The document summarizes a study that investigated whether auditory attention is necessary for the cortical propagation of binaural beats. The study found that binaural beats were detected at the proposed brain location even with a distractor task, indicating the signal propagation is independent of attentional control. This suggests binaural beats could be used for applications like relaxation or hypnosis without requiring attention.

1. Binaural Beats and Attention:
SSAEP suitability
Dominic Portain
Jacek Sliwinski
22.04.2010
Seminarium HMI
Course code: 211032

2. Binaural Beats and Attention
Abstract
Binaural beats are a phenomenon created by the additive and destructive
overlap of two auditive waveforms with slightly different frequency. Due
to the possible low beat frequency, they can be used for external neuronal
stimulation. As shown by Schwarz & Taylor (2004), neuronal excitation
concentrates in the brainstem and spreads to the mediofrontal cortex.
Unlike many forms of steady-state evoked potentials, binaural beats only
have one point of origin, allowing for accurate feedback to a brain-
computer interface. The topic current study studied whether auditory
attention is necessary for the cortical propagation of binaural beats. In a
controlled double-task experiment, the neuronal correlates of binaural
beats could be detected at the proposed location. The presence of a
distractor task did not have an influence on the signal strength, adding to
the support against thalamic signal distribution. Because the signal
propagation is independent from attentional control, this method could be
find applications in the field of relaxation or hypnosis.
Introduction
Binaural beats are auditory perceptions which result from the interaction of two different tones,
if each waveform is fed into a separate hearing channel. Usually, due to the overlap of sound
waves and the subsequent addition of amplitudes, an acoustic beat is created. In this case, the
basic frequency of both tones is propagated by the cochlea, until the two channels are intertwined
in the superior olivary nucleus – a part of the brainstem. To allow for an accurate perception of
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3. Binaural Beats and Attention
the waveform phase through the cochlea - essential to create a binaural beat - the basic waves
have to oscillate below 1000Hz and differ not more than 30Hz from each other (Oster, 1973).
Above a frequency of 1000Hz, the synchronously firing auditory neurons in the cochlea become
saturated and are superseded by (uncoupled) frequency-specialized neurons.
Neuronal activation, originating in the auditory channel intersection, subsequently spreads
into shallow brain areas – including the mediofrontal cortex (Schwarz & Taylor, 2004). The
mechanism of signal propagation is still a topic of debate. First, the cerebral fluid allows for
large-scale passive distribution across the brain and skalp if a large enough group (the superior
olivary nucleus consists of several 100.000 neurons) would be firing simultaneously. Second, the
electrical activity from binaural beats could be propagated by the “frequency following
response”, a phenomenon that causes nearby neurons to adapt the strong frequency pattern in
their direct proximity – especially if this pattern contains frequencies similar to regular activation
patterns. These adapted patterns would then be propagated by using regular long-scale axons
(probably related to motor control) towards the mediofrontal center of the brain. The third
alternative, including the regular - attention-regulated - thalamic pathway for auditory signals,
has been considered improbable due to the comparable weak activation in the (auditory)
temporal cortex.
Binaural beats have already been found to alter neural oscillation patterns in a large scale,
which makes them a probable source to be used as an auditory steady state evoked response
(Schwarz & Taylor, 2004). In this way, binaural beats were proposed to be applicable to brain
computer interfaces. However, as the original study required full auditory attention, it is not clear
if the influence of binaural beats on larger neural groups depends on the attention of the listener.
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4. Binaural Beats and Attention
The current study investigates the effect of attention on binaural beats. By using a study
design with four different conditions, comprising an auditory attention task and the introduction
of distractors, the influence of auditory attention on binaural beats is examined. The underlying
hypothesis is that the influence of binaural beats can be controlled by auditory attention.
Method
Participants
Two students participated in this study, one female (22 years), and one male (23 years).
According to a self-report, none had a hearing impairment or prior note experience. The
experiment was performed within the master course Seminar HMI on the University of Twente,
the Netherlands.
Procedure
The experiment of this study used a 2 (Binaural beat sequences) x 2 (conditions) design. This
test setup presented a (primary) auditory attention task and an auditory distractor throughout
various conditions. Participants were - depending on the current block - asked to focus their
attention either on the auditive attention task or the distractor. By comparing the brainwaves of
similar training blocks with each other, the effect of attention (or distraction) on the propagation
of the binaural beat pattern could be investigated. To prevent a sequence bias during one trial, the
order of the genuine binaural beat blocks was reversed for each participant.
Apparatus
The experimental setting contained two computers, one Biosemi EEG set, and headphones.
One computer was connected to the EEG and used for the recording of brainwaves. The other
computer was used for presentation of the stimuli (prepared and mixed into separate .wav files),
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providing auditory output via semi-open AKG studio headphones. Both computers were
equipped to perform their tasks fluently, and were running Microsoft Windows XP as operating
system. The EEG signals were delivered by a BioSemi amplifier, connected to 32 electrodes in
standard BioSemi layout.
Task & Stimuli
After initially greeting the participants, each was briefed with short information about the
experiment. Afterwards, the participants were asked to sit on a chair, while their heads were
measured and a fitting head cap installed. The electrodes were connected and the recording and
presentation software set up.
The treatment consisted of four blocks (see Figure 1). Each block had a length of two
minutes. Binaural beats including variable beat frequency (shifting from 15Hz to 8Hz) and
amplitude (shifting from 100% to 10% and back) were presented in the blocks two and four.
Additionally, an attention task was included. Per block, 24 consequential tone modulations
across three different base frequencies (random distribution, no immediate repetitions) were
presented, while the participants had to indicate the perceived tone height on a 24x3 sheet of
paper. The base frequency of the tone was 440 Hz, modulated one or two chromatic notes higher.
This task was conducted to help the participants to focus their attention on the given task (and to
be able to use the results as covariates afterwards).
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6. Binaural Beats and Attention
Figure 1. Training schema. AAT = Auditive Attention Task, D = Distractor,
Filled boxes show were the participants were instructed to pay attention to.
In block two and four, additionally, a distractor task was presented, which was a snippet of a
podcast about neuroscience. The distractors were band-pass filtered at -32db between 420Hz and
540Hz to prevent acoustic interference with the binaural beat stimuli. Participants were asked to
focus their attention on the distractor in the last block. At the end of the blocks with distractor
task, participants were asked to fill in a short questionnaire about the content of the distractor (5
questions, 4 multiple-choice answers) to test their retention and infer there from if they focused
their attention on the distractor. After the treatment, the participants were given thanks and bid
farewell.
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Analysis
All calculations have been made using Matlab R2009b. Additionally, the EEGLab 6.03b
plugin for Matlab was used to import and process the data from the BioSemi EEG files. After the
channel locations were defined, common average referencing was applied. Every further step of
the analysis was limited to the channel Fz, partly because prior research indicated the highest
effect there (Schwarz & Taylor, 2004) and partly because the high impendance of the channel Cz
(which would have been the “optimal” electrode with approximately +15% effect size) did not
allow any useful signal analysis.
In the first step, 91 wavelet coefficients of each sequence (EEG and stimulus files) were
calculated. For this purpose, the combined audio file (containing the binaural beats in monaural
form) was resampled from 44100 to 1024Hz using cubic interpolation. The wavelet coefficients
were calculated by using a continous wavelet transformation on the sample sizes 20-200
(equivalent to 5-50Hz) in steps of 2. Quadratic bi-orthagonal B-Splines (Cohen et al, 1992) were
used as the basic wavelet functions for this transformation because they provides the most
accurate simulation of the response curve of a group of neurons.
The second step was aimed to test whether the results from Schwarz & Taylor could be
replicated. To confirm the electrical propagation of the stimulus patterns, the EEG files were
correlated against the equivalent wavelet spectrum of an idealized waveform (2 minutes,
continuous sinus wave in decreasing frequency from 15 to 8 Hz). The assymmetric correlation
power (averaged over all non-distractor trials) is then sampled over the relevant coefficients (11-
20) and the level of statistical significance is calculated by comparing the result with the grand
total of signal standard deviation across the same dataset. 95% correlation significance is desired
(equal to 1.6x standard deviation).
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The third step included the comparison between auditory stimulus signals and EEG files. As
preparation, an averaged wavelet spectrum of all trials is calculated, seperately for the two
conditions (including or excluding attentive distractors). These spectra are normalized and then
correlated against the stimulus signal channel-wise with including a moving delay of 0 to +1024
samples (eq. to 1 second). The two resulting twodimensional time-correlation spectra are reduced
to a grand average (and standard deviation) of all correlation coefficients for each time point.
The resulting two time-dependent scales for correlation are then subtracted from each other,
creating a difference scale. The meaningless correlation units (due to the normalization) are
augmented by the standard deviation scale that illustrates a significant difference between the
two conditions if met.
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Results
The time-symmetric correlation spectrum (see Figure 2) is showing an assymetric bias
concerning the spectral time-energy-distribution, which indicates a mediocre correlation
(assymetric correlation between coefficients 11 and 20: r²=0.114; p=0.032 on average) between
the optimized stimulus and the response data. The findings of Schwarz & Taylor (2004) are
confirmed, although the link is barely significant.
Figure 2. 2-sided correlation between EEG data and idealized auditive spectrum; clear asymmetry in the region
between 11 and 20 samples ( eq. to 8 and 15Hz) across the time symmetry axis at x=1024 (total width: 2 seconds)
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Considering the main research question, the difference between the tasks without distractors
and those under influence of a second task are depicted in figure 3. The limit of random
fluctuation is indicated by the blue line of 95%-significance (in fact, these lines are symmetric to
the x-axis - ranging between ±0.68 and ±0.37). The differential correlation values (ranging
between -0.28 and +0.32) are limited to the range below the border of significance along the
whole length of correlation (which was limited to one second due to cognitive considerations).
As a result, the effect of attention did probably not change the overall stimulus pattern
propagation in a significant manner, disproving the initial research hypothesis.
Figure 3. Influence of the distractor task, displayed in green as waveform difference between the correlation spectra
(0 is equivalent to zero correlation delay); blue illustrates the 95% significance limit
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Conclusions
The prospect of using binaural beats as steady-state evoked potentials has been depending on
the amount of control that can be excerted via the attentional focus. The use in brain-computer-
interfaces is severely limited if a signal is perceived by the controlling person, but can not be
channeled, filtered or changed in any way discernible by a current signal processing routine.
Our results propose that auditory attention is not only unneccessary to allow the signal
propagation of binaural beats, but is even unable to change the neural patterns in any significant
way. Our findings also deliver support for the theory of signal propagation via frequency
following response, as a passive distribution of electrical patterns would result in a far stronger
peak near the origin of the time-correlation chart. Because the correlation is distributed across a
wider range of time, these findings provide additional evidence for a nonlocalized method of
signal propagation when it comes to binaural beats.
Because binaural beats are apparently propagated across a wide range of nerve fibers, and the
neuronal firing patterns cannot be adequately controlled by attentive processes, this technique
could be find new use cases in the fields of relaxation or hypnosis. The external stimulation of
alpha waves via binaural beats has already been proposed as a relaxation technique (Brady,
2000). However, the external stimulation has never been seriously considered to be out of the
thalamic control of the listener. If this finding is confirmed in future replications, binaural
stimulation could be fine-tuned to individual needs and large-scale brain oscillations in order to
augment or dampen certain large-scale neuronal patterns. Future research should investigate the
power and limitations of the olivary nucleic neuron groups. If the emitted deep-brain signals
prove to be strong and quick enough, even certain medical conditions such as early-stage
parkinson or beginning migraines could be resolved non-invasively.
The scope of our study is, including only two test subjects and incorporating the noise of
several faulty channel connections, severely limited. Hoewever, the research design may prove
to be sound and we invite future researchers to replicate our results with a larger group of
participants.
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References
Brady, B., & Stevens, L. (2000). Binaural-beat induced theta EEG activity and hypnotic
susceptibility. American Journal of Clinical Hypnosis, 43(1), 53–70.
Cohen, A., Daubechies, I., Feauveau, J.C. Bi-orthogonal bases of compactly supported wavelets.
Comm Pure Appl Math 1992;45:485–560.
Oster, G. (1973). Auditory beats in the brain. Scientific American.Volume 229, Issue 4, Pages
94–102.
Schwarz, D.W.F. & Taylor, P. (2004). Human auditory steady state responses to binaural and
monaural beats. Clinical Neurophysiology, Volume 116, Issue 3, Pages 658-668.
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