlates, including sensory, motor, and

memory functions. Alpha oscillations serve as building blocks in several functions.”

20

As we stated in our recent review (Ba�ar and Güntekin, 2012), “the degree of

responsiveness in the alpha frequency range is proportional to the amplitude of pre-

stimulus alpha activity; in cases where no alpha activity is recorded in prestimulus EEG,

desynchronization does not occur” (Barry et al., 2006; Brandt, 1997; Rahn and Ba�ar,

1993a, 1993b; Stampfer and Ba�ar, 1985). Furthermore, in an experiment conducted in

low-illumination conditions, subjects did not show spontaneous alpha activity; however,

following a single stimulation, an immense increase in alpha response oscillations was

observed (Ba�ar et al., 1976).

Spontaneous EEG alpha activity was found to be lower in subjects diagnosed

with schizophrenia (Alfimova and Uvarova, 2008; Itil et al., 1974, 1972; Iacona, 1982;

Miyauchi et al., 1990; Sponheim et al., 2000, 1994), AD (Adler et al., 2003; Babiloni et al.,

2004, 2009a, 2009b; Dunkin et al., 1994; Locatelli et al., 1998; Leuchter et al., 1987;

Rossini et al., 2007) or BP disorder (Ba�ar et al., 2012; Clementz et al., 1994).

Differences in alpha oscillatory responses were also noticed in various

pathologies on presentation of different paradigms (Ba�ar and Güntekin, 2012, 2008).

Several authors reported a decreased alpha response in patients with schizophrenia in

comparison with healthy subjects on visual steady-state stimulation (Jin et al., 2000, 1997,

1995, 1990; Rice et al., 1989; Wada et al., 1995). Patients with schizophrenia also showed

abnormalities in alpha responses on cognitive stimulation (Ba�ar-Ero�lu et al., 2008, 2009;

Bachman et al., 2008; Haenschel et al., 2010, 2009; Koh et al., 2011).

2.2.3. Alpha oscillatory responses on presentation of faces,

facial expressions and IAPS pictures

Alpha oscillations during emotional processes have mostly been studied by

analysing the resting state frontal alpha asymmetry, starting with the work of Davidson et

21

al. (1979). Researchers suggested that greater left-frontal activity was associated with

greater positive affect, and greater right-frontal activity was associated with greater

negative affect. Since then, the frontal alpha asymmetry was studied by several groups

(Fox, 1991; Hagemann et al., 2002; Harmon-Jones and Allen, 1997; Jaworska et al., 2012a,

2013; Sutton and Davidson, 1997). Some studies supported the hypothesis of alpha

asymmetry (Coan and Allen, 2003b; Davidson, 1998a; Hagemann et al., 2002), whereas

others expressed concerns about a lack of proper evidence (Hagemann et al., 1998; Heller

and Nitschke, 1998; Reid et al., 1998). Approach motivation was associated with greater

left-frontal activity, whereas withdrawal motivation was associated with greater right-

frontal activity (Fox, 1991; Harmon-Jones and Allen, 1997; Sutton and Davidson, 1997).

Earlier studies used spontaneous EEG and statistical analysis to determine the subject’s

affective style and its association with the frontal alpha asymmetry. There are a few studies

on frontal alpha asymmetry that investigated the effect of various emotional paradigms.

Pönkanen and Hietanen (2012) showed that presentation of pictures with different gaze

directions and facial expressions (happy or neutral) did not affect frontal alpha asymmetry.

In a study by Harmon-Jones (2007), participants were exposed to pictures that evoked

anger, fear, disgust, positive and neutral affective reactions. The affective pictures did not

evoke reliable shifts in asymmetrical cortical activation. Because the present study was

planned to review evoked/event-related oscillations on application of different emotional

paradigms, we will not discuss the relationship between spontaneous alpha activity and

affective style in detail. For further information on emotional processes and frontal alpha

asymmetry, refer reviews by Coan and Allen (2004), Davidson (2003, 2004) and

Hagemann (2004).

In our previous study, we showed that the alpha responses were significantly

higher on presentation of angry face stimulation in comparison with happy face stimulation

22

at T5, P3 and O2 electrode locations (Güntekin and Ba�ar, 2007). On the other hand,

Balconi et al. (2009a, 2009b) reported a decrease in alpha power for positive and negative

or arousing emotions in comparison with neutral stimuli. Balconi and Mazza (2009)

showed that desynchronisation of alpha correlates with higher behavioural inhibition

system (BIS) measures in the right-frontal region.

Aftanas et al. (2002) analysed ERS and ERD in three alpha sub-bands (6.21–8.28;

8.28–10.35 and 10.35–12.46) in response to IAPS stimuli with low-arousal (LA),

moderate-arousal (MA) and high-arousal (HA) content. The MA and HA versus LA

stimuli yielded larger alpha-1 synchronisation, which was predominantly over occipital

leads. Furthermore, in the alpha-3 band, HA stimuli induced a lateralised time-dependent

power increase over anterior leads of the left hemisphere. In an MEG study, Onada et al.

(2007) investigated event-related power changes of alpha activity on stimulation by

affective images (IAPS). The power changes in the occipital region in affective cue

negative condition were larger than those in other conditions (neutral, positive). Their

results suggested that alpha ERD depends on the affective style of the anticipated stimulus.

It should also be noted that the anticipation of painful stimuli also caused ERD of alpha

activity (Babiloni et al., 2003; Del Percio et al., 2006). Baumgartner et al. (2006) reported

no difference in alpha power between IAPS pictures of ‘fear’, ‘happiness’ and ‘sadness’,

but reported decreased alpha power when emotional pictures were presented with

emotional music.

Zion-Golumbic et al. (2010) reported larger ERD for popular faces compared

with ordinary faces. Rousselet et al. (2007) reported that 5–20 Hz oscillations showed

larger amplitude in a 50–300 ms time window after the onset of face stimulus compared

with images of objects. In an MEG study, Tuladhar et al. (2007) showed that strong alpha

activity emerged approximately 1 s after presentation of faces in the Sternberg task.

23

Furthermore, they observed systemic increase in alpha power as the memory load

increased. These authors did not exclude the phase-locked component from the signal, but

subtracted the baseline activity from the response. Most studies on ERD on presentation of

emotional stimuli employed the method presented by Kalcher and Pfurtscheller (1995),

which included subtracting the phase-locked component before calculating the EEG

power. However, it is important to note that, although this analysis successfully reveals the

ERD of alpha activity, it may filter the alpha responses that are known to have specific

functions (Ba�ar and Stampfer, 1985; Busch and Herrmann, 2003; Kolev et al., 1999; Öniz

and Ba�ar, 2009; Stampfer and Ba�ar, 1985). In previous studies on delta, theta, beta and

gamma oscillatory responses, the phase-locked component was occasionally excluded from

the signal. However, because blocking or desynchronisation of alpha activity was reported

more often than other frequency bands, this methodology was more commonly used in the

analysis of alpha oscillatory responses.

Compared with other frequency bands (delta, theta, beta and gamma), the effect

of emotional processes on alpha oscillatory responses was unclear. Research on alpha

response and emotional processes mostly focussed on spontaneous EEG frontal alpha

asymmetry or ERD of alpha activity. Therefore, advanced research on emotional

processes, where the alpha oscillatory responses can be analysed without removing the

phase-locked activity, is clearly needed. In future, phase-locking, evoked and/or induced

power, time–frequency compositions, evoked/event-related coherence of alpha oscillatory

responses on presentation of face, face expression and IAPS pictures should be analysed to

understand the complete dynamics of alpha activity. The above-mentioned studies used

different methodologies, so it seemed impossible to directly compare the results.

24

2.1. Beta oscillatory responses

Beta response oscillations are commonly related to the sensorimotor functions,

and reduced by voluntary movements and motor imagery (Engel and Fries, 2010;

Pfutscheller et al., 1989, 1996; Neuper et al., 2009). Traub et al. (1999), Haenschel et al.

(2000) and Kisley and Cornwell (2006) concluded that the beta band activity is closely

related to stimulus-driven salience. In a recent review, Engel and Fries (2010) discussed

the relationship between beta responses and the mechanisms that maintain the status quo.

These authors suggested that if beta responses maintain the status quo during sensory and

cognitive processes, tasks involving strong endogenous top-down component should be

associated with increased beta response. On the other hand, decrease in beta responses

would be observed during exogenous bottom-up factors. These authors hypothesised that

beta band activity and/or coupling in the beta band are expressed strongly, if the status quo

is intended or predicted.

Several studies have shown that evoked oscillatory beta activity was associated

with projections from sensory-specific cortex (Haenschel et al., 2000; Sakowitz et al.,

2005; Senkowski et al., 2006; Tzelepi et al., 2000). Auditory stimuli enhanced beta

responses from central and temporal electrodes (Haenschel et al., 2000; Makinen et al.,

2004; Peterson and Thaut, 2002); on the other hand, visual stimuli enhanced beta responses

from occipital electrodes (Senkowski et al., 2006).

Higher beta responses were reported during multisensory stimuli in comparison

with unisensory stimuli. Sakowitz et al. (2005) reported increased beta activity for

multisensory responses compared with that for summed unisensory responses, starting at

50 ms after stimulation onset. Senkowski et al. (2006) demonstrated widespread increase in

25

beta enhancement on presentation of multisensory stimuli compared with unisensory

stimuli, peaking at approximately 120 ms.

To the best of our knowledge, there are six prior studies that analysed event-

related beta oscillations on application of an oddball paradigm in healthy subjects (Cacace

and McFarland, 2003; Mazheri and Picton, 2005; Güntekin et al., 2013, in press; Ishii et

al., 2009; Kukleta et al., 2009a, b). In three of these studies, subjects were required to press

a button during presentation of target stimuli (Cacace and McFarland, 2003; Ishii et al.,

2009; Mazheri and Picton, 2005). The authors reported beta ERD on presentation of target

stimulation. Furthermore, they suggested that the decrease in beta responses could be

movement dependent. Using a slightly different methodology, Kukleta et al. (2009a) asked

the subjects to press the button and at the same time mentally count the target stimuli. They

reported increased beta synchronisation in response to both target and non-target stimuli.

In a recent study (Güntekin et al., 2013, in press), we showed that event-related beta power

was greater on presentation of target stimulation compared with non-target stimulation.

Increased beta responses were also reported in different working memory paradigms

(Onton et al., 2005; Ravizza et al., 2005; Tallon-Baudry et al., 1998). In an auditory

working memory paradigm, Peterson and Thaut (2002) reported that auditory working

memory induces beta synchronisations in the right temporal cortex.

According to the above-mentioned studies, beta oscillatory responses increase on

high arousal, multisensory stimulation and by cognitive load. Accordingly, as Wrobel

(2000) discussed, the beta responses could shift the system to an attention state. As

explained in the next section, beta oscillations were also increased on negative emotional

stimulation.

26

2.1.1. Beta oscillatory responses on presentation of face, facial

expression and IAPS pictures

An association between the beta response oscillations and emotional stimuli has

been reported in various studies. In a spontaneous EEG study, Schutter et al. (2001)

reported a significant relationship between the asymmetry in parietal beta activity and the

attentional response to angry faces. We previously reported increased beta responses at F3

and CZ electrodes to ‘angry’ facial expressions compared with ‘happy’ expressions

(Güntekin and Ba�ar, 2007a). We also showed that male subjects have lower occipital beta

responses than female subjects on presentation of facial expression paradigms (Güntekin

and Ba�ar, 2007b). Furthermore, our studies indicated greater beta responses for negative

images compared with those for positive images in frontal, central and parietal electrodes

on application of IAPS images (Güntekin and Ba�ar, 2010b). These results were supported

by Woodruff et al. (2011), who demonstrated that valence IAPS imagery induces greater

beta power than neutral images. Miskovic and Schmidt (2010) examined coherences

during affective image presentation and found an increase in beta responses during free

viewing of images that were highly arousing (pleasant and unpleasant) and those that were

not (neutral) emotionally arousing images. This finding supports that neural activity in the

beta range relates to processing of emotional stimuli (Güntekin and Ba�ar, 2010b, 2007a,

2007b; Miskovic and Schmidt, 2010; Woodruff et al., 2011). Using complex stimuli,

including facial expression, body language and vocal information, Jessen and Kotz (2011)

investigated multisensory emotion perception. In contrast to previous findings, these

authors reported suppression in both alpha and beta responses for emotional stimuli and

not for neutral stimuli. Since Jessen and Kotz (2011) used complex emotional stimulation,

including visual, auditory and visio-auditory factors, it is impossible to directly compare

their results with those of the other mentioned studies. In a study analysing the influence of

27

facial expressions on pain perception, Senkowski et al. (2011) showed that presentation of

faces with emotional expressions leads to a stronger bilateral suppression of the pain-

induced beta band activity compared with the faces with neutral expressions, possibly

reflecting enhanced response readiness from the sensorimotor system.

Event-related beta oscillations on presentation of known and unknown faces were

also analysed in the literature. Özgören et al. (2005) reported prolonged frontal event-

related beta oscillations in response to unknown faces compared with those to known

faces. Sakihara et al. (2012) showed that event-related beta synchronisation over the right

prefrontal region was significantly greater in response to the participant’s own face than to

a familiar face at 400–800 ms post-stimulus.

Figure 3 summarises the above-mentioned studies.

2.2. Gamma oscillatory responses

According to Whittington et al. (2010), three distinct but interrelated forms of

gamma rhythms have been characterised: interneuron network gamma, pyramidal

interneuron network gamma and persistent gamma. Interneuron network gamma is the

simplest and can be seen in network of inhibitory interneurons alone (Traub et al., 1998).

Both the inhibitory and excitatory cells are involved in generating the pyramidal

interneuron network gamma. The predominant mechanism underlying the pyramidal

interneuron network gamma involves phasic excitation presented to interneurons following

orthodromic spike generation in principle cells. Finally, the authors reported that tonic

activation of kainate, muscarinic and metabotropic glutamate receptors generates gamma

oscillations that persist for several hours (Whittington et al., 2010). Herrmann et al. (2010)

discussed that intra-cortical inhibitory mechanisms cannot entirely explain data measured

28

from the scalp. According to these authors, induced gamma activity can be explained by

synchronising the impact of inhibitory interneurons, as explained by Traub et al. (1998)

and Whittington et al. (2010). On the other hand, Herrmann et al. (2010) reported that

evoked gamma response may be related to excitatory inputs.

A number of excellent reviews on cellular mechanisms and

cognitive/behavioural correlates of gamma oscillations are available in the literature (Ba�ar

2013; Ba�ar et al., 2001; Ba�ar-Ero�lu et al., 1996b; Herrmann et al., 2010; Herrmann and

Knight, 2001; Herrmann et al., 2004; Jensen et al., 2007; Singer, 1999; Tallon-Baudry and

Bertrand, 1999).

According to Ba�ar et al. (2001), auditory and visual gamma responses are

selectively distributed in different cortical and subcortical structures. They are phase-

locked stable components of EPs in cortex, hippocampus, brain stem and cerebellum of

cats occurring 100 ms after the sensory stimulation, with a second window of

approximately 300 ms latency (Ba�ar, 1999, 1980; Schurmann et al., 1997). Application of

six cognitive paradigms showed that the 40-Hz response during the 100-ms after

stimulation has a sensory origin, independent of cognitive tasks (Karaka� and Ba�ar, 1998).

A P300–40-Hz component has been recorded in the cat hippocampus, reticular formation

and cortex by utilisation of omitted auditory stimuli as a target. This response occurs

approximately 300 ms post-stimulation, being superimposed with a slow wave of 4 Hz

(Ba�ar-Eroglu and Ba�ar, 1991). Ba�ar-Ero�lu et al. (1996a) reported an increase in

gamma response to ambiguous stimulation. During repeated presentation of familiar

stimuli, a decrease in induced gamma band responses was reported (Wiggs and Martin,

1998). On the other hand, unfamiliar stimuli enhanced gamma band responses during

presentation of unfamiliar stimuli (Fiebach et al., 2005; Gruber and Müler, 2005, 2006;

Henson et al., 2000).

29

Researchers have proposed that gamma-band activity may primarily reflect the

binding processes (Singer and Gray, 1995; Tallon-Baudry and Bertrand, 1999). Several

studies suggested that gamma oscillations are also connected to memory processes (Ba�ar

Ero�lu and Ba�ar, 1991; Debener et al., 2003; Gruber et al., 2002; Herrmann et al., 2004;

Karaka� et al., 2000; Kaiser et al., 2003; Miltner et al., 1999; Tallon-Baudry et al., 1998).

Herrmann et al. (2004) proposed a new framework that related gamma oscillations to two

underlying processes: memory match and utilisation. Their model explained early gamma

band responses in terms of a match between bottom-up and top-down information.

Furthermore, these authors relate late gamma band response to the utilisation of

information resulting from this match (Herrmann. et al., 2004; Ba�ar, 2011; Ba�ar, 2012b;

Ba�ar, 2013).

2.2.1. Gamma oscillatory responses on presentation of face,

facial expression and IAPS pictures

Recent findings showed that gamma responses play an important role in face

recognition. In the monkey brain, Ghazanfar et al. (2008) showed that during presentation

of face and voice stimuli, gamma band activity increased in strength compared with

unimodal (only visual or auditory) conditions. Keil at al. (1999) analysed the gamma

response on upright/inverted gestalt stimulation and upright/inverted Rubin’s vase. They

showed that EEG power in the gamma (29–45 Hz) range was greater during presentation

of the sad/happy faces compared with Rubin’s vase. Furthermore, upright sad/happy faces

and upright Rubin’s vase elicited higher gamma response than inverted images (Keil et al.,

1999). Rodriguez et al. (1999) also showed that upright Mooney faces elicited a greater

induced gamma response than inverted Mooney faces (Rodriguez et al., 1999). Anaki et al.

(2007) reported higher induced gamma activity for upright faces than for inverted faces,

30

and for familiar faces compared with unfamiliar faces. The same group subsequently

reported a greater gamma activity for faces compared with non-face stimuli (Zion-

Golumbic et al., 2008). Higher induced gamma was also reported in response to famous

faces compared with non-famous faces (Zion-Golumbic et al., 2010). By contrast, in an

MEG study, Dobel et al. (2011) reported that unknown faces elicit higher gamma

responses than familiar faces. Engel and McCarthy (2010) showed that face-specific

gamma activity was strongly modulated by selective attention.

Dobel et al. (2011) showed that a person suffering from prosopagnosia displayed

less induced gamma activity compared with healthy subjects. Lee et al. (2010) investigated

event-related MEG signals in healthy subjects, patients with BP disorder and patients with

a major depressive disorder on application of facial expression stimuli. These authors

reported that compared with healthy controls, patients with BP or major depressive

disorder showed decreased gamma response in frontal and parietal regions. Furthermore,

patients with BP disorder showed increased alpha–beta activities in the bilateral temporal

and occipital regions compared with healthy controls. Matsumoto et al. (2006) showed that

gamma band activity was lower in alexithymic subjects compared with non-alexithymic

subjects. Accordingly, the above-mentioned studies showed that gamma band activity is an

important component in the recognition of faces and facial expressions.

Researchers also analysed gamma oscillations on presentation of different face

expression paradigms. In both EEG and MEG experiments, negative facial expressions

(angry, fearful) elicited higher gamma responses than neutral and/or happy expressions

(Balconi and Lucchiari, 2008; Luo et al., 2007, 2009; Sato et al., 2011). Luo et al. (2007)

analysed gamma MEG activity on presentation of angry, fearful and neutral facial

expressions. They used a source analysis technique called synthetic aperture magnetometry

(SAM) and attempted to identify sources of gamma activity during face expression

31

perception. The findings showed an early event-related synchronisation in response to

fearful faces in the hypothalamus/thalamus region (10–20 ms) and the amygdala (20–30

ms). This response was earlier than ERS response observed in the visual cortex (40–50 ms)

in response to fearful faces. Moreover, ERS in the amygdala in response to angry

expressions had a late onset (150–160 ms).

Sato et al. (2011) subsequently reported similar results by recording intracranial

field potentials of the amygdala. These authors analysed gamma oscillations on

presentation of fearful, happy and neutral expressions; gamma band activity was higher in

response to fearful faces than neutral faces within an early time window (50–150 ms).

Accordingly, the authors concluded that the amygdala is involved in rapid processing of

fearful expressions. The role of the amygdala in identification of negative emotional

stimulation was also reported by Oya et al. (2002) on presentation of negative IAPS

pictures.

Similar to negative expressions, negative IAPS pictures elicited higher gamma

response. To our knowledge, the first study analysing gamma oscillations on presentation

of IAPS pictures was conducted by Müller et al. (1999). These authors showed an

increased 30–50-Hz response on application of negative valence over the left hemisphere

region compared with the right hemisphere region, and a laterality shift towards right

hemisphere for a positive valence. Consistent with this, Keil et al. (2001) reported that the

early (80 ms) gamma band activity in the 30-Hz range was significantly higher on

presentation of unpleasant pictures than for neutral and pleasant pictures. Furthermore, late

gamma response (480–550 ms) was significantly higher for affectively arousing than

neutral pictures.

Keil et al. (2007) reported increased stimulus-locked oscillatory response within

the 18–35 Hz range in the visual cortex on presentation of unpleasant pictures.

32

Furthermore, these authors showed that the second block was greater in comparison to the

first block on presentation of unpleasant pictures in 18–35 Hz frequency range. No such

effect was observed for neutral pictures. Accordingly, the authors concluded that “the

initial sensory response in the human visual cortex is sensitive to simple features

associated with emotionality, and that the amplification of this response increases as a

function of learning.”

Studies on gamma oscillations on presentation of IAPS pictures mostly reported

two different time windows (Keil et al., 2001, 2007; Martini et al., 2012): early evoked

gamma activity, in the lower gamma frequency ranges between 60 and 90 ms at 18–35 Hz

and the second gamma frequency between approximately 350 and 420 ms at 55–80 Hz

(Keil et al., 2001, 2007). Later studies reported (Martini et al., 2012) the first gamma time

window as 0–250 ms at 38–45 Hz and the second gamma time window as 500–750 ms at

30–37 Hz, with two peaks of weak activation at 65–80 Hz for the same time windows.

Karaka� and Ba�ar (1998) reported that the early evoked gamma response is sensory and

the late response is cognitive. In a recent study, Ba�ar (2012b) reported a possibility of 3–4

phase-/time-locked gamma responses in 28–45-Hz frequency window on presentation of

cognitive stimuli. Furthermore, Ba�ar (2012b) concluded that “late responses (starting

around 300 ms) are probably conveyed over regions such as reticular formation

hippocampus, whereas, the early response in the primary occipital cortex O1 (starting at

100 ms) is probably the direct response over the short pathway via lateral geniculate

nucleus.” From the above-mentioned studies, it can be concluded that the early gamma

response could be sensory in origin and could be sensitive to simple features associated

with emotionality (Keil et al., 2007). By contrast, the late gamma response could be

associated with the conscious perception of the emotional stimuli.

33

Electrophsyiological studies of human classical conditioning were also conducted

by several researchers (Miskovic and Keil, 2012). In these studies, increases in gamma

power and gamma coherence were reported on fear conditioning (Miltner et al. 1999, Klein

et al., 2006). Keil et al. (2007) reported early (60–90 ms) gamma responses evoked by

differential conditioning in the visual cortex at second consecutive days of conditioning.

Miskovic and Keil (2013) showed that conditioning was associated with increased visual

cortex activation in response to threat stimulus but not to safety stimulus. There are few

studies analysing fear conditioning by means of evoked/event-related studies. In these

studies, gamma oscillatory responses were analysed. Further research is needed that

includes all frequency bands in different time windows.

Table 2 summarises analyses of gamma oscillations on presentation of faces,

facial expressions and IAPS pictures. The last part of the table presents findings on gamma

oscillations in other emotional paradigms. Figure 4 summarises the above-mentioned

studies.

3. Highlights and Conclusions

1) Studies analysing oscillatory responses in identification or recognition of

faces showed that oscillatory responses increase selectively depending on face stimulation.

Face stimuli versus non-face stimuli, upright faces versus inverted faces, known/loved

versus unknown faces, emotional versus non-emotional and negative versus positive

emotional faces elicited higher oscillatory responses in different frequency ranges and in

different time windows. A correlation between the oscillatory response and the number of

components (such as loved, emotional, negative emotional) in the picture of the face was

also noticed; this may be caused by increased neuronal activity leading to increased

oscillatory responses.

34

2) The reviewed literature showed that the brain is more sensitive to emotional

stimuli than neutral stimuli. This was observed both in the facial expression paradigms and

in the IAPS pictures. High-arousing (both positive and negative) expressions elicited

stronger delta and theta oscillatory responses than low-arousing (neutral) expressions.

Similar results were observed with IAPS pictures. High-arousing (both positive and

negative) IAPS pictures elicited stronger delta and theta oscillatory responses than low-

arousing (neutral) IAPS pictures. The primary difference between positive versus negative

facial expressions (valence effect) or IAPS pictures was noticed in the beta and gamma

frequency ranges. Angry expressions elicited stronger beta and gamma oscillatory

responses than happy and neutral expressions (Güntekin and Ba�ar, 2007; Keil et al.,

1999). Negative IAPS pictures elicited higher beta (Güntekin and Ba�ar, 2010b; Miskovic

and Schmidt, 2010; Woodruff et al., 2011) and gamma responses (Garci-Garcia et al.,

2010; Keil et al., 2007, 2001; Martini et al., 2012; Müller et al., 1999; Oya et al., 2002)

than neutral or positive stimuli.

3) A common and interesting finding from the above-discussed studies was the

increased responsiveness of brain towards negative emotional pictures (facial expression or

IAPS). This could be explained from a phylogenetic viewpoint; there could be a

phylogenetic advantage associated with faster processing of and heightened physiological

response to negative emotional stimuli.

4) The reviewed studies pointed out that the emotional modulation on beta and

gamma oscillatory responses reflects fast and automatic processing of negative stimuli

(Garci-Garcia et al., 2010; Güntekin and Ba�ar, 2010b; Keil et al., 2007, 2001; Martini et

al., 2012; Miskovic and Schmidt, 2010; Müller et al., 1999; Oya et al., 2002; Woodruff et

al., 2011).

35

5) The ERP research on affective picture processing showed that valence is

likely to influence relatively early (100–250 ms) components, while arousal influences late

(200–1000 ms) components (Olofsson et al., 2008). In event-related oscillation studies,

valence is represented (unpleasant vs. pleasant) mostly by higher frequencies such as beta

and gamma oscillations in early time windows, whereas arousal is represented by lower

frequencies such as delta and theta; arousal is also represented by gamma oscillatory

responses in the late time window (Keil et al., 2001).

6) The time window of the oscillatory responses during emotional picture

processing (face expression or IAPS) showed that the difference between different picture

groups began very early (approximately 80 ms) and extends up to 850 ms. The late time

windows were mostly represented by lower frequency windows or with second discharges

of higher frequency windows. On the other hand, the early time windows are represented

by higher frequency windows.

7) The topology of oscillatory responses on presentation of faces, facial

expressions and IAPS pictures showed that it was impossible to identify any single

location for any mentioned stimulations. The reviewed literature indicates that the exact

localisation of ‘single’ sources underlying the measured electrical activity related to

emotional stimuli processing is not possible. Instead, the notion of a ‘whole brain work’ for

all types of functional processing in the brain (Ba�ar, 2006) was emphasised.

8) This review demonstrated that in the research of oscillatory dynamics on

presentation of faces, facial expressions and IAPS pictures, the gamma oscillatory

responses were analysed more than other frequency bands. Moreover, there is a need for

in-detail analysis of all oscillatory responses (delta, theta, alpha and beta). The brain

oscillatory responses distributed over the scalp should be considered as a template and

ensemble of features, including various degrees of amplitudes, phases and prolongations.

36

We emphasise the analysis of several parameters of electrical activity to reach a more

reliable description of distinct brain functions (Ba�ar, 2006).

9) Compared with other frequencies, the alpha band gave inconsistent results

between studies by various groups. This may be due to various mathematical methods

applied in the analysis of the alpha frequency band. Various studies analysed filtered

oscillatory responses: some applied evoked power analysis, some applied ERD/ERS

method and others analysed alpha asymmetry. Most ERD analyses on presentation of

emotional stimuli used the method presented by Kalcher and Pfurtscheller (1995). Further

research is needed on emotional processes, using methods in which the alpha oscillatory

responses are analysed without removing the phase-locked activity. In future ERD, phase-

locking, evoked and/or induced power, time–frequency compositions, evoked/event-related

coherence of alpha oscillatory responses on presentation of face, face expression and IAPS

pictures should be analysed to observe the whole dynamics of alpha activity.

10) Previous hypotheses on analysis of spontaneous EEG suggested that

relatively greater right-alpha activity was associated with greater negative affect, and

relatively greater left-frontal-alpha activity was associated with greater positive affect. In

general, these studies analysed spontaneous EEG, and the subject’s affective style was

determined via questionnaires and frontal alpha asymmetry; the effect was further

calculated by statistical analysis methods. However, this theory was not corrected with the

evoked/event-related oscillation studies. Pönkanen and Hietanen (2012) showed that

presentation of pictures with different gaze directions and facial expressions (happy or

neutral) did not affect frontal alpha asymmetry. In a study by Harmon-Jones (2007),

participants were exposed to pictures that evoked anger, fear, disgust, positive and neutral

affective reactions. The affective pictures did not evoke reliable shifts in asymmetrical

cortical activation.

37

11) Interindividual differences are crucial in studies of facial and emotional

responses. Subjects who are more emotionally sensitive or who have experienced higher

emotional involvement have stronger oscillatory responses than less emotionally sensitive

subjects (Knyazev et al., 2009). Alpha desynchronisation was correlated with higher BIS

measures (Balconi and Mazza, 2009). Individual tendency towards anxiety also influences

oscillatory responses. Beta oscillatory response was found to be high during periods of

anxiety (Andersen et al., 2009) or in response to anxious rumination (Pavlenko et al.,

2009). Individuals scoring highly for the anger trait showed an attentional bias for angry

faces (Putnam et al., 2004). Anger is associated with increased theta band synchronisation

and decreased alpha band desynchronisation (Knyazev et al., 2009), whereas anxiety

shows the opposite associations (Knyazev et al., 2008a, 2008b). In a recent study, the same

group also showed that the anger scores of subjects with low anxiety were positively

related to the extent of face presentation-related theta synchronisation (Bocharov and

Knyazev, 2011). As mentioned above, studies indicated the importance of interindividual

differences, which should be considered during experimental research.

12) Age and gender differences should also be considered during experiments

on face, facial expression and affective picture processing. Female subjects had stronger

oscillatory responses than male subjects on presentation of facial expressions (Güntekin

and Ba�ar, 2007b) or IAPS pictures (Klados et al., 2009). The analysis of differences

between genders in all frequency bands on application of face, face expression and IAPS

pictures is a subject of future studies.

13) This review further demonstrated the need for standardisation of applied

stimulation designs and applied brain dynamic methods. The present lack of

standardisation restrains comparison of the results obtained from different studies. (1)

Face/facial expression identification or affective picture processes are complicated

38

processes. (2) The designs applied in studies differ (block, random, passive, oddball,

Sternberg). (3) The brain dynamic methods used in the studies differ (filtered oscillatory

responses, evoked power, induced power, phase locking, evoked coherence, ERD/ERS

etc.). The combination of these three factors precludes us from drawing strong conclusions

from the existing studies. However, it is possible to proceed step-by-step towards

standardisation of research into these complex paradigms. In future, there is a strong need

to address these issues.

Acknowledgements

The authors are grateful to Aysel Düzgün for secretarial help.

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Tables Legends Table 1: Summary of methodology used in the analysis of EEG–brain dynamics

a) Power spectral density of the spontaneous EEG

b) Evoked spectra (FFT analysis of the sensory-evoked potential (elicited by simple

light, tone signal, etc.)

c) Event-related Spectra (FFT analysis of an ERP, for example target or non-

target signal during an oddball paradigm)

d) Digital filtering of EPs and ERPs

e) Phase locking, phase synchrony

f) EEG Coherence

g) Evoked Coherence

h)Event-related Coherence

i) Cross-correlation

j) Cross-spectrum

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Table 2: Summary of analyses of gamma oscillations on presentation of faces, facial expressions and IAPS pictures.

FACE RECOGNITION PARADIGMS

Rodriguez et al.

(1999)

Upright/inverted faces Gamma power Perception of upright faces is associated

with an increase in induced gamma power

Keil et al. (1999) Upright/inverted

happy–sad gestalt

stimuli

Evoked gamma

power

Increase in gamma response in presence

of happy–sad faces compared with Rubin

vase; Increase in gamma response with

upright happy–sad faces compared with

the inverted ones

Lauchaux et al.

(2005)

Upright faces

Inverted faces

Mooney faces

Evoked and

induced gamma

power and EEG

recordings of

epileptic patients

Distributed gamma responses by Mooney

faces, characterised by (a) an early burst

of signal (before 200 ms) in the fusiform

gyrus and the adjacent lateral occipital

cortex; (b) followed by posterior–anterior

propagation along the intra-parietal

sulcus; (c) along with deactivation in the

primary visual cortex.

Anaki et al.

(2007)

Inverted faces

Upright faces

Familiar faces

Unfamiliar faces

Induced gamma

power

Increase in gamma amplitudes for upright

(25–50 Hz) and familiar faces compared

with inverted (50–70 Hz) and unfamiliar

faces, respectively

Zion-Golumbic

and Bentin

(2007)

Face stimuli

Non-face stimuli

Gamma power Increase in induced gamma for face

stimuli in comparison with non-face

stimuli

Zion-Golumbic

et al. (2008)

Face stimuli

Non-face stimuli

Induced gamma

power

Higher gamma activity for faces

compared with non-face stimuli

Zion Golumbic

et al. (2010)

Famous faces

Nonfamous faces

Induced gamma

Power

Higher induced gamma activity with

famous faces compared with nonfamous

faces

Engel and

McCarthy (2010)

Face Field potentials

from cortical

surface;

Evoked power

Face-specific gamma activity was

strongly modulated with selective

attention

58

Grützner et al.

(2010)

Upright/inverted

Mooney faces

MEG study;

Gamma power

Time–frequency analyses revealed

sustained high-frequency gamma-band

activity associated with the processing of

Mooney face stimuli

Hansen et al.

(2010)

Fractal noise stimuli

were presented and the

subjects were asked

to rate each stimulus

across a scale: ‘face

not present’ to ‘face

present’

Delta, theta, alpha,

beta, gamma

power

Authors suggested that

theta through gamma bands are involved

in the selective integration of low-level

physical attributes into a coherent face

percept

Park et al. (2010) Faces and morphed

faces

Theta, beta,

gamma power

Enhanced gamma activity was observed

in the right temporal–parietal region

during successful change detections of

faces.

Dobel et al.

(2011)

Inverted faces

Upright faces

Famous faces

Unknown faces

MEG study;

Induced gamma

power;

Patients with

prosopagnosia

Gamma amplitudes were higher for

upright than inverted faces; Unknown

faces elicited higher gamma response than

familiar faces.

Patients with prosopagnosia displayed

less induced gamma activity than healthy

subjects

Minami et al.

(2011)

Natural and bluish-

colour faces

Induced gamma

power

Face colour affected gamma response

FACE EXPRESSION PARADIGMS

Luo et al. (2007) Fearful/angry/neutral

facial expressions from

the Nim Stim Face

Stimulus

MEG study;

Gamma power

ERS in response to fearful faces in

the hypothalamus/thalamus area

(10–20 ms) and then the amygdala

(20–30 ms). ERS response in the

amygdala to angry expressions had

late onset (150–160 ms).

Balconi and Lucchiari Healthy subjects; ERD/ERS GBA was enhanced by more for

59

(2008) Happy, sad, angry,

fearful or neutral faces,

supraliminal vs.

subliminal

supraliminal than subliminal

elaboration; and more by high-

arousal (anger and fear) than low-

arousal (happiness and sadness)

emotions

Luo et al. (2009) Fearful and neutral faces

(Karolinska directed

emotional faces;

Lundqvist et al. (1998)

MEG study;

Gamma power

Emotional relative to neutral stimuli

were associated with significantly

greater gamma band

synchronisation

Chen et al. (2010) Audiovisual,

visual and auditory

emotional stimuli:

Happy, angry, neutral

expressions

MEG;

Gamma power

Occipital gamma activity was

observed only when multisensory

stimuli were presented

Jung et al. (2011) Angry, fearful, happy,

disgust, and neutral

expressions

Gamma power;

Intracranial

field potentials

from the

amygdala

Processing negative facial

expressions as well as receiving

negative feedback elicited gamma

band responses in the lateral

orbitofrontal cortex

Sato et al. (2011) Fearful, happy, and

neutral expressions

Intracranial

field potentials

from the

amygdala;

Gamma power

The amygdala showed greater

gamma-band activity in response to

fearful compared with that of

neutral facial expressions at 50–150

ms

IAPS PARADIGMS

Müller et al. (1999) IAPS pictures;

Unpleasant, neutral,

pleasant

Gamma power Gamma band showed more power

for negative valence over the left

temporal region as compared to the

right; and a laterality shift towards

the right hemisphere for positive

valence. Emotional processing

60

enhanced gamma band power at

right-frontal electrodes regardless of

the particular valence compared to

processing neutral pictures.

Keil et al. (2001) IAPS pictures;

Unpleasant, neutral,

pleasant

Gamma power Early (80 ms) gamma band activity

in the range of 30 Hz was higher for

unpleasant stimuli than for neutral

and pleasant stimuli. Late increase

in the higher gamma range was

higher for arousing than for neutral

pictures.

Oya et al. (2002) IAPS;

Aversive/pleasant/neutral

Intracranial

field potentials

from the

amygdala;

Gamma power;

Phase

synchrony

Emotional relative to neutral stimuli

were associated with significantly

greater gamma band power without

any phase-locking

Keil et al. (2007) IAPS pictures:

Unpleasant/neutral

Beta/gamma

power

Early (60–90 ms) stimulus-locked

oscillatory response in the

frequency range 18–35 Hz was

reported for unpleasant stimuli.

Matsumato et al. (2006) IAPS pictures:

Unpleasant/neutral

Induced gamma

power;

Phase

synchrony

Non-alexithymic subjects showed a

significant increase in induced

gamma band power 400–450

ms after presentation of negative

pictures compared with neutral

pictures

Garci-Garcia et al.

(2010)

Auditory–visual

distraction paradigm;

IAPS pictures;

Negative/neutral

Induced gamma

power;

Phase

synchrony

Larger PLF increase after novel

sounds occurring in the negative

emotional context compared to that

in the neutral one

Martini et al. (2012) IAPS pictures; Induced gamma Unpleasant pictures elicited

61

Unpleasant, neutral power;

Phase

synchrony

increased gamma power compared

with neutral visual stimuli

OTHER EMOTIONAL PARADIGMS

Gemignani et al. (2000) The experimental

paradigm included

periods of rest, during

which hypnotised

subjects were asked to

produce an emotionally

neutral mental image,

and periods of emotional

activation, in which they

were asked to image a

phobic object

Spontaneous

EEG gamma

power

Significant increase in gamma

power related to negative emotions

in the left fronto-central regions

Gemignani et al. (2006) Verbal presentation of a

neutral animal/verbal

presentation of each

subject’s animal phobic

object

Gamma power During phobic stimulation both

high- and low-hypnotisable groups

exhibited a similar significant

increase in EEG gamma relative

power

Oathes et al. (2008) Participants were

encouraged to consider a

topic of ‘current

concern’ that would

‘worry them intensely’

for several minutes

Gamma power During worry induction, patients

with generalised anxiety disorder

showed higher

levels of gamma activity than

control participants at posterior

electrode sites

Onton and Makeig

(2009)

Subjects were asked to

experience the suggested

emotion for 3–5 min

ICA

decomposition;

Gamma power

Negative correlation between

gamma activity and emotional

valence in the medial occipital lobe

region

Jausovec and Jausovec

(2005)

Pictures taken from

MEIS Mayer et al.

(2000)

ERD/ERS Individuals with high emotional IQ

showed increased induced gamma

band ERS

62

Figures legend Fig. 1. Representative illustration of delta response oscillations on presentation of

emotional stimuli. Based on the results from Ba�ar et al., 2007, 2008; Balconi et al., 2009a,

2009b; Balconi and Luchiari, 2006; Güntekin and Ba�ar, 2009; Knyazev et al., 2009;

Klados et al., 2009; Sakihara et al., 2012.

Fig. 2. Representative illustration of theta response oscillations on presentation of

emotional stimuli.

Fig. 3. Representative illustration of beta response oscillations on presentation of

emotional stimuli. Based on the results from Güntekin and Ba�ar, 2010b, 2007a, 2007b;

Miskovic and Schmidt, 2010; Özgören et al., 2005; Sakihara et al., 2012; Woodruff et al.,

2011.

Fig. 4. Representative illustration of gamma response oscillations on presentation of

emotional stimuli. Based on the findings of articles presented in Table 1.

Figure

Figure

Figure

Figure