Psychology
2013. Vol.4, No.3A, 261-267
Published Online March 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.43A039
Approach the Good, Withdraw from the Bad—A Review on
Frontal Alpha Asymmetry Measures in Applied
Psychological Research
Benny B. Briesemeister, Sascha Tamm, Angela Heine, Arthur M. Jacobs
Department of Psychology, Center for Applied Neuroscience, Free University, Berlin, Germany
Received December 20th, 2012; revised January 9th, 2013; accepted February 2nd, 2013
Basic research has established a strong relationship between stimulus induced human motivation for ap-
proach-related behavior and left-frontal electrophysiological activity in the alpha band, i.e. frontal alpha
asymmetry (FAA). Since approach motivation is also of interest for various fields of applied research,
several recent studies investigated the usefulness of FAA as a diagnostic tool of stimulus induced motiva-
tional changes. The present review introduces the theory and the methods commonly used in approach/
withdrawal motivation research, and summarizes work on applied FAA with a focus on product design,
marketing, brain-computer communication and mental health studies, where approach motivation is of
interest. Studies investigating and developing the application of FAA training in the treatment of affective
disorders such as major depressive disorder and anxiety disorder are also introduced, highlighting some of
the future possibilities.
Keywords: Component; Frontal Alpha Asymmetry; Applied Science; Marketing; Depression; EEG
Introduction
Some 35 years ago, in 1978, a group of researchers around
Richard J. Davidson presented a novel finding at the 18th an-
nual meeting of The Society for Psychophysiological Research:
Participants who watched portions of a television show were
asked to indicate online how much they liked the program,
while electroencephalographic (EEG) data was recorded from
bilateral frontal and parietal regions. Positively rated TV scenes
were associated with greater relative left-hemispheric alpha
activation in frontal electrodes, while negatively rated scenes
were associated with greater relative right-hemispheric frontal
activation. Parietal electrodes, in contrast, did not discriminate
between the conditions (Davidson, Schwartz, Saron, Bennett, &
Goleman, 1979). Thirty-five years later, hemispheric asymme-
tries in the alpha frequency range (8 - 12 Hz) over prefrontal
electrodes have become widely accepted as a correlate of ap-
proach and withdrawal related motivation in basic research
(Harmon-Jones, Gable, & Peterson, 2010; Price, Peterson, &
Harmon-Jones, 2012; Rutherford & Lindell, 2011), with more
and more studies extending the frontal alpha asymmetry (FAA)
methodology to diverse fields in applied psychology. The pur-
pose of this non-exhaustive review is to introduce and summa-
rize some of this latter work, beginning with a short introduce-
tion to FAA measurement.
Frontal Alpha Asymmetry, Physiology and the
Approach-Withdrawal Hypothesis
When speaking of FAA, at least two separate perspectives
can be identified. On the one hand, FAA refers to a specific
way to analyze EEG data, which is described in detail in Allen,
Coan, and Nazarian (2004). This perspective will be referred to
as the FAA analysis, which involves four processing steps. First,
the raw data collected from frontal and prefrontal electrode
sites (usually including F3/F4, F7/F8, FP1/FP2) is decomposed
into the underlying frequency bands using Fourier transform.
Second, assuming that decreased alpha indicates an increase in
allocation of cortical resources (Gevins, Smith, McEvoy, & Yu,
1997) or cortical activation (Allen et al., 2004; Davidson, 1988),
frequency bands other than alpha are typically discarded. In a
third step, the data is natural-log transformed to reduce positive
skewness and kurtosis. Finally, the FAA metric is calculated
from these values as difference between right-hemispheric
electrodes data minus their left-hemispheric electrodes coun-
terparts (ln(R)-ln(L), Allen et al., 2004; Davidson et al., 1979).
Thus, positive FFA values indicate larger relative right-hemi-
spheric power, which corresponds to larger cortical resource
allocation in the left hemisphere.
Apart from the FAA analysis, there is also the issue of the
functional interpretation of FAA on the other hand, which will
be referred to as FAA theory. Initial research using the FAA
analysis focused on affective manipulations within the stimulus
material, reporting positive FAA metrics (interpreted as larger
relative left-hemispheric activation) for positive stimuli and
negative FAA metrics for negative stimuli (Davidson et al.,
1979). This was interpreted as indicators of a systematic rela-
tionship between FAA and experienced positive versus nega-
tive affect (Harmon-Jones et al., 2010). However, recent re-
search including manipulations that allow for a differentiation
of valence and approach-withdrawal motivation (e.g. anger)
suggests that only the latter is lateralized in frontal and pre-
frontal brain regions (Berkman & Lieberman, 2010; Carver &
Harmon-Jones, 2009). Additional studies on patients with uni-
lateral brain lesions and experiments using transcranial mag-

B. B. BRIESEMEISTER ET AL.
netic stimulation (TMS) further support the approach/with-
drawal motivation hypothesis (Rutherford & Lindell, 2011).
Evidence that FAA analysis is linked to larger relative left-
hemispheric activation during approach-related motivation but
larger right-hemispheric activation during withdrawal-related
motivation is convincing. However, the use of FAA analysis as
a diagnostic tool, which would be desirable for applied contexts
such as marketing research, product design, or brain-computer-
interfacing (BCI) is at least problematic. As pointed out by
Poldrack (2006), this type of reverse inference, that is to infer
motivational states from a given FAA lateralization, would
require FAA to be specific for motivational processes only, an
issue which, however, is disputable. Several published studies
show that the FAA metric is also influenced by unilateral hand
contractions (Harmon-Jones et al., 2010), the seating position
(Harmon-Jones, Gable & Price, 2011) or task difficulty in dif-
ferent working memory tasks, even in the absence of emotional
distractors (Baldwin & Penaranda, 2012; Wacker, Chavanon,
Leue, & Stemmler, 2010). None of these variables are explicitly
emotionally arousing, even though moderating effects such as
greater effort caused by increased task difficulty cannot be
excluded (Harmon-Jones et al., 2010). To estimate the specific-
ity of FAA analysis for approach-withdrawal motivation, we
follow Ariely and Berns (2010; see also Poldrack, 2006) by
determining the posterior probability of the reverse inference
using Bayes’ theorem. This approach applied to the FAA re-
verse inference problem is summarized in Table 1. According
to the results based on studies published between 2002 and
2012, the probability of approach/withdrawal motivation given
that an FAA effect is observed is 0.79 and the corresponding
Bayes factor of 3.75 indicates moderate evidence.
A Few Critical Words Concerning This Review
Before the published studies that use FAA for diagnostic
purposes are summarized in the second part of this review,
some methodological and terminological issues need to be ad-
dressed. The FAA metric as reviewed here is always used as a
relative metric, indicating differences between two groups (e.g.
depressed versus non-depressed), two stimuli (e.g. different TV
advertisements) or two points in time (e.g. before versus after
treatment). Since the FAA metric is a quotient, significant
changes (e.g. increased FAA score) can originate from a rela-
tive increase in right-hemispheric alpha power, a decrease in
left-hemispheric alpha power, or both. Unfortunately, most
studies do not provide enough details to distinguish between
these three possibilities, which is why effects are reported
solely in relation to the FAA theory framework for this review.
Moreover, the terms activity and activation are used inter-
changeably.
Frontal Alpha Asymmetry as a Diagnostic Tool
Knowing whether a given stimulus elicits approach-related
motivation is valuable information for everyone who is design-
ing products or marketing measures to promote products, espe-
cially when this information can be collected ahead of a market
launch. Therefore, one application for FAA is the improvement
of product designs and marketing (section “Frontal alpha
asymmetry in design and marketing”). A second major field
deals with the application of EEG and FAA analysis in the
context of human-computer interactions (section “Frontal alpha
asymmetry in Brain-Computer Interfaces”). BCIs are a promis-
ing way to help individuals with communication and motor
control problems caused by disability (Adams, Bahr & Moreno,
2008), gaining additional popularity when used as controllers
for computer games (Singer, 2008). However, in contrast to
design and marketing applications, BCIs are methodologically
challenging, since they require FAA feedback in real-time.
Finally, a growing literature relating FAA at rest to affective
disorders suggests that FAA analysis might reliably distinguish
between healthy and clinical populations (section “Frontal al-
pha asymmetry in the diagnosis of affective disorders”). The
following sections are intended to give a comprehensive over-
view over published work in each of these areas of FAA appli-
cation.
Frontal Alpha Asymmetry in Design and
Marketing
Studies using FAA to compare different product designs of-
ten interpret greater left-frontal activity as an index of pleas-
Table 1.
Inferring motivational states from frontal alpha asymmetry (FAA).
A Google Scholar search with the keywords <“frontal asymmetry” EEG> was used to find literature using FAA analysis, published within the last decade
(2002-2012). Only experiments investigating changes in FAA (i.e. FAA as a state metric) in adult healthy samples were considered. Studies using FAA as a
diagnostic or therapeutic tool, but without an explicit hypothesis, were excluded from the calculation (but taken into account for the review).
Several studies (e.g. Wyczesany, Kaiser, & Barry, 2009) used multiple approach/withdrawal manipulations, some of them yielding results in line with the
hypothesis, some of them being inconclusive or contradictory. For those studies, each single contrast was included as separate datum.
Approach and withdrawal motivation were treated as two ends of one continuum and thus were not analyzed separately.
Probability of FAA effect given an approach/withdrawal task 102/139 = .734
Probability of FAA effect given no approach/withdrawal task 9/46 = .196
Assumed prior probability of approach/withdrawal motivation .5
Probability of approach/withdrawal motivation given an FAA effect .734/(.734 + .196) = .79
Approach/withdrawal task No approach/withdrawal task
FAA effect 102 9
No FAA effect 37 37
Based on these calculations, the probability of approach/withdrawal motivation given an FAA effect is .79, with a Bayes factor of 3.75 indicating moderate
eidence. v
262

B. B. BRIESEMEISTER ET AL.
antness or liking. Park and Watanuki (2005), for example, were
interested in whether the type of sanitary napkins (mesh versus
nonwoven) influences menstrual unpleasantness, as indicated
by self-report and greater right hemispheric activity. Results
were not consistent, however. Pleasantness ratings indicated
that mesh sanitary napkins were less pleasant to wear during
follicular phase than nonwoven sanitary napkins, with no dif-
ferences in menstrual phase or during simulated menstrual
bleeding. In contrast, left-hemispheric alpha power was in-
creased after (as compared to before) simulated menstrual
bleeding only when wearing nonwoven sanitary napkins, leav-
ing the authors to conclude that the FAA theory might be un-
suitable for their purposes. A second study tested the effect of
six different ballpens on frontal alpha power, focusing on sin-
gle-subject analysis (Tomico et al., 2008). Participants were
asked to first just look at the pen, to manipulate it in a second
step, to use it, and, finally, to manipulate it a second time. This
way, a subject-specific alpha power ranking of the six pens in
each of the four conditions was obtained. These rankings were
combined with self-report data to conclude that the first look at
a pen and its manipulation but not its usage were related to the
overall product perception.
Another line of design related FAA research focuses on the
affective properties of different olfactory aromas. Kline, Black-
hart, Woodward, Williams and Schwartz (2000) presented eld-
erly women with seven odors, choosing the most pleasantly
(vanilla) and unpleasantly (valerien) rated odor for FAA analy-
sis. As expected, vanilla aroma induced significantly greater
relative left-hemispheric activity when compared with valerien
or plain water (neutral condition). Hemispheric asymmetries for
valerien were not found, however (Kline et al., 2000). Two
additional studies focused on the effect of lavender on mood
and FAA (Field et al., 2005; Sanders et al., 2002). Sanders et al.
(2002) contrasted lavender with rosemary odors, finding the
expected increase in relative left-frontal activity for lavender
versus baseline, but not for rosemary. This result was replicated
by Field et al. (2005), using a lavender shower gel. All of these
studies used FAA to investigate and possibly modify the attrac-
tiveness of products. Other research, in contrast, focuses on
increased marketing efficiency. Neuromarketing (Ariely &
Berns, 2010), that is the application of neuroscientific methods
to marketing purposes, has become popular within the last dec-
ade, with FAA analysis being one of the most frequently used
methods. Vecchiato et al. (2011), for example, presented par-
ticipants with a 30 min documentary, interrupted by three com-
mercial breaks. Participants were asked which of the TV com-
mercials they remembered and for those remembered, how
pleasant they were. Alpha power was then correlated with the
pleasantness ratings. As a direct replication of Davidson et al.
(1979), pleasant commercials were accompanied by greater
right-hemispheric power, while greater left-hemispheric power
was observed for unpleasant commercials. Moreover, the FAA
score at medial frontal electrodes was significantly correlated
with perceived pleasantness. These results were replicated in a
very similar setting by Vecchiato et al. (2010), adding skin
conductance responses (SCRs) and heart rate variability (HR)
as dependent variables.
The Vecchiato studies used a quite complex yet realistic ex-
perimental design, embedding the TV commercials into normal
program context. Other studies relied on the presentation of TV
commercials without additional context. Comparing three dif-
ferent versions of a TV commercial for SONY Bravia (“Balls”
aired in 2005, “Paints” aired in 2006 and “Play-Doh” aired in
2007), Ohme, Reykowska, Wiener and Choromanska (2010)
used the FAA metric as an indicator of approach tendencies.
Each commercial was made of four parts, i.e. the initial emo-
tional part meant to built emotional engagement, a product
information blended over the last seconds of the emotional part,
presentation of the product itself and finally the presentation of
the brand. Using FAA analysis, an overall advantage (i.e.
greater FAA score) was found for the balls commercial, driven
by the emotional part, the product information and the part
where the brand was revealed. The advantage of the brand part
was likely to be a carry-over effect, however, caused by the
animation of balls surrounding the Sony bravia television in the
product presentation scene, which was apparent only in the
balls commercial. Consequently, removing the ball animation
made the FAA effect disappear (Ohme et al., 2010). In a fol-
low-up study, Ohme, Matukin and Szezurko (2010) further
analyzed the balls commercial on a second-by-second basis, in
order to identify the scenes which are crucial for emotional
involvement. They focused especially on a scene depicting a
frog leaping from a drainpipe. Assuming that this scene may
have acted as an emotional prime for the rest of the spot based
on their initial study, the authors compared two versions of the
balls commercial, with and without the frog scene. As predicted,
the product information scene following the emotional scenes
including the frog caused significantly greater relative left-
hemispheric activation than baseline recordings, while no dif-
ferences from baseline were observed without the frog scene.
A very similar procedure was used in Ohme, Reykowska,
Wiener and Choromanska (2009), comparing two versions of a
TV commercial for skin care product advertisements. The two
spots were almost identical, differing only with respect to a
single scene. Behavioral pretests revealed that the difference
between the spots was not consciously perceived and did not
affect ad recall. When participants of three experimental groups
presented with either spot version A, spot version B or neither
spot version were given the chance to select the advertised
product as a complimentary gift in a shelf test, however, only
one spot affected the choice behavior in comparison to the con-
trol group. FAA analysis revealed that this spot contained the
scene which was associated with larger relative left-hemi-
spheric activation (Ohme et al., 2009).
In contrast to FAA research on advertisements, Burshteyn
and Buff (2008) were interested in the perceived differences
between private-label brands and manufacturer brands. They
assumed that small private-label brands are intentionally de-
signed to resemble manufacturer brands in their product pack-
aging, which should grant them similar attention from the cus-
tomers. As expected by Burshteyn and Buff, participants indi-
cated no difference between manufacturer-brand and private-
label brand packaging based on similarity ratings and FAA me-
trics. Manufacturer-brands were, however, rated as being more
familiar.
In summary, previous FAA studies focusing on commercials
successfully investigated the emotional engagement of TV
viewers. In addition to that, first studies indicate that FAA can
provide information concerning the affective value of every day
products (Park & Watanuki, 2005; Tomico et al., 2008) and
product packaging (Burshteyn & Buff, 2008), and suggest that
marketing and design can possibly benefit from applied FAA
research.

B. B. BRIESEMEISTER ET AL.
Frontal Alpha Asymmetry in Brain-Computer
Interfaces
The basic idea behind BCI is to allow for the interaction be-
tween computers and humans, who have problems communi-
cating with electronic devices due to disabilities or extreme
circumstances (Adams et al., 2008; Molina, Tsoneva, & Nijholt,
2009). Thus, most of the BCI literature focuses on motor activ-
ity and cognitive control, with affective processes being only of
secondary interest. At least two BCI applications are assumed
to benefit from affective information, however. First, there is
evidence that cognitive processes are influenced by emotional
processes and mood (Vartak, 2010). In this context, information
about a person’s affective state can help to make BCI aiming at
cognitive processes more efficient by explaining variance that
else remains unspecified. Second, BCIs focusing on emotions
have caused great interest in the entertainment industry (Gil-
leade & Allanson, 2005). Several computer games controlled
by the gamers’ affective states were developed in recent years,
triggered by the availability of affordable BCI based PC con-
trollers (Singer, 2008). Thus, from a theoretical perspective,
FAA analysis is a promising BCI methodology.
FAA based BCIs are confronted with two critical methodo-
logical problems, however. First, in order to communicate with
electronic devices, FAA analysis must be done in real-time—
which is a matter of computational power. Second, unlike most
research on FAA, BCI need to operate on the basis of single-
subjects, which increases error variance relative to standard
group analyses. This problem is addressed by Winkler, Jäger,
Mihajlović and Tsoneva (2010), who analyzed and classified
individual data from nine participants on a single trial basis.
Only the data of two subjects lead to a classification error be-
low 40%, i.e. a level that is better than chance. These results are
somewhat disappointing, but it is important to keep in mind that
to evoke emotional responses, Winkler et al. (2010) relied on
affective pictures, which have been shown to induce approach
or withdrawal related moods only under specific circumstances
(Gable & Harmon-Jones, 2008). In contrast, Huang et al. (2012),
using affective video clips, report considerably better classifi-
cation results. Vartak (2010), finally, demonstrated that the
combination of FAA metrics and physiological variables like
SCR and HR can identify not only approach/withdrawal moti-
vational tendencies, but discriminate between different discrete
emotions on population level. He concludes his paper by stating
that the next step should be to get closer to real life applications:
“Data should be collected in such environments built for the
purpose of training and education” (Vartak, 2010: p. 141). Ac-
cording to Gilleade and Allanson (2005), this already is the
case. Affective BCIs receive increased attention from the en-
tertainment industry, and although FAA still plays a minor role
with cheaper methods like SCR and HR being preferred, first
EEG based videogame controllers are in development (Singer,
2008), which in turn allow the use of FAA metrics.
Taken together, although affective information is of second-
dary interest in the development of BCIs, a number of studies
indicate that it is possible to reliably classify emotional states
based on FAA (Huang et al., 2012; Vartak, 2010), even when
being used in single subjects (Huang et al., 2012). With the
entertainment industry being interested in affective gaming
(Gilleade & Allanson, 2005), future research in this direction
might be profitable. Whether FAA can compete with other
currently tested emotion classification approached remains to
be investigated (Ko, Yang, & Sim, 2009; Mühl et al., 2011).
Frontal Alpha Asymmetry in the Diagnosis of
Affective Disorders
While the studies reviewed so far focused on FAA as a
measure of induced approach/withdrawal motivation changes,
the most prominent line of applied FAA research investigates
the relationship between FAA at rest and affective disorders,
especially focusing on the major depressive disorder (MDD).
Roughly following Thibodeau, Jorgensen and Kim’s (2006)
excellent meta-analysis on the matter, this part of this review
will focus on four aspects: First, studies supporting the claim
that FAA can differentiate between participants currently suf-
fering from MDD and healthy controls are introduced. Second,
evidence for FAA as an indicator of risk for MDD is reviewed.
Third, studies testing FAA as an indicator for therapeutic suc-
cess are discussed. This aspect was not considered in Thibo-
deau et al. (2006) and is thus discussed more extensively. Fi-
nally, the problem of comorbidity of MDD with other affective
disorders such as the anxiety disorder is addressed.
MDD is one of the most prevalent affective disorders at pre-
sent (Kurth, 2012), which entails the importance of effective
diagnostic tools. FAA analysis seems to reliably discriminate
between depressed and control subjects, by revealing decreased
left-frontal lateralization for depressed subjects in the presence
(Stewart, Coan, Towers, & Allen, 2011) and absence of exter-
nal stimulation (Allen, Urry, Hitt, & Coan, 2004; Gotlieb, Ran-
ganath, & Rosenfeld, 1998). Allen et al. (2011) document in-
ternal consistency and stability of FAA scores in depressed
participants over a time period of altogether 16 weeks, and
Rosenfeld, Baehr, Baehr, Gotlib and Ranganath (1996) report
therapy-induced affective changes in five depressed participants
being significantly related to the FAA score. Based on alto-
gether 31 studies using 59 separate tests, Thibodeau et al. (2006)
conclude that the existing data suggest a relation between de-
pression and reduced relative left-frontal activation, with age,
recording duration and operationalization of depresssion as
important moderating factors.
However, FAA is not only sensitive to the current status of
patients (depressed versus non-depressed), but also differenti-
ates high from low depression risk groups. Tomarken, Dichter,
Garber and Simien (2004), who contrasted adolescents whose
mothers had a history of depression (high risk group) with ado-
lescents with never-depressed mothers (low risk group), found
decreased relative left-frontal activation for the high risk group.
Bruder, Tenke, Warner and Weissman (2005) basically repli-
cated this result for grandchildren of depressed (high risk) ver-
sus never-depressed (low risk) parents and grandparents1.
Complementing this line of research, FAA analysis may also
be used as an indicator of the effects of therapeutic intervention.
Based on FAA theory, a decrease in depressive symptoms and
an increase in well-being should be accompanied by an increase
in left-frontal activity, given that reduced left-frontal activity is
characteristic for MDD—which is supported by a study by
Segrave et al. (2011). Contrasting depressed participants with
and without current medication, significantly larger relative
left-frontal activity was found for participants taking antide-
pressants, even though both groups did not differ with respect
1The effect was significant over parietal rather than frontal electrodes, how-
ever.
264

B. B. BRIESEMEISTER ET AL.
to the severity of symptoms. It should be noted, however, that
these results stem from a between-subject design, which means
that these differences may also be explained by confounding
between-subject variables. Rosenfeld et al. (1996), in contrast,
report strong correlations between the FAA metric and affect
change in depressed participants. A comparison of depressed
participants before and after a 12 week antidepressant treatment
replicated this result (Bruder et al., 2001), identifying lateral-
ized alpha power as significant predictor of treatment outcome
in a multiple regression analysis. Moreover, participants who
did not respond to medication showed greater right-hemispheric
activation in FAA analysis than participants who did, suggest-
ing that FAA might be able to predict medication success.
While most FAA studies on depression report promising re-
sults, with the meta-analysis by Thibodeau et al. (2006) reveal-
ing moderately large effect sizes, several studies also report null
effects (Thibodeau et al., 2006). Since reliable results are of
great importance for diagnostic purposes, first studies try to
improve the diagnostic value of FAA by developing advanced
FAA analysis techniques. Segrave et al. (2011), for example,
contrasted FAA data within a predefined alpha range (8 - 13 Hz)
with FAA data using individualized alpha bandwidths. This
approach aims at reducing between-subject variance not related
to depression. No significant differences between the two ap-
proaches were found, but since depressed and normal control
subjects did not differ in any FAA analysis, these results are
difficult to interpret anyway. Baehr, Rosenfeld, Baehr and Ear-
nest (1998) took a different approach, comparing the predictive
power of the standard FAA score with the percentage of time
where the asymmetry score was greater than 0, which indicates
relative left-hemispheric activity. Both measures discriminated
between depressed participants and controls, but the percentage
score was the better predictor on a single-subject level with a
discrimination rate of 83% (versus 54% for FAA).
According to FAA theory, hemispheric asymmetries are an
indicator of approach/withdrawal motivation and thus not spe-
cific for a single affective disorder. This is probably the greatest
challenge for FAA as a diagnostic tool. MDD is typically char-
acterized by reduced positive affect and a loss of interest and
energy, which—in terms of the approach/withdrawal frame-
work—means reduced approach motivation causing reduced
left-frontal activity. Other affective disorders, however, should
affect FAA in similar ways. Anxiety disorder, as a prominent
example, is characterized by strongly increased withdrawal
motivation, and should, thus, affect FAA analysis. In fact, Thi-
bodeau et al. (2006) summarize several studies which indicate
relative right-hemispheric activation for anxiety patients. Mos-
covitch et al. (2011) report that successful cognitive behavioral
therapy for socially anxious patients is accompanied by a shift
from relative right-hemispheric to relative left-hemispheric
activity, with relative left FAA at rest before the treatment pre-
dicting greater reduction and an overall lower level of social
anxiety after successful treatment. A comparison of depressed
patients with patients diagnosed with post-traumatic stress dis-
order—both disorders being associated with reduced approach/
increased withdrawal motivation—and healthy controls found
differential effects for both disorders (Kemp et al., 2010). Ke-
une et al. (2011), in contrast, focused on subjects with increased
approach motivation, namely patients with attention deficit
hyperactivity disorder (ADHD). In comparison to healthy con-
trols, ADHD patients were characterized by increased left-
frontal activity. Moreover, ADHD patients scored significantly
higher on the hostility subscale of an aggression questionnaire,
which further supports the approach/withdrawal FAA theory.
Frontal Alpha Asymmetry as a Therapeutic Tool
Thibodeau et al. (2006: p. 728) conclude their meta-analysis
by saying “that both depression and anxiety are meaningfully
related to relative right-sided resting frontal EEG asymmetry.”
Spronk, Arns, Bootsma, van Ruth and Fitzgerald (2008) even
suggest a causal relationship, showing remission of depressive
symptoms after repeated use of repetitive TMS stimulation over
left-frontal sides. Rosenfeld, Cha, Blair and Gotlib (1995)
document that FAA can be modulated by simple training, and
Allen, Harmon-Jones and Cavender (2001) report that the ma-
nipulation of FAA affects emotional responses in healthy par-
ticipants. Taken together, these studies suggest that FAA might
be able to not only diagnose but actually affect depressive
symptoms via neurofeedback. Initial studies training depressed
participants to increase their relative left-hemispheric activity
document that depressive symptoms indeed decrease after neu-
rofeedback training (Baehr, Rosenfeld, & Baehr, 1997; Ham-
mond, 2000, Rosenfeld et al., 1996), even when participants
were under age (Earnest, 1999).
Baehr et al. (1997) additionally report two case studies where
both patients received medication and psychotherapy before the
neurofeedback study had started, but neither of them made sig-
nificant progress until neurofeedback was introduced. FAA
treatment not only affected the depressive symptoms, but also
improved flexibility in thinking and induced a positive outlook
for the future compared to before treatment (Baehr et al., 1997).
Choi et al. (2010) basically replicated these results, contrasting
neurofeedback with psychotherapy placebo in depressed pa-
tients. Neurofeedback exceeded psychotherapy placebo in terms
of decreased depressive symptoms, enhanced executive func-
tions and enhanced verbal fluency. Moreover, benefits from
neurofeedback seem to be stable long after the actual training
has ended, as indicated by a 5 year follow-up study (Baehr,
Rosenfeld, & Baehr, 2001).
Given the bilateral relationship between FAA and depres-
sion—depression is characterized by decreased left-frontal ac-
tivity, and manipulations within FAA affect depressive symp-
toms—Choi et al. (2010: p. 49) conclude that the result of their
study “suggested a causal relationship between the asymmetric
frontal activity and depressive symptoms”. An earlier paper by
Davidson (1998), in contrast, favors a less deterministic view of
FAA, suggesting that “differences in prefrontal activation are
neither necessary nor sufficient for the production of a specific
type of affective style or psychopathology” but should be seen
as “diatheses that bias a person’s affective style, and then in
turn modulate an individual’s vulnerability to develop depres-
sion” (Davidson, 1998: p. 608).
Irrespective of the theoretical background, two recent studies
document that FAA training is not only effective in depression
therapy, but generally helps patients suffering from disorders that
affect approach/withdrawal motivation (Hammond, 2005; Kerson,
Sherman, & Kozlowski, 2009). The presented literature thus
strongly suggests that FAA can be a successful therapeutic tool.
Conclusion
Approach/withdrawal motivation as a theoretical construct
can be useful in several applied contexts. FAA analysis has

B. B. BRIESEMEISTER ET AL.
been established as a reliable index influenced by approach/
withdrawal motivation, and the analysis of the probability of
approach/withdrawal motivation given an FAA effect (see Ta-
ble 1) revealed a substantial relationship (Kass & Raftery,
1995), thus allowing for a cautious interpretation of FAA ef-
fects in the absence of an a-priori hypothesis.
Keeping in mind that the application of FAA in applied re-
search is still in its infancy, the present review was meant to
introduce and summarize some of the first studies documenting
the various possibilities of FAA application. Based on the re-
viewed studies, FAA analysis seems to be a promising diag-
nosetic tool at least in marketing and in the detection of affec-
tive disorders. Moreover, assuming that affective gaming con-
tinues to gain popularity, BCI might also profit from FAA
analysis.
Finally, based on the existing literature, FAA seems to sig-
nificantly contribute to the diagnosis and—even more impor-
tant—treatment of affective disorders, such as depression and
anxiety. Even though this area of application still needs further
systematic research including experimental studies on larger
samples, the existing results are promising. Given that the case-
study by Baehr et al. (1997) indicates that FAA neurofeedback
successfully complements medication and psychotherapy, and
given that Choi et al. (2010) report a neurofeedback-related
increase in several cognitive functions exceeding the expected
decrease in depressive symptoms, the diagnosis and treatment
of affective disorders is a field which will greatly benefit from
FAA application in the future.
Acknowledgements
This work was financially supported by the German Federal
Ministry of Education and Research (BMBF) under the re-
search initiative ‘ForMaT’ (03FO2912).
REFERENCES
Adams, R. G., Bahr, G. S., & Moreno, B. (2008). Brain computer in-
terfaces: Psychology and pragmatic perspectives for the future. Pro-
ceedings of the AISB 2008 Symposium on Brain Computer Interfaces
and Human Computer Interaction: A C on ve rg enc e of Ideas, 5, 1-6.
Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004). Issues and assump-
tions on the road from raw signals to metrics of frontal EEG asym-
metry in emotion. Biological Psychology, 67, 183-218.
doi:10.1016/j.biopsycho.2004.03.007
Allen, J. J. B., Harmon-Jones, E., & Cavender, J. H. (2001) Manipula-
tion of frontal EEG asymmetry through biofeedback alters self-re-
ported emotional responses and facial EMG. Psychophysiology, 38,
685-693. doi:10.1111/1469-8986.3840685
Allen, J. J. B., Urry, H. L., Hitt, S. K., & Coan, J. A. (2004). The stabil-
ity of resting frontal electroencephalographic asymmetry in depress-
sion. Psychophysio l ogy , 41, 269-280.
doi:10.1111/j.1469-8986.2003.00149.x
Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype
of neuroimaging in business. Nature Reviews Neuroscience, 11, 284-
292. doi:10.1038/nrn2795
Baehr, E., Rosenfeld, J. P., & Baehr, R. (1997). The clinical use of an
alpha asymmetry protocol in the neurofeedback treatment of depress-
sion. Journal of Neurotherapy: Investigations in Neuromodulation,
Neurofeedback and Applied N eu r o science, 2, 10-23.
Baehr, E., Rosenfeld, J. P., & Baehr, R. (2001). Clinical use of an alpha
asymmetry neurofeedback protocol in the treatment of mood disor-
ders: Follow-up study one to five years post therapy. Journal of Neu-
rtherapy, 4, 11-18. doi:10.1300/J184v04n04_03
Baehr, E., Rosenfeld, J. P., Baehr, R., & Earnest, C. (1998). Compari-
son of two EEG asymmetry indices in depressed patients vs. normal
controls. International Journal of Psychophysiology, 31, 89-92.
doi:10.1016/S0167-8760(98)00041-5
Baldwin, C. L., & Penaranda, B. N. (2012). Adaptive training using an
artificial neural network and EEG metrics for within- and cross-task
workload classification. NeuroImage, 59, 48-56.
doi:10.1016/j.neuroimage.2011.07.047
Berkman, E. T., & Lieberman, M. D. (2010). Appoaching the bad and
avoiding the good: Lateral prefrontal cortical asymmetry distin-
guishes between action and valence. Journal of Cognitive Neurosci-
ence, 22, 1970-1979. doi:10.1162/jocn.2009.21317
Bruder, G. E., Stewart, J. W., Tenke, C. E., McGrath, P. J., Leite, P.,
Bhattacharya, N., & Quitkin, F. M. (2001). Electroencephalographic
and perceptual asymmetry differences between responders and non-
responders to an SSRI antidepressant. Biological Psychiatry, 49,
416-425. doi:10.1016/S0006-3223(00)01016-7
Bruder, G. E., Tenke, C. E., Warner, V., & Weissman, M. M. (2005).
Grandchildren at high and low risk for depression differ in EEG
measures of regional brain asymmetry. Biological Psychiatry, 62,
1317-1323. doi:10.1016/j.biopsych.2006.12.006
Burshteyn, D., & Buff, C. L. (2008). Provate-label brands, manufac-
turer brands, and the quest for stimulus generalization: An EEG ana-
lysis of frontal cortex response. Review of Business Research, 8, 92-
96.
Carver, C. S., & Harmon-Jones, E. (2009). Anger is an approach-re-
lated affect: Evidence and implications. Psychological Bulletin, 135,
183-204. doi:10.1037/a0013965
Choi, S. W., Chi, E. E., Chung, S. Y., Kim, J. W., Ahn, C. Y., & Kim,
H. T. (2010). Is alpha wave neurofeedback effective with random-
ized clinical trials in depression? A pilot study. Neuropsychobiology,
63, 43-51. doi:10.1159/000322290
Davidson, R. J. (1988). EEG measures of cerebral asymmetry: Con-
ceptual and methodological issues. International Journal of Neuro-
science, 39, 71-89. doi:10.3109/00207458808985694
Davidson, R. J. (1998). Anterior electrophysiological asymmetries,
emotion, and depression: Conceptual and methodological conun-
drums. Psychophysiology, 35, 607-614.
doi:10.1017/S0048577298000134
Davidson, R. J., Schwartz, G. E., Saron, C., Bennett, J., & Goleman, D.
J. (1979). Frontal versus parietal EEG asymmetry during positive and
negative affect. Ps ychophys iology, 16, 202-203.
Earnest, C. (1999). Singe case study of EEG asymmetry biofeedback
for depression: An independent replication in an adolescent. Journal
of Neurotherapy: Investigations in Neuromodulation, Neurofeedback
and Applied Neuroscience, 3, 28-35.
Field, T., Diego, M., Hernandez-Reif, M., Cisneros, W., Feijo, L., Vera,
Y., & He, Q. C. (2005). Lavender fragrance cleansing gel effects on
relaxation. I nte rn a t io n al J ou r nal of Neuroscience, 115, 207-222.
doi:10.1080/00207450590519175
Gable, P., & Harmon-Jones, E. (2008). Relative left frontal activation
to appetitive stimuli: Considering the role of individual differences.
Psychophysiology, 45, 275-278.
doi:10.1111/j.1469-8986.2007.00627.x
Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High-resolu-
tion EEG mapping of cortical activation related to working memory:
Effects of task difficulty, type of processing, and practice. Cerebral
Cortex, 7, 374-385. doi:10.1093/cercor/7.4.374
Gilleade, K. M., & Allanson, J. (2005). Affective videogames and
modes of affective gaming: Assist me, challenge me, emote me.
Proceedings of DiGRA 2005 Conference: Changing Views. Worlds
in Play, 1-7.
Gotlieb, I. H., Ranganath, C., & Rosenfeld, P. (1998). Frontal EEG
alpha asymmetry, depression, and cognitive functioning. Cognition
& Emotion, 12, 449-478. doi:10.1080/026999398379673
Hammond, D. C. (2000). Neurofeedback treatment of depression with
the Roshi. Journal of Neurotherapy: Investigations in Neuromodula-
tion, Neurofeedback and Applied Neuroscience, 4, 45-56.
Hammond, D. C. (2005). Neurofeedback treatment of depression and
anxiety. Journal of Adult Development, 12, 131-137.
doi:10.1007/s10804-005-7029-5
Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of
asymmetric cortical activity in emotion-related phenomena: A review
266

B. B. BRIESEMEISTER ET AL.
and update. Biological Psychology, 84, 451-462.
doi:10.1016/j.biopsycho.2009.08.010
Harmon-Jones, E., Gable, P. A., & Price, T. F. (2011). Leaning embod-
ies desire: Evidence that leaning forward increases relative left fron-
tal activation to appetitive stimuli. Biological Psychology, 87, 311-
313. doi:10.1016/j.biopsycho.2011.03.009
Huang, D., Zhang, H., Ang, K., Guan, C., Pan, Y., Wang, C., & Yu, J.
(2012). Fast emotion detection from EEG using asymmetric spatial
filtering. Proceedings of the International Conference on Acoustics,
Speech and Signal P r ocessing (ICASSP), 589-592.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the
American Statistical Associatio n, 90, 773-795.
doi:10.1080/01621459.1995.10476572
Kemp, A. H., Griffiths, K., Felmingham, K. L., Shankman, S. A., Drin-
kenburg, W., Arns, M., & Bryant, R. A. (2010). Disorder specificity
despite comorbidity: Resting EEG alpha asymmetry in major depres-
sive disorder and post-traumatic stress disorder. Biological Psychol-
ogy, 85, 350-354. doi:10.1016/j.biopsycho.2010.08.001
Kerson, C., Sherman, R. A., & Kozlowski, G. P. (2009). Alpha sup-
pression and symmetry training for generalized anxiety symptoms.
Journal of Neurotherapy, 13, 146-155.
doi:10.1080/10874200903107405
Keune, P. M., Schönenberg, M., Wyckoff, S., Mayer, K., Riemann, S.,
Hautzinger, M., & Strehl, U. (2011). Frontal alpha-asymmetry in
adults with attention deficit hyperactivity disorder: Replication and
specification. Biological Psychology, 87, 306-310.
doi:10.1016/j.biopsycho.2011.02.023
Kline, J. P., Blackhart, G. C., Woodward, K. M., Williams, S. R., &
Schwartz, G. E. R. (2000). Anterior electroencephalographic asym-
metry changes in elderly women in response to a pleasant and an un-
pleasant odor. Biologica l Psychology, 52, 241-250.
doi:10.1016/S0301-0511(99)00046-0
Ko, K.-E., Yang, H. C., & Sim, K.-B. (2009). Emotion recognition
using EEG signals with relative power values and Bayesian network.
International Journal of Control, Automation, and Systems, 7, 865-
870. doi:10.1007/s12555-009-0521-0
Kurth, B.-M. (2012). Erste Ergebnisse aus der “Studie zur Gesundheit
Erwachsener in Deutschland” (DEGS). Bundesgesundheitsblatt, 55,
980-990. doi:10.1007/s00103-012-1504-5
Molina, G. G., Tsoneva, T., & Nijholt, A. (2009). Emotional brain-
computer interfaces. Proceedings of the International Conference on
Affective Computing and Intel ligent Interaction, 1-9.
Moscovitch, D. A., Santesso, D. L., Miskovic, V., McCabe, R. E., An-
tony, M. M., & Schmidt, L. A. (2011). Frontal EEG asymmetry and
symptom response to cognitive behavioral therapy in patients with
social anxiety disorder. Biological Psychology , 87, 379-385.
doi:10.1016/j.biopsycho.2011.04.009
Mühl, C., Brouwer, A.-M., van Wouwe, N. C., van den Broek, E. L.,
Nijboer, F., & Heylen, D. K. J. (2011). Modality-specific affective
responses and their implications for affective BCI. Proceedings of
the Fifth International Brain-Computer Interface Conference 2011,
120-123.
Ohme, R., Matukin, M., & Szezurko, T. (2010). Neurophysiology un-
covers secrets of TV commercials. Der markt, 49, 133-142.
doi:10.1007/s12642-010-0034-7
Ohme, R., Reykowska, D., Wiener, D., & Choromanska, A. (2009).
Analysis of neurophysiological reactions to advertising stimuli by
means of EEG and galvanic skin response measures. Journal of
Neuroscience, Psychology, and E c o nomics, 2, 21-31.
doi:10.1037/a0015462
Ohme, R., Reykowska, D., Wiener, D., & Choromanska, A. (2010).
Application of frontal EEG asymmetry to advertising research. Jour-
nal of Economic Psychology, 31, 785-793.
doi:10.1016/j.joep.2010.03.008
Park, M.-K., & Watanuki S. (2005). Electroencephalograhic responses
and subjective evaluation on unpleasantness induced by sanitary
napkins. Journal of Physiological Anthropological and Applied Hu-
man Science, 24, 67-71. doi:10.2114/jpa.24.67
Poldrack, R. A. (2006). Can cognitive processes be inferred from
neuroimaging data? Trends in Cognitive Sciences, 10, 59-63.
doi:10.1016/j.tics.2005.12.004
Price, T. F., Peterson, C. K., & Harmon-Jones, E. (2012). The emotive
neuroscience of embodiment. Motivation and Emotion, 36, 27-37.
doi:10.1007/s11031-011-9258-1
Rosenfeld, J. P., Baehr, E., Baehr, R., Gotlib, I. H., & Ranganath, C.
(1996). Preliminary evidence that daily changes in frontal alpha
asymmetry correlate with changes in affect in therapy sessions. In-
ternational Journal of Psy ch op hys io lo gy , 23, 137-141.
doi:10.1016/0167-8760(96)00037-2
Rosenfeld, J. P., Cha, G., Blair, T., & Gotlib, I. H. (1995). Operant
(biofeedback) control of left-right frontal alpha power differences:
Potential neurotherapy for affective disorders. Biofeedback and Self-
Regulation, 20, 241-258. doi:10.1007/BF01474516
Rutherford, H. J. V., & Lindell, A. K. (2011). Thriving and surviving:
Approach and avoidance motivation and lateralization. Emotion Re-
view, 3, 333-343. doi:10.1177/1754073911402392
Sanders, C., Diego, M., Fernandez, M., Field, T., Hernandez-Reif, M.
& Roca, A. (2002). EEG asymmetry responses to lavender and rose-
mary aromas in adults and infants. International Journal of Neuro-
science, 112, 1305-1320. doi:10.1080/00207450290158214
Segrave, R. A., Cooper, N. R., Thomson, R. H., Croft, R. J., Sheppard,
D. M., & Fitzgerald, P. B. (2011). Individualized alpha activity and
frontal asymmetry in major depression. Clinical EEG and Neurosci-
ence, 42, 45-52. doi:10.1177/155005941104200110
Singer, E. (2008). Brain Games. Technology Review, 111, 82-84.
Spronk, D., Arns, M., Bootsma, A., van Ruth, R., & Fitzgerald, P. B.
(2008). Long term effects of left frontal rTMS on EEG and ERPs in
patients with depression. Clinical EEG and Neuroscience, 39, 118-
124. doi:10.1177/155005940803900305
Stewart, J. L., Coan, J. A., Towers, D. N., & Allen, J. J. B. (2011).
Frontal EEG asymmetry during emotional challenge differentiates
individuals with and without major depressive disorder. Journal of
Affective Disorders, 129, 167-174. doi:10.1016/j.jad.2010.08.029
Thibodeau, R., Jorgensen. R. S., & Kim, S. (2006). Depression, anxiety,
and resting frontal EEG asymmetry: A meta-analytic review. Journal
of Abnormal Psychology, 115, 715-729.
doi:10.1037/0021-843X.115.4.715
Tomarken, A. J., Dichter, G. S., Garber, J., & Simien, C. (2004). Rest-
ing frontal brain activity: Linkages to maternal depression and socio-
economic status among adolescents. Biological Psychology, 67, 77-
102. doi:10.1016/j.biopsycho.2004.03.011
Tomico, O., Mizutani, N., Levy, P., Takahiro, Y., Cho, Y., & Yama-
naka, T. (2008). Kansei physiological measurements and construc-
tivist psychological explorations for approaching user subjective ex-
perience during and after product usage. Proceedings of the DESIGN
2008, 10th International Desig n Conference. Croatia, 529-536
Vartak, A. A. (2010). Biosignal processing challenges in emotion rec-
ognition for adaptive learning. Ph.D. Thesis, Orlando, FL: University
of Central 2010.
Vecchiato, G., Toppi, J., Astolfi, L., De Vico Fallani, F., Cincotti, F.,
Mattia, D., & Babiloni, F. (2010). Changes in brain activity during
the observation of TV commercials by using EEG, GSR and HR
measurements. Brain Topography, 23, 165-179.
doi:10.1007/s10548-009-0127-0
Vecchiato, G., Toppi, J., Astolfi, L., De Vico Fallani, F., Cincotti, F.,
Mattia, D., & Babiloni, F. (2011). Spectral EEG frontal asymmetries
correlate with the experienced pleasantness of TV commercial adver-
tisements. Medical & Biological Engineering & Computing, 49, 579-
583. doi:10.1007/s11517-011-0747-x
Wacker, J., Chavanon, M.-L., Leue, A., & Stemmler, G. (2010). Trait
BIS predicts alpha asymmetry and P300 in a Go/No-Go task. Euro-
pean Journal of Personality, 24, 85-105.
Winkler, I., Jäger, M., Mihajlović, V., & Tsoneva, T. (2010). Frontal
EEG asymmetry based classification of emotional valence using
common spatial patterns. World Academy of Science, Engineering
and Technology, 45, 373-378.
Wyczesany, M., Kaiser, J., & Barry, R. J. (2009). Cortical lateralization
patterns related to self-estimation of emotional state. Acta Neurobiolo-
giae Experimentalis, 69, 526-536.