Specific effects of EEG based neurofeedback training on memory functions in post-stroke victims

Abstract

Background:
Using EEG based neurofeedback (NF), the activity of the brain is modulated directly and, therefore, the cortical substrates of cognitive functions themselves. In the present study, we investigated the ability of stroke patients to control their own brain activity via NF and evaluated specific effects of different NF protocols on cognition, in particular recovery of memory.

Methods:
N = 17 stroke patients received up to ten sessions of either SMR (N = 11, 12-15 Hz) or Upper Alpha (N = 6, e.g. 10-12 Hz) NF training. N = 7 stroke patients received treatment as usual as control condition. Furthermore, N = 40 healthy controls performed NF training as well. To evaluate the NF training outcome, a test battery assessing different cognitive functions was performed before and after NF training.

Results:
About 70 % of both patients and controls achieved distinct gains in NF performance leading to improvements in verbal short- and long-term memory, independent of the used NF protocol. The SMR patient group showed specific improvements in visuo-spatial short-term memory performance, whereas the Upper Alpha patient group specifically improved their working memory performance. NF training effects were even stronger than effects of traditional cognitive training methods in stroke patients. NF training showed no effects on other cognitive functions than memory.

Conclusions:
Post-stroke victims with memory deficits could benefit from NF training as much as healthy controls. The used NF training protocols (SMR, Upper Alpha) had specific as well as unspecific effects on memory. Hence, NF might offer an effective cognitive rehabilitation tool improving memory deficits of stroke survivors.

Full text

R E S E A R C H Open Access
Specific effects of EEG based
neurofeedback training on memory
functions in post-stroke victims
Silvia Erika Kober
1,2*
, Daniela Schweiger
1
, Matthias Witte
1
, Johanna Louise Reichert
1
, Peter Grieshofer
3
,
Christa Neuper
1,2,4
and Guilherme Wood
1,2
Abstract
Background: Using EEG based neurofeedback (NF), the activity of the brain is modulated directly and, therefore,
the cortical substrates of cognitive functions themselves. In the present study, we investigated the ability of stroke
patients to control their own brain activity via NF and evaluated specific effects of different NF protocols on cognition,
in particular recovery of memory.
Methods: N= 17 stroke patients received up to ten sessions of either SMR (N= 11, 12–15 Hz) or Upper Alpha
(N=6,e.g. 10–12 Hz) NF training. N= 7 stroke patients received treatment as usual as control condition. Furthermore,
N= 40 healthy controls performed NF training as well. To evaluate the NF training outcome, a test battery assessing
different cognitive functions was performed before and after NF training.
Results: About 70 % of both patients and controls achieved distinct gains in NF performance leading to improvements
in verbal short- and long-term memory, independent of the used NF protocol. The SMR patient group showed specific
improvements in visuo-spatial short-term memory performance, whereas the Upper Alpha patient group specifically
improved their working memory performance. NF training effects were even stronger than effects of traditional
cognitive training methods in stroke patients. NF training showed no effects on other cognitive functions than
memory.
Conclusions: Post-stroke victims with memory deficits could benefit from NF training as much as healthy controls.
The used NF training protocols (SMR, Upper Alpha) had specific as well as unspecific effects on memory. Hence, NF
might offer an effective cognitive rehabilitation tool improving memory deficits of stroke survivors.
Keywords: Cognitive rehabilitation, EEG, Neurofeedback, Memory, Stroke recovery
Background
Approximately two-thirds of stroke patients experience
cognitive impairment following stroke including failures
in executive functions, memory, language, visuo-spatial
abilities, or global cognitive functioning [1]. Traditional
cognitive rehabilitation methods have not proven fruitful
or they have not been evaluated sufficiently yet [2–4]. A
recent review by Elliott and Parente (2014) indicated
that on the one hand traditional memory rehabilitation
is an effective therapeutic intervention after stroke, espe-
cially to improve working memory performance, but on
the other hand significant memory improvement also
occurred spontaneously over time [5]. Some major draw-
backs of traditional cognitive rehabilitation are the em-
ployment of similar tasks for training and evaluation of
outcomes, the requirement of overt responses from the
patients, its dependence on relatively complex verbal
instructions, and the requirement of a lot of cognitive
effort. The aim of the present study was to evaluate a
new rehabilitation strategy suitable to overcome the usual
pitfalls of traditional cognitive rehabilitation. An adaptive
human-computer interface architecture for improving
cognition, in particular memory, was evaluated. This setup
* Correspondence: silvia.kober@uni-graz.at
1
Department of Psychology, University of Graz, Universitaetsplatz 2/III, Graz
8010, Austria
2
BioTechMed-Graz, Graz, Austria
Full list of author information is available at the end of the article
© 2015 Kober et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107
DOI 10.1186/s12984-015-0105-6
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

modulated electrical brain activity using Electroencephalo-
gram (EEG) based neurofeedback (NF) as a cognitive
rehabilitation tool for stroke patients.
Using EEG based NF, the electrical activity of the brain
is modulated directly and, therefore, the cortical substrates
of cognitive functions themselves. This direct access to
neural activity by means of NF may alter or accelerate
functional reorganization in the brain following stroke,
indicating the great potential value of NF in cognitive
rehabilitation. Hence, NF might speed up functional re-
covery or even enable functional recovery that otherwise
would not have occurred [6]. When healthy participants
successfully modulate their own brain activity, e.g. increas-
ing voluntarily specific EEG frequency bands, improve-
ments in cognition and behavior usually follow [7, 8].
In the present study, we evaluated the effects of NF
training on memory in stroke patients. We used two NF
training protocols with beneficial effects on memory in
healthy people: SMR (sensorimotor rhythm, 12–15 Hz)
and UA (Upper Alpha, e.g. 10–12 Hz) based NF. In prior
studies, healthy participants, who were trained to increase
their SMR power, showed improvements in declarative
memory performance [7–12], referring to memories which
can be consciously recalled such as facts and knowledge
[13]. Generally, SMR is largest over central scalp regions
over the sensorimotor cortex, it is generated in a thalamo-
cortical network, emerges when one is motionless yet re-
mains attentive, and is suppressed by movement [14–16].
This EEG rhythm is associated with “internal inhibition”,
since there is evidence that during SMR activity the
conduction of somatosensory information to the cortex
is attenuated or inhibited [15]. This inhibition of somato-
sensory information flow to the cortex during increased
SMR activity is associated with improved cognitive per-
formance. Sterman (1996) proposed that motor activity
may interfere with perceptual and integrative components
of information processing, since motor activity can disen-
gage visual processing areas of the cortex. Such sensori-
motor interference with visual processing may hamper
cognitive performance [15, 17]. In this context, voluntary
control of sensorimotor excitability by means of SMR
based NF training may facilitate cognitive processing by
decreasing sensorimotor interference and maintaining
perceptual and memory functions at the same time [15].
In line with this assumption, Kober et al. (2015) could
show that SMR based NF training leads to reduced sen-
sorimotor interference and can thereby promote cognitive
processing in healthy people [9].
Increasing UA activity by means of NF training also
causes memory improvements, especially improvements
in working memory (WM) and short-term memory per-
formance [18–24]. Alpha oscillations are generally most
pronounced over parieto-occipital areas [25]. It is assumed
that alpha activity inhibits processes unnecessary for or
conflicting to the task being performed, thus facilitating
attention and memory by actively suppressing distracting
stimuli [26]. Klimesch (1999) proposed to split up the
alpha frequency range (e.g. 8–12 Hz) in a lower (e.g.
8–10 Hz) and upper (e.g. 10–12 Hz) alpha band, since
the upper and lower alpha frequency range were linked to
different cognitive processes. While lower alpha activity is
associated with attentional processes that are relatively
task- and stimulus-non-specific, upper alpha activity is
specifically related to memory performance. In particular,
search and retrieval processes are reflected by upper alpha
oscillations [25].
Several NF studies in healthy participants reported on
a link between the ability to gain control over EEG signals
and cognitive benefits [8]. Similar results were observed in
patients with traumatic brain injury (TBI) [2, 27] and
stroke [28–31]. Single-case studies in stroke patients
found positive but unspecific NF training effects on
cognitive functions [28–32]. However, the generalizability
of these prior findings is limited due to the incomplete de-
scription of training-specific EEG signal changes as well as
the absence of control groups. A study by Hofer et al.
(2014) is one of the first NF training studies investigating
the effects of SMR and Theta/Beta quotient (4-8/13-
21 Hz, T/B NF training) based NF training on cognitive
functions in stroke patients and healthy controls [33].
The authors could demonstrate that stroke patients
with memory impairments showed specific perform-
ance improvements in declarative memory tasks after
SMR NF training, while stroke patients with deficits in
attention and inhibition showed specific improvements
in inhibitory control and cognitive flexibility after repeated
T/B NF training.
In summary, a few prior studies indicated that NF
might be a promising new treatment for cognitive re-
habilitation after stroke [6]. The present study addressed
the following open questions: First, are stroke patients
comparable to controls regarding the ability to modulate
their EEG signal using NF? Second, is the impact of NF
specific regarding cognitive functions such as memory in
stroke patients, or are NF effects more general (e.g.,
global cognitive functioning) [6]? We used two NF
training protocols (SMR, UA), which should have
beneficial effects on different memory functions, and
investigated training effects on attention, inhibition,
cognitive flexibility, short- and long-term memory and
WM in stroke patients. Based on previous investiga-
tions, we hypothesized that stroke patients should be
able to modulate their EEG activity voluntarily by
means of NF training [28–31, 33]. Furthermore, we
expected that SMR and UA based NF protocols should
have specific effects on memory in both stroke patients
and controls [8]. Based on the literature, SMR based
NF training should have specific effects on declarative
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memory performance, whereas UA based NF training
should specifically affect working memory performance.
Furthermore, a comparable control group of stroke pa-
tients was employed who received treatment as usual.
Hence, we could directly compare the effects of NF train-
ing with the effects of traditional cognitive rehabilitation
methods on cognitive functions in stroke patients. As
the electrophysiological balance is disturbed after brain
lesion [2], any intervention might even accentuate this
disturbance and thus may result in negative impact on
cognition. Therefore, particular attention was given to
the inspection of deleterious effects of NF on EEG and
cognition as well.
Methods
Participants
We recruited 24 stroke patients with first-time stroke
for this study. Table 1 summarizes patient specific data.
As this study was a proof-of-principle study, we included
stroke patients with any site of brain lesion and motor
deficit and with a time laps from the event of at least
4 weeks. With regard to the drug therapies administered,
patients treated with drugs that interfere with the vigilance
state were not included. Furthermore, all participants had
normal or corrected-to-normal vision and hearing. Pa-
tients with visual hemi-neglect, dementia (MMSE < 24,
[34]), psychiatric disorders such as depression or anxiety,
Table 1 Patient description
Code Number of NF
training sessions
Sex Age Handedness ICD-10
diagnosis
Lesion location Time since
onset (days)
MMSE NF performance
SMR NF training
1 8 M 65 Lt I63.5 Rt posterior 41 29 +
2 7 M 64 Rt I63.5 Lt internal carotid artery 98 24 +
3 7 M 51 Rt I61.9 Lt basal ganglia 2869 29 -
4 9 M 64 Rt I63.9 Lt thalamus, arteria cerebri posterior
(occipital-medial)
30 29 -
5 10 M 74 Rt I63.1 Bt cerebellum (rt), hippocampus (bt),
mesencephalon (rt), occipital lobe (lt),
splenium (bt)
136 29 +
6 10 M 62 Rt I61.9 Lt arteria cerebri media, occipital-parietal 1783 26 +
7 10 F 52 Rt I63.9 Rt basilar artery, pons- mesencephalon 693 29 +
8 9 F 37 Rt I60.2 Rt arteria communicans posterior, temporal 87 28 +
9 10 M 65 Rt I63.5 Lt arteria cerebri posterior 78 28 +
10 10 M 62 Rt I63.5 Lt arteria cerebri media 247 29 -
11 10 M 50 Rt I64 Bt basal ganglia, corpus collosum
(truncus, genu), inferior temporal
2714 29 +
Upper Alpha NF training
12 10 M 72 Rt I61.9, I60.9 Bt arteria cerebri media, occipital-parietal,
frontal
808 30 +
13 6 M 73 Rt I63.3 Lt arteria cerebri media 2111 29 +
14 8 F 82 Rt I63.9 Lt pons 104 28 -
15 10 F 53 Rt I63.5 Rt arteria cerebri media 930 30 -
16 10 M 76 Rt I63.3 Rt arteria cerebri media 133 24 +
17 5 M 71 Rt I63.9 Rt arteria cerebri media 362 29 +
Treatment as usual
18 M 75 Rt I63.9 Rt arteria cerebri media 87 27
19 M 57 Rt I63.5 Lt arteria cerebri media 93 27
20 M 78 Rt I63.9 Lt posterior occipital, cerebellum 88 28
21 M 64 Rt I63.3 Rt arteria cerebri media 61 24
22 M 49 Rt I63.5 Lt capsula interna 32 28
23 W 61 Rt I60.9 Lt arteria communicans interior 138 29
24 M 71 Rt I63.8 Rt cerebellum 43 29
Bt bilateral, Ffemale, Lt left, Mmale, MMSE mini-mental state examination, and Rt right. NF neurofeedback performance: “+”indicates that the patient was able to
linearly increase the trained frequency band, “-“indicates that the patient was not able to linearly increase the trained frequency band
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other concomitant neurological disorders (e.g. Parkinson
disease; visual-reflex epilepsy), aphasia, or insufficiently
motivation and cooperation were excluded from the study.
All participants gave written informed consent to partici-
pate. We have also obtained consent from the participants
to publish and to report individual patient data. The study
was approved by the local ethics committee of the Univer-
sity of Graz (reference number GZ. 39/22/63 ex 2011/12
and GZ. 39/21/63 ex 2011/12) and was in line with the
code of ethics of the World Medical Association, Dec-
laration of Helsinki.
Stroke patients showed deficits (T-scores < 40) in tests
assessing verbal (CVLT: California Verbal Learning Test,
all parameters, [35]; VVM2: Visual and Verbal Memory
Test, subscale “construction”, [36]) and visuo-spatial
(VVM2: subscale “city map”, [36]) short- and long-term
memory performance during the pre-assessment (Fig. 3).
In line with previous NF training studies [33, 37], stroke
patients were assigned to the NF protocols depending
on their most prominent cognitive deficits as assessed
before the start of the NF training. N= 11 (9 men, 2
women; mean age 58.72 years, SE = 3.08; age range 37–
74 years) stroke patients with memory deficits, especially
with deficits in long-term memory performance, performed
SMR (12–15 Hz) based NF training. N=6 (4 men, 2
women; mean age 71.17 years, SE = 3.98; age range 53–82
years) stroke patients with memory deficits, especially with
deficits in their WM performance, participated in an UA
NF training (training frequency: 2 Hz above the individual
Alpha frequency, [25]). Furthermore, N=7 (6 men, 1
women; mean age 65.00 years, SE = 3.91; age range 49–78
years) stroke patients with memory deficits received
traditional cognitive training during their stationary
stay in the rehabilitation clinic Judendorf-Strassengel,
Austria. This group forms the treatment as usual (TAU)
group. Treatment as usual was comparable to NF training
in terms of training frequency and duration. Note that it
was not possible to perform all neuropsychological tests
with the TAU group during the pre- and post-assessment,
since not all tests were available at the clinic and because
of economic reasons. The cognitive profiles of patients re-
ceiving either SMR NF training, UA NF training or TAU
are illustrated in Fig. 3.
Additionally, a neurologically healthy control group
(CG) (N= 40; 17 men, 23 women; mean age 59.63 years,
SE = 1.41; age range 41–73 years) was recruited. N=16
(9 men, 7 women; mean age 55.13 years, SE = 2.65; age
range 41–70 years) controls performed SMR NF training
and N= 24 (8 men, 16 women; mean age 62.63 years,
SE = 1.25; age range 50–73 years) controls received UA
based NF training. The healthy CG showed no deficits
in any test parameter (Fig. 3).
Fig. 1 illustrates the design of the whole study in more
detail.
Fig. 1 Design of the whole study, demonstrating the procedure for each group
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Neuropsychological assessment of cognitive functions
In pre- and post-assessment (before and after NF/cog-
nitive training) participants performed standardized
neuropsychological tests to assess attention, divided
attention, inhibition, cognitive flexibility, declarative mem-
ory (long-term memory), short-term memory, and WM.
The pre- and post-assessment was performed a few days
before and after training, respectively. In Table 2 the list of
neuropsychological tests assessing different cognitive func-
tions can be found.
Description of both NF training protocols, EEG data
recording and analysis
For both NF training protocols, EEG signal was recorded
using a 10-channel amplifier (NeXus-10 MKII, Mind
Media BV) with a sampling frequency of 256 Hz; the
ground was located at the right mastoid, the reference
was placed at the left mastoid. Furthermore, one EOG
channel was recorded at the left eye. The NF paradigms
were generated by using the software BioTrace + (Mind
Media BV, [38]). Up to ten NF training sessions were
carried out on different days three to five times per
week. Each session lasted approximately 45 minutes and
consisted of seven runs á three minutes each. The first
run was a baseline run, in which participants were
instructed to relax themselves and received no reward.
The subsequent six runs were feedback runs, in which
participants were instructed to try to modulate their
brain activity in the desired direction. Participants received
combined audio-visual feedback about their own brain
activity. The feedback display contained three moving
bars: One big bar in the middle and two smaller bars
on the left and right side of the feedback screen (see
Fig. 2b). During each three-minute feedback run the
bars were continuously moving in a vertical direction.
The height of the bar in the middle of the screen
reflected the absolute power of the trained EEG fre-
quency (12–15 Hz for SMR NF protocol, 2 Hz above
the individually defined Alpha peak for the Upper
Alpha NF protocol) in real time. Whenever the band
power reached an individually predefined threshold (mean
power value during the baseline run and the previous
feedback runs) during the feedback runs, the color of this
bar changed from red to green and participants were
rewarded by getting points, which were also displayed at
the feedback screen (reward counter). Furthermore, audi-
tory feedback was provided as a reward by means of a
midi tone feedback. When the bar in the middle of the
screen was below the threshold it turned red again, the re-
ward counter stopped and no tone was presented. Partici-
pants were instructed to try to voluntarily increase this
bar. The threshold for the middle bar was adapted after
each run. In order to prevent augmentation of the trained
EEG frequency by artifacts, such as movements or eye
blinks, two inhibit-bands were used, represented on the
screen by the two smaller vertical moving bars on the left
and right side of the display. The small bar on the left
side of the feedback screen indicated eye blinks or eye
movements. The height of the left bar reflected the ab-
solute power between 0.05-10 Hz of the EOG channel.
The small bar on the right side of the screen depicted
Table 2 List of neuropsychological tests assessing cognitive functions performed during the pre- and post-assessment
Cognitive
function
Neuropsychological test Analyzed test parameters
Attention Alertness Subtest Alertness of the TAP test battery [60] RT without sound, RT with sound
Divided Attention Subtest Divided Attention of the TAP test battery [60] RT auditory, RT visual, total errors, total omissions
Executive
functions
Cognitive flexibility Subtest Flexibility of the TAP test battery [60] RT, errors, total performance index
Inhibitory control Subtest Go/NoGo of the TAP test battery [60] RT, total errors, total omissions
Memory Long-term memory •CVLT [35]
•VVM2 [36] subscales “construction 2”and
“city map 2”
Short Delay Free Recall, Long Delay Free Recall,
Short Delay Cued Recall, Long Delay Cued Recall,
Learning Slope, List A Immediate Free Recall
Trial 1–5, Learning Efficiency (List A Trial 5)
Subtest “city map”(visuo-spatial memory),
Subtest “construction”(verbal memory)
Short-term memory •CBTT (subtest of the WMS-R) forward task [61]
•Digit Span test (subtest of the WMS-R) forward task [61]
•List A Trial 1 of CVLT [35]
•List B of CVLT [35]
•VVM2 [36] subscales “construction 1”and “city map 1”
Working Memory •CBTT backwards task [36,61]
•Digit Span test (subtest of the WMS-R)
backwards task [61]
CBTT Corsi Block Tapping Test, CVLT California Verbal Learning Test, RT reaction time, TAP Test of Attentional Performance, VVM Visual and Verbal Memory Test,
WMS Wechsler Memory Scale. Parallel forms of the memory tests were used to avoid learning effects
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muscle activity. The height of the right bar reflected
the absolute power between 75–100 Hz of the feedback
electrode [39, 40]. Artifact rejection thresholds were set
for each participant individually (mean of baseline run + 1
SD), suspending feedback when eye-movements or other
muscle activity caused gross EEG fluctuations. Hence,
participants were instructed to keep these two bars as
small as possible, but they were not told that they could
influencetheheightofthesebarsbymuscleactivityor
eye-movements. Participants were not rewarded when
these two controlling bars were above their respective
thresholds even when the trained EEG frequency was
above its individually defined threshold.
For the SMR NF protocol, participants had to increase
their SMR (12–15 Hz) activity recorded over Cz (accord-
ing to the international 10–20 EEG placement system),
since SMR is generally most pronounced over central
scalp regions over the sensorimotor cortex [41]. For the
Upper Alpha NF, we defined the individual Alpha fre-
quency (IAF) of each single participant. Therefore, par-
ticipants performed resting measurements with open
and closed eyes á two minutes before the start of the NF
training. These resting measurements were used to cal-
culate the EEG power spectrum for each participant.
EEG power spectra were calculated using Fast Fourier
Transformation (FFT). FFT was computed for the seg-
mented resting measurements (segment length 1 s)
with maximum resolution of ~0.98 Hz. Furthermore, a
10 % Hanning window was applied including a variance
correction to preserve overall power. Afterwards, peak
detection in the Alpha frequency range was performed
to identify the IAF. The Upper and the Lower Alpha
band were defined in the following way [25]:
Lower Alpha ¼lAF‐2HzðÞto lAF
Upper Alpha ¼lAF to lAF þ2HzðÞ
The individually defined Upper Alpha frequency was
used as feedback frequency for the Upper Alpha NF
protocol. Participants should try to increase the Upper
Alpha power recorded over Pz during NF training, since
alpha oscillations are generally most pronounced over
parieto-occipital areas [25].
Data analysis of EEG recordings was performed offline
using the Brain Vision Analyzer software (version 2.01,
Brain Products GmbH, Munich, Germany). Artifacts
(e.g. eye blinks/movements, muscle activity) were rejected
by means of a semi-automatic artifact rejection (criteria
for rejection: > 50.00 μV voltage step per sampling point,
absolute voltage value > ±100.00 μV). To analyze the feed-
back training data, absolute values of SMR (12–15 Hz)
and Upper Alpha (IAF to (IAF + 2 Hz)) power were calcu-
lated and averaged separately for each three-minute run of
each session using the Brain Vision Analyzer’s built-in
method of complex demodulation. The complex demodu-
lation is based on the complex (analytical) signal of a time
series, where all frequencies except the one of interest are
filtered out [42, 43].
Description of statistical analysis
In order to analyze the NF performance, we determined
the time course of the trained feedback frequency (either
Fig. 2 aNeurofeedback (NF) performance. Z-transformed EEG power for the two feedback frequency bands (SMR or UA) over the six NF training
runs, presented separately for the two patient groups and two control groups. Values were averaged over all repeated NF training sessions. The
regression lines for each group are indicated by finer lines. bFeedback screen. The amplitude of the relevant feedback frequency (either SMR or
UA) was represented by the bar in the middle of the screen. The two smaller bars on the left and on the right side of the screen represented the
inhibit bands (eye blinks or eye movements, muscle activity). The horizontal lines represented the individually defined thresholds for each bar
(for details see methods section). The counter at the bottom indicated the number of reward points accumulated during the feedback runs: it
increased whenever the middle bar was above and the left and right bars were below their individually defined thresholds
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SMR power or Upper Alpha power) averaged over the
ten NF training sessions across the six feedback runs.
Therefore, regression analyses were carried out separ-
ately for each group (predictor variable = feedback run
number; dependent variable = z-transformed power of
the feedback frequency). In addition, one-sample t-tests
were calculated for each group to verify the consistency
of the learning effects. For statistical analyses and better
comparability of the data between groups, SMR and Upper
Alpha power values were z-transformed. The probability of
a Type I error was maintained at 0.05. Holm corrections for
multiple comparisons were applied [44].
For statistical analysis, T-scores of the single neuro-
psychological test parameters were used. To investigate
the effects of NF on cognitive performance, we conducted
intra-individual comparisons between cognitive perform-
ance assessed during pre- and post-assessment by using
critical difference analysis [45, 46]. To establish the critical
difference for a pair of test scores, a correction for meas-
urement error based on the test-retest reliability of the test
is performed. The test-retest reliability is defined as the
variation in measurements taken by a single subject or in-
strument on the same task, under the same conditions,
and in a short period of time. It describes the consistency
and stability of a measure over time [47]. To identify sig-
nificant improvement or decline for each participant, the
critical difference of the relevant test parameter was com-
pared with the test score difference obtained during the
post-assessment minus the pre-assessment. A test param-
eter is considered significant when the difference between
pre- and post-assessment shown by the single participants
is larger than the critical difference, which can be detected
by each test and only occurs in the population with a
probability lower than α< 10 %. When the test-retest reli-
ability for a given psychological test is low (e.g. <.60), even
large test score differences between pre- and post-test can-
not be distinguished from random noise. In contrast,
when the test-retest reliability is very high (e.g. >.90), every
difference between pre- and post-test will be highly sig-
nificant though in many cases of no clinical relevance.
The test-retest reliability of the tests used in the present
investigation lay in a moderate to high range. Differences
in T-scores between pre- and post-assessment for each
cognitive test parameter were compared with critical
differences on the single subject level as well as on the
group level. Furthermore, we calculated the probability
that the number of significant performance improve-
ments and declines were observed by chance alone
using the binomial model. Given measurement inde-
pendency across participants and the probability of one
single participant reaching the critical difference of p=
0.01, each statistical comparison evaluated the proportion
of successes (performance differences between post- and
pre-assessment > critical differences) in relation to the
total number of comparisons. These probability values
were corrected for multiple comparisons using false dis-
covery rates [48].
Results
NF performance
Stroke patients as well as healthy controls were able to
voluntarily modulate brain rhythms during NF training
(Fig. 2a). This was reflected in a linear increase of power
in the target frequency band. For the SMR patient
group, regression analysis revealed linear changes of z-
transformedSMRpoweroverthesixtrainingruns
within the NF training sessions (F(1,5) = 6.37, p= 0.05).
The regression model explained 61 % of variance of
SMR power over the training runs. Eight out of eleven
patients (73 %) receiving SMR NF training were able to
linearlyincreasetheirSMRpoweroverthetraining
runs. One sample t-tests revealed that the individual
regression slopes of the SMR patient group differed
significantly from zero (t(10) = 2.38, p<0.05). For the
Upper Alpha patient group, the regression model was
significant (F(1,5) = 8.25, p< 0.05) and explained 67 %
of variance of Upper Alpha power over the training
runs. Four out of six (67 %) patients of the Upper
Alphagroupshowedapositiveslope,indicatingthat
they were able to linearly increase their individually
defined Upper Alpha power over the feedback runs
within the NF training sessions. One sample t-tests re-
vealed that the individual regression slopes of the Upper
Alpha patient group differed by trend significantly from
zero (t(5) = 2.15, p= 0.08). In sum, 12 out of 17 patients
(70 %) were able to linearly increase their EEG power. The
SMR CG also showed a significant linear increase in SMR
power over the NF training runs (F(1,5) = 14.58, p<0.05).
The regression model explained 78 % of variance of SMR
power over the training runs and 11 out of 16 participants
(69 %) showed a positive slope (t(15) = 2.08, p= 0.05). For
the UA control group the regression model was also sig-
nificant (F(1,5) = 45.73, p< 0.01) and explained 92 % of
variance of Upper Alpha power over the training runs.
Nineteen out of 24 participants (79 %) of the UA CG
group showed a positive slope over the runs (t(23) = 1.80,
p= 0.08). A repeated measures ANOVA with the between
subject factor group revealed no differences in the regres-
sion slopes between groups (F(1,3) = 0.64, ns.). There were
no significant changes in SMR or UA power across the
feedback training sessions neither in the patient nor in the
control groups.
Cognitive performance –comparison between pre- and
post-assessment –group level
After NF training, the SMR patient group showed sig-
nificant performance improvements in parameters of the
CVLT assessing verbal short- (“List B”) and long-term
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107 Page 7 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

memory (all CVLT test parameters) compared to the
pre-assessment (Fig. 3). Furthermore, SMR patients
showed a numerical performance improvement in visual-
spatial short-term memory (VVM2 subscale “city map 1”),
which slightly failed to reach the significance level. Com-
parable to the results of the SMR patient group, the UA
patient group showed significant performance improve-
ments in the CVLT parameters assessing verbal long-term
memory, except for the test parameter “Learning slope”
(Fig. 3). UA patients also improved their verbal short-term
memory performance (CVLT “List A”)aswellastheir
memory span (Digit Span forwards task), which was sig-
nificant by trend. Additionally, the UA patients showed a
numerical improvement in the CBTT backwards task
assessing WM, although the difference between pre- and
post-test T-scores was marginally lower than the critical
difference. The TAU patient group showed the least cogni-
tive improvements of all patient groups after cognitive
training compared to the pre-assessment. Performance
improvements could be observed in two parameters of the
long-term memory tasks: “short delay free recall”and
“learning efficiency”.
Healthy controls showed cognitive improvements due
to NF training as well, which were comparable to cogni-
tive improvements of stroke patients. Comparable to the
results of the SMR patient group, the SMR CG also
showed significant performance improvements in verbal
short- (CVLT List B) and long-term memory (CVLT
short and long delay free recall and learning slope) as
well as in visual-spatial short- (VVM2 city map 1) and
long-term memory (VVM2 city map 2) when comparing
the post- and pre-assessment (Fig. 3). Furthermore, the
SMR CG improved in WM performance (CBTT back-
wards task). This is the only NF training group that also
showed decreases in cognitive performance on the group
level when comparing pre- and post-assessment. After
the NF training, the SMR CG showed a lower perform-
ance in the CVLT parameter “List A”assessing short-term
memory performance compared to the pre-test, although
the T-score of 49.81 reached by the SMR CG during the
post-assessment is still in the normal range. The Upper
Alpha CG showed the fewest significant performance
improvements when comparing the pre- and post-
assessment (Fig. 3). Note that this CG already showed the
highest cognitive performance during the pre-assessment
compared to the other groups. After NF training, the
Upper Alpha CG significantly improved its performance
in the CVLT parameter “long delay cued recall”assessing
long-term memory performance compared to the pre-test.
AstheUApatientgroup,theUACGgroupshoweda
numerical improvement in the CBTT backwards task
assessing WM when comparing pre- and post-assessment,
although this difference between pre- and post-test T-
scores was marginally lower than the critical difference.
Cognitive performance –comparison between pre- and
post-assessment –single subject level
For each group, we determined the number of participants
showing significantly increased, constant or decreased
cognitive performance by counting the number of pre-
post differences scores larger than the test specific critical
differences and dividing this amount by the number of
Fig. 3 Test performance is expressed in T-scores with population mean M= 50 and standard deviation SD = 10. Group average test scores and
standard errors for measurements of attention, executive functions, short- and long-term memory, and working memory (WM) performed during
the pre- and post-assessment are depicted separately for stroke patients and healthy controls. Significant differences between pre- and post-test
(critical difference analysis on the group level, [45, 46]) are marked with asterisks (*significant,
+
marginally significant). CBTT, Corsi Block Tapping
Test; CVLT, California Verbal Learning Test; VVM, Visual and Verbal Memory Test
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107 Page 8 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

measurements per construct and participants [45, 46]. All
groups showed the strongest performance improvements
in memory tests (Fig. 4). Cognitive decline was present
after training but never markedly over 20 % and therefore
attributable to random performance fluctuations and not
to any deleterious NF training effects (Fig. 4b) [45, 46].
Importantly, cognitive decline was comparable across
groups. Statistical comparisons using chi-square tests
revealed that the number of participants showing in-
creased, constant or decreased cognitive performance
was comparable between groups (all p-values > 0.10).
SMR and UA NF training led to comparable individual
improvements and decline in cognitive performance.
The TAU group showed the lowest percentage of cognitive
improvement in short- and long-term memory tasks com-
pared to the NF training groups.
The probability that the numbers of significant per-
formance improvements and declines were observed by
chance alone is depicted in Table 3. After correction for
multiple comparisons using false discovery rates [48],
substantial improvements in measurements of short-
term memory and long-term memory could be detected
among patients and controls who performed NF train-
ing. Healthy controls also showed significant improve-
ments in working memory. The probability that the
observed performance improvements of the NF training
groups were attributable to random noise alone was ra-
ther low, as indicated by the p-values shown in Table 3.
The TAU group showed no significant improvements
any more. No significant decrease in performance could
be observed in any of the patient groups. The UA CG
showed significant performance declines in short- and
long-term memory tasks. Here, we would like to note
that four participants of the UA CG were responsible
for this effect. Theses participants showed performance
declines in most of the cognitive constructs, regardless
of their functional connection with UA rhythm. In our
view these results are more easily explained in terms of
a general decrease in motivation in these four participants,
rather than representing genuine deleterious effects of
UA NF.
Discussion
The aim of the present study was twofold. First, we in-
vestigated whether stroke patients were able to learn to
modulate their own EEG activity by means of NF train-
ing. Second, we evaluated the effects of two different
NF training protocols on cognition, especially memory,
in stroke patients to demonstrate its feasibility and use-
fulness as cognitive rehabilitation tool.
In a first step, we could show that stroke patients were
able to voluntarily increase their EEG activity within the
NF training sessions in the trained frequency range (ei-
ther SMR or UA). NF performance of stroke patients
and healthy controls was comparable. All groups showed
a linear increase in the trained frequency band over the
feedback runs, indicating successful NF training [9]. In
line with previous findings in healthy participants, about
30 % of patients were not able to modulate their EEG ac-
tivity [49]. The inability to control the own brain activity
may be attributed to different factors such as differences
in brain structure [49], inter-individual differences in
neurophysiological and psychological factors, or cognitive
strategies [38, 50, 51]. Furthermore, the type and
localization of brain lesion might explain that part of
stroke patients were not abletomodulatetheirbrain
activity. However, there was no clear relationship between
lesion location and the ability to up-regulate SMR or UA
Fig. 4 Individual improvements and declines in cognitive performance after training, presented separately for stroke patients and healthy
controls. Percentage of participants per group showing either increased (a) or decreased (b) performance in the different cognitive constructs
(short-term STM, long-term LTM, and working memory WM) when comparing the pre- and post-assessment
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107 Page 9 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

power in our study (see Table 1). For instance, some
patients were able to increase the feedback frequencies
(e.g. patient code 6 and 16), while others were not (e.g.
patient code 10 and 15), although they showed compar-
able lesion locations (e.g. arteria cerebri media). Prior
neuroimaging studies revealed a network of different
brain regions, which seemed to be involved in more
general cognitive regulatory mechanisms that are engaged
during NF. This cognitive control network included the
insula, anterior cingulate cortex, supplementary motor
and dorsomedial and lateral prefrontal areas [52, 53].
Based on the present vague description of lesion locations,
it is difficult to determine whether stroke patients of the
present study had lesions in this network or not. For in-
stance, a lesion in the arteria cerebri media can affect
many different brain areas. Nevertheless, patients of the
present study showed no lesions in the arteria cerebri an-
terior. This implies that the anterior cingulate cortex
should have been intact, which is part of this cognitive
control network involved in NF [52, 53]. Another study by
Birbaumer et al. (2013) postulated that the basal ganglia
would play a critical role in NF [54]. However, we could
not support this assumption since we had two patients
with lesions in the basal ganglia, one was able to increase
SMR power (patient code 11) while the other was not (pa-
tient code 3). Future studies with an exact determination
of the brain lesion are necessary to draw a meaningful
conclusion concerning the relationship between lesion
location and ability to control EEG rhythms by means
of NF training.
We did not find any consistent changes in SMR or UA
power across the NF training sessions. This is in line
with prior studies in healthy people [9, 12, 55, 56], which
also found robust modulations of EEG power values within
sessions but not across sessions. Hence, our results provide
evidence that stroke patients and healthy controls were able
to adaptively modulate SMR or UA activity according to
task demands at a given time (within a NF training session),
which does not necessarily imply that the average SMR/UA
baseline level of a feedback user has to change [9]. The abil-
ity to upregulate SMR/UA power voluntarily at a given time
might also explain the improvements in cognitive perform-
ance after NF training compared to the pre-test. In a recent
study, Kober et al. (2015) also found significant changes in
SMR power within NF training sessions but not across NF
training sessions [9]. After NF training, healthy participants
showed significant correlations between SMR power and
event-related potentials (P300, N1) in the EEG, which
are generally indicators for stimulus processing intensity,
during the performance of a memory task. A higher SMR
power during the memory task was associated with a more
intense stimulus processing and consequently with an im-
proved memory performance after NF training compared
to the pre-test [9]. Hence, although no changes in absolute
SMR power across NF training sessions could be ob-
served, these prior findings indicate that participants
can learn to voluntarily increase SMR power at a given
time, for instance during performing a memory test,
which goes along with improved cognitive performance.
In the present study, it was not possible to perform
multi-channel EEG measurements in stroke patients at
the clinic during the pre- and post-assessment. Future
studies are needed to investigate whether stroke patients
are also able to transfer the mental state reached during
successful NF training to other situations without real-
time feedback of own brain activity.
Table 3 Probability that the number of successes (performance differences between post- and pre-assessment > critical differences)
is due to chance alone given a probability of success for each individual comparison of p= 0.01 and the total number of comparisons
performed in each group.
(A) Probability that chance alone is responsible for cognitive improvements after training and total number of comparisons performed in each group
Alertness Divided Attention Cogn. Flexibility Inhibition WM STM LTM
SMR patients 0.38 (22) 0.65 (44) 0.42 (33) 0.86 (33) 0.17 (22) 1.64 × 10
−5
(66)* 2.66 × 10
−15
(99)*
UA patients 0.72 (12) 0.21 (24) 0.27 (18) 0.55 (18) 0.03 (12) 1.25 × 10
−4
(36)* 1.11 × 10
−12
(54)*
TAU patients 0.42 (14) 0.95 (28) 0.64 (21) 0.04 (14) 0.06 (28) 0.02 (35)
SMR CG 0.97 (32) 0.89 (64) 0.87 (48) 0.99 (48) 0.003 (32)* 3.50 × 10
−8
(96)* 3.55 × 10
−15
(144)*
UA CG 0.20 (48) 0.05 (96) 0.18 (72) 0.86 (72) 4.35 × 10
−5
(48)* 3.12 × 10
−11
(144)* <1 × 10
−35
(216)*
(B) Probability that chance alone is responsible for cognitive declines after training and total number of comparisons performed in each group
SMR patients 0.66 (22) 0.27 (44) 1.00 (33) 0.23 (33) 0.90 (22) 0.002 (66) 0.94 (99)
UA patients 0.34 (12) 0.92 (24) 0.85 (18) 0.85 (18) 0.72 (12) 0.06 (36) 0.04 (54)
TAU patients 0.77 (14) 0.79 (28) 0.89 (21) 0.77 (14) 0.14 (28) 0.69 (35)
SMR CG 0.84 (32) 0.96 (64) 1.00 (48) 0.87 (48) 0.40 (32) 0.05 (96) 0.37 (144)
UA CG 0.35 (48) 0.36 (96) 0.99 (72) 0.99 (72) 0.53 (48) 0.0005 (144)* <1 × 10
−35
(216)*
Probability that chance alone is responsible for improvements (A) and declines (B) in cognitive performance after training, presented separately for stroke patients
and healthy controls for the different cognitive constructs (short-term STM, long-term LTM, and working memory WM). Uncorrected p-values, which were significant after
correction for multiple comparisons employing false discovery rates [48], are marked with asterisks.
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107 Page 10 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

In a second step, we investigated the NF training out-
come. Independent of the used NF protocol, stroke patients
and healthy controls showed more general improvements
in verbal short- and long-term memory performance after
NF training supporting prior findings in healthy people
[7–12, 18–24] and post-stroke victims [28–31, 33]. An
explanation for these comparable effects of SMR and
UA NF on verbal memory may be that both EEG frequen-
cies are associated with improved stimulus processing,
which should foster the storage and retrieval of informa-
tion and might explain the overlapping effects of SMR and
UA NF [9, 15, 26]. Furthermore, the SMR and UA fre-
quency range may overlap in some cases. Participants who
received SMR NF and at the same time showed a high in-
dividual Alpha frequency (e.g. 12 Hz, resulting in an UA
frequency range of 12–14 Hz, [25]) might be actually
modifying their power in the UA range. Considering the
importance of UA for cognitive performance, one might
assume that at least part of the effects found for SMR
based NF training might be due to the influence of UA
activity [57].
Specific effects of SMR and UA NF training on mem-
ory functions were observed as well. The SMR patient
group showed additional and specific improvements in
visuo-spatial short-term memory performance. Prior SMR
based NF studies also found performance improvements
in visual-spatial tasks, such as mental rotation tasks [40].
However, the majority of prior SMR NF studies reported
on specific improvements in verbal memory tasks in
young adults [8–12]. In contrast, our results in older
stroke patients and older healthy controls indicate that
SMR NF seems to have specific positive effects on visuo-
spatial memory, including short- and long-term memory
as well as WM (improvement in CBTT backwards task in
the SMR CG). Furthermore, SMR patients showed the
strongest performance improvements in their learning ef-
ficiency, which is a measure of memory consolidation,
probably due to a more intense stimulus processing after
SMR based NF [9, 15, 35].
The UA patient group showed additional and specific
improvements in WM performance. Prior NF studies that
found positive effects of NF training on WM performance
also trained EEG frequencies in the Alpha range such as
the total Alpha band (about 8–12 Hz), Upper Alpha
(about 10–12 Hz), or the Alpha peak frequency [22, 23].
Furthermore, in line with previous findings in healthy
people, the UA patient group showed strong performance
improvements in verbal short-term memory and memory
capacity after NF training [12, 18, 20, 21, 23].
NF training had no effects on other cognitive functions.
The majority of prior NF studies also linked SMR and UA
to memory functions, whereas performance improvements
in other cognitive functions such as attention, inhibitory
control, or cognitive flexibility were mainly found in T/B
based NF training studies [7, 8, 33, 58, 59]. In the NF litera-
ture, there are issues concerning specificity of NF training
[8]. Our results indicate that there is specificity and inde-
pendence regarding cognitive outcome such that perform-
ance enhancement in memory functions is specific to
changes in the SMR and UA frequency band while leaving
other cognitive functions unchanged.
NF training was more efficient than traditional cognitive
rehabilitation. The TAU group also showed improvements
in long-term memory functions after traditional cognitive
training compared to the pre-assessment, which supports
prior findings [5]. However, these memory improvements
were not as strong as in the NF patient groups. After cor-
rection for multiple comparisons, the TAU group showed
no significant performance improvements any more.
Based on these results, one may exclude that the NF train-
ing effects can be merely attributed to placebo-effects or
spontaneous cognitive improvements. NF training might
be more efficient than traditional cognitive training be-
cause with NF training one can directly modulate brain
activation patterns that underlie cognitive functions. This
underlines the potential usefulness of NF training as cog-
nitive rehabilitation tool.
After NF training, all groups showed a small decrease
in cognitive performance compared to the pre-test. How-
ever, cognitive decline due to NF training was insignificant
and did not differ between stroke patients and healthy con-
trols. Therefore, one may conclude that SMR and UA NF
do not have deleterious effects on the electrophysiological
balance or associated cognitive functions [2]. Reasons for
this cognitive decline after NF training, e.g. changes in mo-
tivation, mood or fatigue, have to be investigated in more
detail in future studies. In the present study, all stroke pa-
tients showed more or less circumscribed memory deficits,
but no severe deficits in further cognitive functions. Hence,
we could demonstrate that NF is a tool to treat moderate
memory deficits in post-stroke victims.
The patient inclusion and exclusion criteria used in
the present study determine the limits of generalization
of the present findings to other patient populations. For
instance, the effects of NF on stroke patients with aphasia
have to be clarified in future studies. All stroke patients
showed memory deficits, but no severe deficits in other
cognitive functions. As our patient population differed in
brain lesion sites, we cannot exclude potential side-effects
of the individual lesions on learning. This issue clearly
calls for future studies that incorporate detailed ana-
tomical data. Further limitations of the present study
are the small sample size and the heterogeneity of pa-
tients concerning post-stroke delays and cause of brain
lesion. Additionally, no follow-up measurements were
performed. Hence, we cannot draw any conclusion con-
cerning long-term effects of NF training at this stage of
research.
Kober et al. Journal of NeuroEngineering and Rehabilitation (2015) 12:107 Page 11 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Conclusions
In this proof-of-principle study, we could show that
post-stroke victims were as successful in modulating
their own brain activity by means of NF training as
healthy controls. Furthermore, SMR and UA based NF
training had specific positive effects on memory func-
tions in stroke patients and healthy controls. NF training
effects were even stronger than effects of traditional cog-
nitive rehabilitation methods in stroke patients. Hence,
compared to prior single-case studies in stroke patients
[28–32], we could (i) demonstrate the specificity of dif-
ferent NF training protocols in a larger sample of stroke
patients, (ii) compared NF related training effects in stroke
patients to the effects of treatment as usual in stroke sur-
vivors, (iii) used a comprehensive neuropsychological test
battery to assess possible effects of NF on many different
cognitive functions, and (iv) reported on possible negative
effects of NF training for the first time. The NF training
protocols were feasible for stroke patients with memory
deficits and may represent a new rehabilitation strategy
suitable to overcome some of the usual pitfalls of trad-
itional cognitive rehabilitation. Hence, NF seems to be an
alternative, innovative and easy-to-use cognitive rehabilita-
tion tool since the electrical activity of the brain is affected
directly and, therefore, the cortical substrates of cognitive
functions themselves [6].
Abbreviations
Bt: bilateral; CBTT: corsi block tapping test; CG: Control group; CVLT: California
Verbal Learning Test; EEG: electroencephalogram; F: female; FFT: fast fourier
transformation; IAF: individual alpha frequency; Lt: left; LTM: long-term
memory; M: male; MMSE: mini-mental state examination; NF: neurofeedback;
RT: reaction time; Rt: right; SD: standard deviation; SE: standard error;
SMR: sensorimotor rhythm; STM: short-term memory; TAP: test of attentional
performance; TAU: treatment as usual; T/B: Theta/Beta; TBI: traumatic brain
injury; UA: upper alpha; VVM: visual and verbal memory test; WM: working
memory; WMS: Wechsler Memory Scale.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
SEK conceived of the study and made substantial contributions to
conception and design of the study, performed the statistical analysis, made
the analysis and interpretation of data and drafted the manuscript. DS
participated in the design of the study, performed statistical analysis and
collected the data. MW participated in the design of the study and helped
to draft the manuscript. JLR participated in the design of the study,
performed statistical analysis and collected the data. PG participated in the
selection and medical care of the neurologic patients, coordination, data
collection and interpretation. CN participated in the design of the study and
interpretation of data and revised the manuscript critically for important
intellectual content. GW participated in the design of the study, has been
involved in the interpretation of data, drafting the manuscript and revising it
critically for important intellectual content. All authors read and approved
the final manuscript.
Acknowledgment
This work was partially supported by the European Community’s Seventh-
Framework-Programme FP7/2007-2013, CONTRAST project, under grant
agreement nr. 287320. Possible inaccuracies of information are under the
responsibility of the project team. The text reflects solely the views of its
authors. The European Commission is not liable for any use that may be
made of the information contained therein. The authors are grateful to
Alexandra Pongratz, Aida Mujkanovic, Margit Krenn, Katharina Farveleder,
Julia Schobel and Jasmin Wiesler for data acquisition.
Author details
1
Department of Psychology, University of Graz, Universitaetsplatz 2/III, Graz
8010, Austria.
2
BioTechMed-Graz, Graz, Austria.
3
Klinik Judendorf-Strassengel,
Gratwein-Strassengel, Austria.
4
Laboratory of Brain-Computer Interfaces,
Institute for Knowledge Discovery, Graz University of Technology, Graz,
Austria.
Received: 6 July 2015 Accepted: 24 November 2015
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