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Infra-Low Frequency Neuro Feedback Modulates Infra-Slow Oscillations of Brain
Potentials: A Controlled Study
Vera A Grin-Yatsenko1*, Olga Kara², Sergey A Evdokimov1, Mark Gregory3, Siegfried Othmer4 and Juri D Kro-
potov1,5
1Laboratory of Neurobiology of Action Programming, N.P. Bechtereva Institute of the Human Brain of the Russian Academy of
Sciences, St Petersburg, Russian Federation
²Brain Fitness (BFC), Tampere, Finland
3BEE Medic GmbH, Stuttgart Area, Germany
4e EEG Institute, Woodland Hills, California, USA
5Department of Neuropsychology, Andrzej Frycz Modrzewski Krakow University, Krakow, Poland
Journal of
Biomedical Engineering and Research
Received Date: May 19, 2020 Accepted Date: June 07, 2020 Published Date: June 10, 2020
Citation: Vera A Grin-Yatsenko (2020) Infra-Low Frequency Neuro Feedback Modulates Infra-Slow Oscillations of Brain Po-
tentials: A Controlled Study. J Biomed Eng 4: 1-11.
*Corresponding authors: Vera A. Grin-Yatsenko, MD, Ph.D., Laboratory of Neurobiology of Action Programming, N. P. Bech-
tereva Institute of the Human Brain of the Russian Academy of Sciences, ul. Academica Pavlova 12a, 197376 St Petersburg,
Russian Federation, Tel: +7-9219497590; Email: veragrin.ihb@gmail.com
©2020 e Authors. Published by the JScholar under the terms of the Crea-
tive Commons Attribution License http://creativecommons.org/licenses/
by/3.0/, which permits unrestricted use, provided the original author and
source are credited.
J Biomed Eng Res 2020 | Vol 4: 104
Abstract
A formal comparison of Infra-Low Frequency Neurofeedback with an active control condition, Heart Rate Vari-
ability training, is undertaken in the present research. 17 participants 21-50 years of age with no history of neurological or
psychiatric diseases conditions, but reporting about some physiological or psychological complaints were involved in the
study. Participant progress was monitored by means of Visual Go/NoGo test performance and spectral power of slow EEG
oscillations during the test before and aer twenty sessions of training. Outcomes favored Infra-Low Frequency Neurofeed-
back training over Heart Rate Variability training with respect to health status and Visual Go/NoGo test results. Signicant
elevation in amplitudes in the Infra-Low Frequency range was observed only for the Neurofeedback cohort.
Keywords: Neurofeedback; Electroencephalogram; Infra-Slow EEG Oscillations; Heart Rate Variability; Infra-Low Frequen-
cy Training
Research Open Access
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Introduction
Infra-slow rhythmic potential oscillations were rst
studied extensively in animals by the Russian neuroscientist
Nina A. Aladjalova [1]. is work rst became accessible to Eng-
lish language audiences through her book titled “Slow Electrical
Processes in the Brain” [2]. Western neuroscience had gone dark
on the tonic Slow Cortical Potential (SCP), dating from the time
in the late thirties when tube ampliers required AC-coupling
for extraction of the EEG signal. When interest was revived in
the sixties, it was with an exclusive emphasis on the phasic SCP
(Contingent Negative Variation, Bereitschaspotenzial). Inter-
est in the tonic SCP only revived once researchers took an inter-
est in the slow uctuations revealed in the blood-oxygen-level-
dependent (BOLD) signals of functional magnetic resonance
(fMRI) [3, 4]. e hemodynamic response of the BOLD signal
limits the accessible bandwidth to <0.1 Hz. Recently published
full-band EEG (EEG) recordings raise awareness of the value
of previously neglected infra-slow oscillations (below 0.5 Hz) in
neuroscience research [5].
Despite decades of residing in scientic backwaters,
infra-low frequency (ILF) phenomenology was extensively stud-
ied by some distinguished scientists [6-10]. e substantial role
of ILF in the spatiotemporal dynamic organization of the brain
had been documented long before the rst publications on the
signicance of the ILF surface potential in revealing the internal
regulation of the Default Mode Network [11].
ILF potentials identied in dierent electrophysiologi-
cal settings are obtained by surface electrodes or by electrodes
implanted into various cortical and subcortical structures. ILF
revealed in in vivo studies shows correlated behavior when re-
corded simultaneously viaEEG [12, 13] and fMRI [14, 15].
e close relation between ILF, BOLD, and the Mono-
chromatic Ultra Slow Oscillation (MUSO) identied in EEG
recordings in the vicinity of 0.1 Hz suggests a relation between
these signals and cerebral vasomotor uctuations [16]. is as-
sumption was supported when researchers were able to detect
causal coupling between low-frequency EEG oscillations and
well-known Mayer's waves observed in the blood pressure, heart
rate variability, and blood oxygenation level distributed around
0.05-0.15 Hz with a peak at 0.1 Hz frequency range [17-19].
Extensive research revealed a signicant role of the ILF
regime in regulating cortical excitability [20]. e ILF regime
dominates the dynamics of large-scale brain organization [21-
23]; and it reects interactions between distinct functional net-
works throughout the performance of cognitive tasks [11].
Deviations from expected ILF potential distributions
have been identied for ADHD, Schizophrenia, Autism, Depres-
sion, Anxiety, and other brain disorders [24-29]. ese data sup-
port the hypothesis of impaired ability to preserve an optimal
level of cortical activation in psychiatric disorders [30, 31] and
are in line with the assumption of diminished exibility and the
capacity to recruit functional networks during action prepara-
tion and performance [32].
Brain activity can be modulated in real-time by way
of neurofeedback, a relatively passive brain-computer interface
approach that allows the trainee to alter brain function through
feedback on brain potentials derived from surface electrodes and
presented in the form of visual, auditory or tactile signal streams.
In ILF neurofeedback the primary driver is feedback on the sur-
face potential below 0.1Hz [33, 34]. It is called ILF neurofeed-
back (or ILF training) because the process was found to be highly
frequency-specic.
ILF training was developed by Susan and Siegfried
Othmer in 2006, as described in a paper titled Clinical Neuro-
feedback: Training Brain Behavior [35]. e method has dem-
onstrated positive outcomes for a variety of mental conditions,
including Post-Traumatic Stress Disorder among military veter-
ans [36], dierent forms of anxiety, depression [37], sleep dis-
turbances, ADHD, the Autism spectrum, developmental trauma,
migraines, and other headaches, epilepsy [33] and traumatic
brain injury [38,39].
ILF training can yield surprisingly substantial improve-
ments in a variety of clinical conditions in a reasonable number
of sessions (20-40). e tonic slow cortical potential appears to
be a direct reection of the dynamics of cortical excitability. With
a signal derived from bipolar montage, network relations are re-
vealed, and thus the training impinges directly on dynamic func-
tional connectivity, in addition to calming hyper-excitability di-
rectly. By operating in the ILF regime, the training preferentially
accesses the functional connectivity of the intrinsic connectivity
networks that were originally identied in fMRI [40].
ILF training also impinges on the ultradian cyclic uc-
tuation of physiological arousal, as well as autonomic nervous
system regulation [10, 41]. e data of Smith and colleagues,
employing an adaptation of the method used here, supports the
hypothesis that ILF training preferentially inuences autonom-
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ic nervous system regulation and thus improves the emotional
equilibrium of patients, which in turn positively inuences atten-
tion and working memory [39]. Further evidence with respect to
vigilance and attention was recently documented in a large-scale
compilation of pre- and post-training continuous performance
test data on a clinical population [42]. Clinically signicant im-
provement in performance was consistently observed among
those in decit, largely irrespective of the clinical condition be-
ing targeted in the training.
Heart rate variability biofeedback (HRV) is another
method widely used to improve stress tolerance and quality of
sleep, as well as to reduce anxiety, depression, and other symp-
toms. HRV is a complex signal that involves a mixture of super-
imposed oscillations, triggered by reexes and modulated by
autonomic pathways [43-47]. e HRV signal can be segregat-
ed into two main frequency bands. e low-frequency domain
(0.01-0.15 Hz) is governed by sympathetic activation (vasomo-
tor and baroreceptor origin) and the high-frequency domain
(0.15-0.45 Hz) reects parasympathetic or vagal inuence (cor-
responding to respiration) [48].
e autonomic nervous system (ANS) balance reects
the organismic ability to adapt to internal and external stressors,
and HRV is a good biomarker to assess autonomic balance. El-
evated stress causes an increase in heart rate and decreased HRV
(augmented sympathetic load, whereas increased HRV is associ-
ated with decreased heart rate and augmented parasympathetic
load (decreased stress reaction).
Deviations in HRV patterns have been reported for pa-
tients with anxiety disorder, depression, and insomnia [49-51].
For these disorders, deciency in autonomic regulation has also
been reported [52]. HRV biofeedback is based on the observa-
tion that parasympathetic activation can be modulated by simple
breathing techniques, with breathing frequency optimally in the
vicinity of 0.1 Hz. Improved autonomic balance is reected in
higher amplitude heart rate oscillations centered on the breath-
ing frequency.
e present study design included both ILF neuro-
feedback training (ILF NF) and HRV biofeedback training for
purposes of comparison. Both techniques generally improve
autonomic regulation. However, since biofeedback is cognitive-
ly mediated, it is subject to greater ambiguity with respect to-
specic eects on ANS balance. Moreover, it impinges on brain
functional organization only indirectly [48].
e goal of the present study is to compare the eects of
these two biofeedback approaches on ILF spectral components
in order to demonstrate that the ILF training procedure induces
persistent changes in the amplitude distribution within the ILF
spectral range and to document improved outcomes with respect
to the control group undergoing HRV biofeedback training.
We postulated that HRV biofeedback at Mayer's wave
frequency would be a reliable control in order to clarify whether
the spectral parameters in the ILF regime are substantially al-
tered during ILF NF, or, alternatively, the eects of training are
predominantly seen in the autonomic nervous system. In the lat-
ter case, HRV and ILF NF would be expected to yield similar
outcomes.
Materials and Methods
Participants
Seventeen healthy individuals voluntarily participated
in this study. With randomization, nine were assigned to the ILF
NF (main) group (6 males and 3 females, ages 21-50 years, mean
33.1 years), and eight were assigned to the HRV BF (control)
group (3 males and 5 females, ages 23-49 years, mean 35.9 years).
Subjects were recruited through the St. Petersburg State Univer-
sity’s and Pavlov First St. Petersburg State Medical University’s
participant pool, scientific sta of the Institute of the Human
Brain of the Russian Academy of Sciences, and by word-of-
mouth.
In order to qualify for the study, participants were re-
quired to complete self-evaluation forms documenting physi-
ological, functional, and psychological parameters of health, and
anamnesis of vitae. Participants were excluded if they had major
chronic health problems; complicated perinatal period; abnor-
mal mental and/or physical development; head injury with cer-
ebral symptoms; the history of convulsions; neurologic or psy-
chiatric diseases; current medication or drugs. Despite the fact
that none of the participants had any medical diagnosis, some
of them reported mental or physical complaints, e.g. symptoms
of anxiety, episodes of depressive mood, mood swings, high re-
activity to stress factors, fatigue, headaches, and sleep problems.
Several participants were not satised with their memory func-
tion and/or concentration.
No specialized instruments were used for the assess-
ment of physiological and psychological parameters in this study.
First of all, the complaints were heterogeneous and minimal in
most participants, and secondly, the clinical eects of the ILF NF
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were subsidiary to the inuence of ILF NF on the infra-slow fre-
quency potentials for purposes of this study. e research was
conducted in accordance with the Declaration of Helsinki. All
participants gave informed consent aer a detailed explanation
of the procedure.
e baseline investigation was performed 1-7 days
before starting the course of training sessions and consisted of
quantitative electroencephalogram (qEEG) recording while un-
der a mild cognitive challenge of a continuous performance test.
qEEG parameters were analyzed by comparison against the Hu-
man Brain Institute (HBI) normative database. ere were no
signicant dierences between groups in any variable. e test
was repeated aer 20 sessions in 1-7 days aer the last session.
e results of the second test were statistically compared with the
pre-treatment baseline.
EEG investigation
e electroencephalogram was recorded with the Mit-
sar 21-channel EEG system (Mitsar, Ltd). 19 silver-chloride
electrodes were applied using Ten20 conductive electrode paste
according to the International 10-20 system. e input signals
referenced to linked ears were digitized at a rate of 250Hz, band-
pass ltered at 0.01–50 Hz, and notch ltered at 50 Hz. e
ground electrode was located on the forehead. All electrode im-
pedances were maintained below 5k Ohm. EEG was recorded
during the performance of the visual cued GO/NOGO task [53].
Quantitative data were obtained using WinEEG soware.
Epochs with an extreme amplitude of un-ltered EEG
and/or excessive slow and high-frequency activity were auto-
matically excluded from further analysis. Eyeblink artifacts were
corrected by setting to zero the activation curves corresponding
to eye blinks. e method is consistent with the one described in
Vigario [54] and in Jung et al. [55] Continuous artifact-free EEG
epochs were selected for analysis manually. Epochs duration var-
ied among the subjects from 550 to 1100 seconds. e omson’s
multi-taper method was used to determine the average spectral
density in the 0.01 - 0.5 Hz frequency band for each electrode
site, for each subject and for each condition separately. Data were
logarithmically transformed prior to statistical analysis.
Neurofeedback
For ILF NF we used the Cygnet system (BEE Medic),
which consisted of the NeuroAmp II amplier and Cygnet so-
ware, integrated with Somatic Vision video feedback installed on
a standard personal computer (PC) running a Windows 7 oper-
ating system with a high-resolution monitor.
For each of the nine NF group subjects, the optimal reinforce-
ment frequency (ORF) was determined at the beginning of each
neurofeedback session, based on the trainee's subjective report.
Participants in the NF group were instructed to observe their
physiological state, their level of vigilance and alertness, and
their emotional ambient while watching a videogame on the
monitor and to report to the therapist about felt changes in their
state. e training was started with bipolar placement at one or
both of two initial placements, T4-T3 and P4-T4 (according to
the International 10-20 system), to observe the trainee's response
and, on that basis, to guide further optimization. Later, T4-Fp2
and T3-Fp1 electrode locations were added to the protocol as
needed. is choice of electrode placement and their sequencing
has been standard in the Othmer Method for ILF NF for some
years [56]. Each participant received 20 separate 30- to 45-min-
ute neurofeedback sessions over a period of 7–8 weeks.
HRV biofeedback
HRV BF for 8 participants of the control group was
provided by the emWave PC Stress Relief System designed by
the HeartMath Institute. e plethysmograph placed on the
participant’s earlobe furnishes the information on his/her heart
rhythms to a soware program that provided real-time feedback
on their performance. ey were instructed to use deep, abdom-
inal, or diaphragmatic breathing, approximately 5-7 complete
breath cycles over the course of 1 min, following the techniques
learned with the emWave program. e graphics displayed on
the computer monitor depicted the HRV power spectrum. ree
bars of varying colors reected coherence, corresponding to low,
medium, or high coherence levels. Participants practiced achiev-
ing a higher level of coherence relative to the basic level, an ob-
jective usually met without diculty.
Sessions were 30 min in length, and they occurred 2-3
times per week. Each subject received 20 separate HRV BF ses-
sions over a period of 7–8 weeks.
Statistical analysis
One-way ANOVA with factor condition (before – aer)
for all locations (19 electrode positions) was used to ascertain the
statistical signicance of the training eect on the slow oscilla-
tions for each group. e same ANOVA test was used for the dif-
ference of logarithmical power of slow EEG oscillations with the
factor (main group-controls) to assess the statistical signicance
of changes aer the training course between the two groups. To
quantify the eect size of dierences of EEG spectral power Co-
hen’s d was computed for each electrode position separately. A
Cohen’s d of 0.6 was adopted as the threshold of signicance.
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Results
Aer completion of 20 training sessions, all nine par-
ticipants of the ILF NF group indicated improvement in their
health status. Most of them reported a decrease of reactivity to
stressful factors and release of inner tension, improved body, and
spatial awareness, and mood stability. Also, they noticed an in-
crease in energy level and better cognitive performance.
Six of eight participants of the HRV NF group also
reported positive changes in their health status aer nishing
20 sessions. e observed improvement consisted of increased
stress tolerance and a better ability to relax. Two participants of
the control group were not sure that the training course induced
any notable changes in their state.
e results of Visual Go/NoGo test performance were
as follows: Of the 9 participants in the ILF NF group, 2 made no
errors pre or post. Of the remaining seven, 4 showed improve-
ments in both omission and commission errors, while the re-
maining three declined in performance in one or another of the
categories. In the control group 4 of 8 participants made fewer
omission errors, and 2 of them – fewer commission errors. In
3 subjects an increase in the number of omission or commis-
sion errors was observed, and in 2 of them, the results did not
change. Whereas the individual data are too granular to draw
conclusions, the cumulative data do indicate a trend favorable
to neurofeedback. Total commission errors declined from 14 to
1 in the NF group, versus a decline from 5 to 4 in the BF group.
Omission errors declined from 60 to 20 in the NF group, versus
an increase from 15 to 24 in the BF group.
e mean Response Time (RT) declined by an insig-
nicant 2% in both groups, whereas the mean Response Time
Variability (RTV) improved by 7% in the NF training group and
by 13% in the BF group. e dierence was not signicant. In the
nal analysis, no signicant group dierences could be identied
in the Visual Go/NoGo test data.
In 13 out of 17 participants (77%), the baseline EEG con-
tained rhythmic infra-slow oscillations in the frequency range of
0.06–0.12 Hz against the background of the prevailing slower po-
tential uctuations. e localization of these oscillations varied:
in some subjects, the episodes of rhythmic 0.06-0.12Hz activity
were widespread, without any denite local dominance; in some
cases, these uctuations were observed over the frontal-central
region, while in other cases they were found over the posterior
brain areas.
e post-training EEG patterns in all 9 members of the
ILF NF group revealed a signicant enhancement of spectral
power in the 0.01-0.5Hz frequency band compared to the pre-
training EEG. One-way ANOVA detected a signicant main ef-
fect of the factor "Condition" for the slow activity in 0.01-0.5Hz
frequency band for the ILF NF group: F [1,16] = 5.22, p < 0.04.
An increase in average power in the 0.01-0.5Hz band is seen in
all 19 electrodes locations. For the control group, the average
values of the power of slow oscillations in the frequency range
0.01–0.5 Hz decreased in all locations aer the training, but this
decrease was not statistically signicant F [1, 14] = 0.96, p <0.35.
ese eects are illustrated in Figure. 1a, b.
e eect size for changes in the logarithmic power in
the 0.01-0.5Hz band was therefore higher for the main group
compared to the control group for all electrodes. ese eects
are illustrated in Figure. 2. In the control group, changes in slow-
wave activity aer the course of HRV BF were not statistically
signicant.
One-way ANOVA test with a factor (main group-con-
trols) revealed the statistically signicant dierence in the change
of power in the 0.01–0.5 Hz frequency band for the main group
compared to the change for the control group F[1, 15]=10.65, p
= 0.005.
Along with the general enhancement of spectral power
in the 0.01-0.5Hz frequency band in the main group, rhythmic
uctuations in the 0.06-0.12Hz band became more prominent in
8 of 9 subjects. It should be noted that the localization and peak
frequency of oscillations changed in some cases with the train-
ing, but no general trend was observable. In the control group,
the power in the 0.06-0.12 Hz band increased in 3 of 8 partici-
pants and did not change or even decreased in some degree in 5
of them.
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Figure 1. Dynamics of the power EEG of activity in a 0–0.5 Hz frequency band under the inuence of the ILF NF in
the main group(a) and HRV BF in the control group (b).
X-axis–Electrode locations; Y-axis–Logarithmic scale of ILF (0–0.5 Hz) power. Whiskers represent the 95% condence interval.
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Figure 2. Cohen's d eect size for changes for the power of EEG activity in 0.01–0.5 Hz frequency band aer training
courses for the ILF NF group (a) and the control (HRV) group (b).
X-axis– Electrode locations; Y-axis– Cohen’s d.
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Discussion
Infra-slow oscillations with frequencies below 0.5 Hz
are observed in the human brain in form of 1) uctuations of
electrical potential measured with non-polarizable electrodes
from the scalp [1, 9, 21], and 2) uctuations in BOLD signal
measured by fMRI [4]. In our own studies in the 1970s, we ob-
served similar infra-slow oscillations of extra-cellular oxygen
measured by a polarographic method utilizing polarizable elec-
trodes implanted into the brains of neurological and psychiat-
ric patients for diagnosis and therapy [13]. In our studies, we
compared these oscillations of extracellular oxygen with local
blood ow, impedance and electrical potentials in the infra-low
frequency band and determined that these oscillations reect
complex metabolic processes in the brain associated with oxygen
consumption by neuronal networks and local blood ow regula-
tion in brain tissue [13].
At the same time, the feedback procedures that utilize
infra-slow electrical potentials and HRV as feedback parameters
are very well established. e positive eect on symptoms of anx-
iety, depression, and sleep disturbances has been demonstrated
for both techniques [36, 47].
e goal of this paper was to test the specicity of ILF
neurofeedback for producing changes in the infra-slow EEG
signals of the human brain. It was indeed demonstrated that 20
sessions of ILF training signicantly enhanced the amplitude of
infra-slow oscillations in brain electric potentials, whereas 20
sessions of HRV training did not change the amplitude of these
oscillations signicantly. ese results support our hypothesis
about the direct inuence of ILF NF on the objective physiologi-
cal parameters reecting brain function.
Little is known regarding the link between the physi-
ological state of the brain and the power/amplitude of infra-slow
electrical potentials. According to 50 years of experience of stud-
ying infra-slow processes in the human brain under various con-
ditions Valentina Iliukhina from the N.P. Bechtereva Institute of
the Human Brain of the Russian Academy of Sciences has sug-
gested that enhancement of the metabolic activity of the brain
is accompanied by an increase of EEG power in the infra-low
frequency band [57]. Taking this statement into account, we may
speculate that 20 sessions of ILF training specically improve the
metabolic activity of the brain.
Nonetheless, the lack of a placebo group, the need for
larger randomized samples, and additional assessment measures
were the limitations of the present study. Placebo-controlled
trials carried out in varied clinical settings with larger groups
of subjects could place evidence for the inuence of ILF NF on
brain electric potentials on sounder footing, although the use of
sham treatment raises ethical concerns and even questions of
feasibility. More studies comparing ILF NF to an adequate con-
trol condition is needed to concern that the ILF NF training is
responsible for observed changes of the infra-slow EEG oscilla-
tions. However, as the rst study of this kind, this research will
hopefully stimulate more interest in the scientic community to
further investigations of the eects of ILF NF on brain function.
Ethics Statement
is study was carried out in accordance with the rec-
ommendations of the ethics committee of the Institute of Human
Brain of the Russian Academy of Sciences, Saint Petersburg. All
subjects gave written informed consent in accordance with the
Declaration of Helsinki.
Conict of Interest Statement
e authors declare that the research was conducted
in the absence of any commercial or nancial relationships that
could be construed as a potential conict of interest.
Author Contributions
VAG-Y recruited subjects, participated in experimen-
tal design, data acquisition, interpretation, and draing of the
manuscript. OK was involved in the draing of the manuscript.
SAE performed the statistical analysis. MG analyzed data. SO
critically revised manuscript and gave nal approval. JDK super-
vised the study and was involved in study design, interpretation
of data, and critical review of the manuscript.
Acknowledgments
e authors thank Susan Othmer, Clinical Director of
the EEG Institute, Woodland Hills, California, and Dr. Bernhard
Wandernoth of BEE Medic, engineer, and head of development
of the Cygnet Neurofeedback system used in this study, for their
support.
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