Press Release
Dear Customers, Dear Friends of Brain Products,
April 2013, Volume 46
In this issue of our Press Release we are introducing a new member
of our family of peripheral sensors for recordings in MRI scanners,
the Respiration Belt MR.
Contents of this issue
IN THE FOCUS
The Respiration Belt MR:
a new device for parallel
respirator y measurements
Products in Practice
Best Current Practice for
Obtaining High Quality EEG Data
During Simultaneous fMRI
Product Development
BrainVision Recorder 1.20.0506
Support Tip
How to add methods to
BrainVision Analyzer quickly
and easily? An example of the
interactive Matlab interface
User Research
Perceiving while acting: How
visual selection is tuned to action
intentions, an EEG study.
New Products
BrainVision Recorder Remote
Brain Products Inside
Changes in company
management
2
3
4
5
6
9
In this context we also present a video on “How to obtain high quality EEG data during
simultaneous fMRI” in this issue of our Press Release. The video is the result of a
cooperation between Brain Products, the University of Nottingham and the Journal
of Visualized Experiments (JoVE).
Another product we are presenting here, is a new version of the BrainVision Recorder
software. The latter is an evergreen product for us, whose first version was compiled
more than 15 years ago. The new version is Windows 8 compatible and includes some very
interesting tools allowing for example to import non-standard cap montages (equidistant
or customized) in a very user friendly manner or the option to display different channel
groups according to different time and amplitude scaling.
I hope you enjoy reading our Press Release.
10
Pierluigi Castellone
Brain Products‘ CEO
Brain Products Inside
Who is who..? - Ratko Petrovic
10
Brain Products Inside
Who is who..? - Manuel Hohmuth
10
Brain Products Distributors
Two job offers at Brain Vision
11
User Workshops
ERP Workshop for beginners and
Advanced EEG & fMRI Workshop
The interest of the scientific community for in MRI usable
respiration sensor grew significantly in the past years. Respiration
is the source of several artifacts both in the EEG and fMRI data,
which can be corrected or disentangled from the signals of interest,
if a respiration belt is used. This sensor joins the already large
family of amplifiers (BrainAmp MR series), caps (BrainCap MR),
sensors (GSR MR, Acceleration 3D MR) and software solutions, which had been explicitly
designed for recordings in MRI scanners, and that make Brain Products the leading company
for the co-registration of ExG and other peripheral physiological signals with fMRI.
12
Brain Products Inside
Who is who..? - Nicola Soldati
12
News in brief
Conferences
13
News in brief
Downloads, Programs & Updates
13
Contact & Imprint
13
Brain Products Press Release
April 2013, Volume 46
IN THE FOCUS
The Respiration Belt MR: a new device for parallel respiratory measurements
by Nicola Soldati
The investigation of physiological signals continues to receive a
great amount of attention. The obvious reason is that such signals
are deeply rooted in the nature of the subject of investigation
(i.e. living beings) and they strongly interact with the organisms
at various levels, from pure physiology to higher level cognitive
functions.
Depending on the type of research, these signals can be useful
when addressing specific questions, or they maybe simply add
noise to the signals of interest. The latter is often the case in
neuroscience, where strong physiological phenomena such as
cardiac and respiratory cycles affect the measurements acquired
by different techniques (EEG, fMRI).
Respiration plays a critical role in the MR environment, where it
may not only be a confounding factor, but also a source of
related artifacts. It can be linked to movement artifacts (due to
the mechanical action of breathing - the typical respiratory rate
of a healthy adult is 12-20 breaths per minute), physiological
alterations (change of BOLD signal properties), induced field
inhomogeneity (change of air volume in the lungs can affect the
magnetic field locally), or interference with the experimental
paradigm.
Studies using fMRI show that respiratory effects cannot be
ignored, given that respiration induces great changes in terms
of artifacts, and different respiratory patterns cause different
oxygenation and finally change the fMRI measured BOLD signal
(Thomason et al. 2005).
For this reason, advanced signal processing techniques have
been developed with the goal of eliminating these confounding
factors. One proposal was the use of Independent Component
Analysis (ICA) to correct and remove structured noise (Thomas
et al. 2002). However, recent work has shown that ICA alone
cannot completely remove physiological noise from fMRI data
(Beall et al. 2010) and moreover that higher order fluctuations
in respiratory patterns induce detectable signal changes which
can act as a confounding factor in research related to resting state
(Birn et al., 2008).
Even if advances in data analysis techniques can provide better
results at the cost of greater complexity, these results are
considerably improved by parallel dedicated measurements of
the sources of the artifacts. An efficient method which exploits
parallel measurements for artifact correction uses acquired
respiratory signals to create a principal regressor, along with
other derived regressors obtained with a higher order analysis
of the signal itself. This approach is known as RETROICOR
(Glover et al., 2000). It is clear that a higher quality and sensitivity
of acquired respiratory data will lead to an improved quality
of all the regressors and finally to a higher quality of artifact
correction and final denoised data, independent of the strategy
adopted to correct for respiratory artifacts. With the aim of
obtaining the best data quality and the optimal method of
artifact correction we have developed the Respiration Belt MR,
a novel device for the acquisition of respiratory signals within
MR environments (Fig.1).
Working in an MR environment imposes several constraints
ranging from the safety and care of the subject to the quality of
the acquired data. Our solution offers advantages for all these
factors. We decided to realize a respiratory belt, because this is
a non-intrusive sensor which is comfortable for the test subjects,
who may already be negatively affected by the fMRI procedure
(Cook et al., 2007).
The compatibility and safety of the Respiration Belt MR result
from its technical characteristics.
One of its main features is that it is based on a pneumatic
technology, unlike most solutions on the market. This avoids
safety issues related to the introduction of electrical devices
in strong magnetic fields. In addition, being pneumatic-based,
Respiration Belt MR Transducer
Respiration Belt MR Sensor
Respiration Belt MR Transducer
Elastic belt and pouch
Auxiliary Connector Cable
Figure 1
www.brainproducts.com
Figure 2
page 2 of 13
Brain Products Press Release
the Respiration Belt MR is not a source of artifacts for the MR
imaging, thus preserving the highest data quality and ensuring
that no noise is induced on the MR recorded signal. Extensive
tests have been carried out with scanners from various
manufacturers with very satisfactory results.
Moreover, we developed our Respiration Belt MR with the aim
of having a device with great sensitivity which is able to
adequately follow different types of respiratory acts in a robust
way. Figure 2 shows a slow and deep respiration (black line)
and a faster and shallow respiration (red line) as measured
by the Respiration Belt MR: The Respiration Belt MR is able to
follow the dynamic of respiratory act over quite a wide range,
April 2013, Volume 46
showing a good sensitivity of the system. This makes of it a
powerful and sophisticated tool to obtain high quality respiratory
signals, and thus regressors for artifact correction, and also
to investigate interrelation between physiology and brain
organization more accurately. The higher sensitivity of the belt
to respiratory dynamics makes it easier and more effective to
compute higher order regressors describing fluctuations of
respiration over time.
We are convinced that the new Respiration Belt MR represents a
very useful instrument for advanced research over a wide range
of applications and we will be pleased to welcome any of your
further enquires.
Moriah E. Thomason, Brittany E. Burrows, John D.E. Gabrieli, Gary H.
Glover, Breath holding reveals differences in fMRI BOLD signal in children
and adults, NeuroImage, Volume 25, Issue 3, 15 April 2005, Pages 824837, ISSN 1053-8119, 10.1016/j.neuroimage.2004.12.026.
Birn, R. M., Murphy, K. and Bandettini, P. A. (2008), The effect of
respiration variations on independent component analysis results of
resting state functional connectivity. Hum. Brain Mapp., 29: 740–750. doi:
10.1002/hbm.20577.
Christopher G. Thomas, Richard A. Harshman, Ravi S. Menon, Noise
Reduction in BOLD-Based fMRI Using Component Analysis, NeuroImage,
Volume 17, Issue 3, November 2002, Pages 1521-1537, ISSN 1053-8119,
10.1006/nimg.2002.1200.
Glover, G. H., Li, T.-Q. and Ress, D. (2000), Image-based
method for retrospective correction of physiological motion
effects in fMRI: RETROICOR. Magn Reson Med, 44: 162–167. doi:
10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E.
Erik B. Beall, Mark J. Lowe, The non-separability of physiologic noise in
functional connectivity MRI with spatial ICA at 3T, Journal of Neuroscience
Methods, Volume 191, Issue 2, 30 August 2010, Pages 263-276, ISSN 01650270, 10.1016/j.jneumeth.2010.06.024.
Cook R., Peel E., Shaw R.L., Senior C., 2007. The neuroimaging research
process from the participants’ perspective. International Journal of
Psychophysiology 63, 152–158.
Products in Practice
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
by Stefanie Rudrich
Brain Products has been a market leader in amplifiers, electrode
caps and software for EEG/fMRI co-registrations for more than a
decade now.
More than 300 pubmed-listed publications have used our EEG/
fMRI equipment over this period (see www.brainproducts.
com/references.php), emphasizing that we not only provide
the equipment, but we also have experience and can offer
competent support in this field.
for successfully correcting them require a number of criteria
to be met during data acquisition. Based on their research,
Karen Mullinger and Richard Bowtell describe an experimental
set-up in the video which provides high quality EEG data during
simultaneous fMRI while minimising safety risks to the subject.
The video will be published shortly at JoVE‘s website:
www.jove.com
Combining EEG/fMRI safely and conveniently for the subject and
the researcher as well as ensuring good signal quality requires
taking many aspects into account. To give you an insight into this
we collaborated with long-time customers Karen Mullinger and
Richard Bowtell (University of Nottingham, Sir Peter Mansfield
Magnetic Resonance Centre) as well as JoVE (Journal of Visualized
Experiments). The result of this project is a video and detailed
protocol on the “Best Current Practice for Obtaining High Quality
EEG Data During Simultaneous fMRI”.
As you know, EEG data acquired during simultaneous fMRI
are affected by several artefacts. Post-processing methods
www.brainproducts.com
page 3 of 13
Brain Products Press Release
April 2013, Volume 46
Product Development
BrainVision Recorder 1.20.0506
by Dr. Roland Csuhaj
We are about to release a new sub-version of the popular
BrainVision Recorder software with the number of 1.20.0506.
This edition introduces some remarkable new features.
Windows 8 compatibility
Operating systems come and go, but Recorder stays. From
now, it supports Windows 8, the latest operating system from
Microsoft. It is compatible with 32 and 64-bit versions, in case the
BrainAmp, V-Amp or actiCHamp is connected. The combination of
QuickAmp and the Windows 8 32-bit is also supported.
This update does not include the support of the special tablet
version, the Windows RT. If the BrainVision Recorder 1.20.0506
has to run on a tablet-like computer, one with Windows 8
operating system should be used.
version improves this situation by handling BrainVision Electrode
Files (BVEF).
As with the Analyzer 2, the electrode file stores channel name
and position pairs in XML format. Now, the Recorder is also able
to open such files while the workspace is created or edited.
This feature offers three advantages: first of all it can replace
the channel name table; the names do not have to be entered
manually anymore. Secondly, the associated locations are
applied on the map of the impedance check mode: the electrodes
are topographically displayed in the real position (Fig. 2.).
Thirdly, and probably most importantly, the imported positions
will be written to the dataset’s header file, so an offline evaluation tool such as the Analyzer 2 can recognize and import the
correct locations automatically together with the data.
‘Tab view’ or ‘Scientific view’
The ‘tab view’ is a new data visualization tool, available during
data monitoring. It shows single or multiple channels in a new
tab, which explains why it is called ‘tab view’. The ‘scientific view’
name refers to the fact that all channels are displayed in their
own coordinate system with horizontal and vertical guidelines
(see also Fig. 1.).
Figure 1. New ‘scientific view’ during data acquisition. Note the different
time scale of the tabs.
Polarity, scaling and displayed time intervals are adjustable, and
the default values of these parameters can be edited. Therefore
this type of visualization is particularly suitable when channels
should be visualized with different time scales. For instance, the
reaction of the GSR data is typically slower than EEG data, so it is
reasonable to display them with a different time scale.
Not just one, but several tab views can be opened at the same
time to display different subparts of the data, e.g. to show only
the EMG channels or only the frontal ones. Furthermore the tab
configurations can be saved in the workspace, so that the same
type of visualization will be available for future recordings.
Electrode positions
Previously, Recorder was optimized for standard caps, which
follow the international 5% standard. However this meant that
the impedance topography map did not work smoothly for nonstandard caps, like equidistant or customized caps. The new
www.brainproducts.com
Figure 2. Impedance measurement map with individual electrode positions
Keyboard shortcuts
This version introduces keyboard shortcuts for scaling and time
interval changing. Dedicated combinations are available for the
standard, average and for the new scientific view. One does not
have to remember all combinations, tool tips around the icons
help to recall the buttons. Although this is a small improvement,
I am sure many users will appreciate it.
Further changes
The V-Amp workspace now has a new checkbox which makes it
possible to invert the polarity of the auxiliary data.
Under the hood some further work was done, the new Recorder
is able to handle the SafeNet-SRM dongle technology and some
small bugs have been fixed.
The new BrainVision Recorder 1.20.0506 will be available for
download at www.brainproducts.com/downloads.php?kid=2
from early April.
page 4 of 13
Brain Products Press Release
April 2013, Volume 46
Support Tip
How to add methods to BrainVision Analyzer quickly and easily? An example of the interactive Matlab interface
by Dr. Roland Csuhaj
New signal processing methods are published virtually every
day; BrainVision Analyzer 2 cannot have all of them. However, the
Analyzer is not just a powerful tool for offline EEG data evaluation it is also a flexible framework which can integrate signal processing
functions from many different sources. Beside the macros and
add-ins, the Analyzer offers an exciting possibility to export your
data to Matlab, do some calculations there and import the results
back to Analyzer in order to continue your work in its user friendly
environment. Today I am going to show how quickly a new method
can be added to Analyzer using the Matlab Transformation.
Let’s look at the fraction peak latency method, which is a technique
to detect onset latency of components. The fractional peak latency
marks the time point, when a certain percentage of the peak
amplitude (e.g. 50%) was reached in the backward direction.
Although such a method is not yet implemented in Analyzer,
we can take advantage of the existing transformations: ‘Peak
detection’ can perform the first step, and identify the peak of
the average nodes. The Matlab transformation can quickly send
the data into Matlab in order to do the rest of the calculation
there. (In this article I am not going to introduce all options of the
transformation, only those which are important for our goal). Open
an average node, where the peaks are already detected and start
the Matlab transformation. In the first window, mark the ‘Calculate
Data on Creation of Node’ radio button, in the second one activate
the ‘Export Markers’. The transformation executes the Matlab
commands typed in the first window. You do not have to enter
hundreds of lines here, it is enough to call the .m file(s). The ‘Show
Matlab Window’ is pretty useful while the code is still polished.
For those who are more familiar with Analyzer, this is similar to the
semi-automatic mode: the data can be checked or even modified
manually in Matlab before it is imported back to Analyzer. Since we
have just started to deal with this transformation, simply mark the
three mentioned boxes, but not the others and enter the following
text to the Code Executed … field:
desktop;
It will only show you the familiar Matlab desktop instead of the
command line. Once the data is sent to Matlab, a dialog with ‘Press
OK to continue in Analyzer’ text will appear. Try to resist, and do
NOT click on OK until you are sure you are ready to close Matlab
and start the re-import phase. Once the Matlab main window is
started, you can have a look at the data. Many different properties
of the node were exported, for now only the ‘EEGData’ and the
‘Markers’ variables are important.
What should be done to mark the fractional peak? We need a loop
which checks all markers. If a ‘Peak’ marker is found (type is stored
in Markers.Type properties) the value of the corresponding data
point is needed from the EEGData variable. The fractional value can
be calculated easily and can be used as threshold. A second loop
should be started that searches for the data point in the backward
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direction at which the threshold is reached. This is the point
where the new marker has to be placed. During the re-import, the
Analyzer will look for the variable name ‘NewMarkers’, its contents
will be added to the existing marker list. Certainly it must have the
same structure as the ‘Markers’ variable. So in the next run we can
enter the following code into the Matlab transformation dialog:
Threshold = 50;
NbPeak = 1;
for i = 1 : size(Markers,2)
if strcmp(Markers(1,i).Type,(‘Peak‘))
Pos = Markers(1,i).Position+1;
PCh = Markers(1,i).Channel+1;
PValue = abs(EEGData(Pos, PCh));
Pos = Pos - 1;
w hile Pos > 0
CurrentValue = abs(EEGData(Pos, PCh));
if CurrentValue <= PValue*Threshold*0.01
N ewMarkers(1,NbPeak) = Markers(1,i);
N ewMarkers(1,NbPeak).Description = ...
strcat(Markers(1,i).Description,‚_‘, num2str(Threshold));
NewMarkers(1,NbPeak).Position = Pos - 1;
N bPeak = NbPeak +1;
break
end
Pos = Pos - 1;
end
end
end
desktop;
The percentage is defined by the ‘Threshold’ variable. The rest
is quite straightforward, except for one point: the ‘Markers’
variable refers to the channel and data position according to the
C# convention, where all indexing starts with 0. But the EEGData
matrix cannot be formed according to this convention because
in Matlab all indexing starts with 1. This difference has to be
compensated for by the code when it looks for the corresponding
data value (by adding 1) and also when it creates the new marker
(by subtracting 1).
Once you are sure the code is running fine, the desktop command
in the last line is not needed anymore and the ‘Show Matlab
Window’ checkbox can be deactivated. The transformation will
now run automatically. As you can see, only 21 lines were needed
to add a new method to Analyzer. It is a fast and effective way to
implement new functions.
The program codes are also available at www.brainproducts.com/
downloads.php?kid=21 as .m file. This version contains more
explanation as additional comment lines.
page 5 of 13
Brain Products Press Release
April 2013, Volume 46
User Research
Perceiving while acting: How visual selection is tuned to action intentions, an EEG study.
by Agnieszka Wykowska 1
1
Allgemeine und Experimentelle Psychologie, Ludwig-Maximilians-Universität, Munich, Germany
Theoretical background
Imagine you’re playing baseball and you’re just about to strike
an approaching ball with your bat. How does your brain plan that
action and what parameters need to be specified to perform
it efficiently? Apart from the obvious control of the motor
commands, the brain needs also to adjust perceptual processing
to fit the goals of the planned action [1,2]. Throughout lifelong
experience, humans learn that for various actions different
perceptual parameters are important and relevant [2]. This implies
that perceptual selection can be tuned to action planning [2-4].
That is, in the baseball example - when you plan an action,
depending on whether you plan to hit the ball or catch it, different
perceptual aspects of the ball will be relevant, and prioritized
accordingly. In catching the ball, grip aperture is important,
and hence, size and shape of the ball needs to be processed
with priority. In case of hitting the ball, its location is the most
important feature. In neither of the cases color of the ball is
relevant. We postulate that the so-called intentional weighting
mechanism [4,5] operates at the level of processing of perceptual
information in order to tune perception to action plans. The idea
is that planning a particular action should affect visual perception
in a way that perceptual dimensions, which are potentially
relevant for the intended action receive a higher weight than
those dimensions that are not action-relevant. This should allow
efficient delivery of perceptual parameters for online action
control [2-5]
This
design
made
participants
activate a movement code while they
were performing a perceptual task,
and created two action-perception
congruent pairs: size target was
Agnieszka Wykowska
congruent with the grasping movement
and luminance target was congruent with the pointing movement.
The congruency between movements and visual search targets
was predicated based on that size is a relevant perceptual
dimension for grasping (in grasping size of grip aperture needs to
be determined) while luminance is relevant for pointing
(luminance is an efficient hint for localization and pointing also
aims at localizing objects). Importantly, the visual search
was entirely unrelated to the movement task both motorically
(the visual search task was performed with one hand and
the movement task with the other) and perceptually (the
visual search display was presented on the computer
screen
while
the
to-be-grasped/pointed
to
objects
were located below the screen). Hence any action-related effects
on perceptual processing in the search task would indicate
influences from action planning at the representational level in
the brain [1-5]. We expected better detection of dimensions in
the action-congruent conditions relative to other conditions,
Experimental design
All figures and parts of the Methods section are as originally published
in Wykowska, A. & Schubö, A. (2012). Action intentions modulate allocation
of visual attention: electrophysiological evidence. Frontiers in Psychology,
3: 379. doi: 10.3389/fpsyg.2012.0037.
We conducted a study published in Frontiers in Psychology [6], in
which we examined behavioral manifestations and EEG correlates
of the intentional weighting mechanism. In our paradigm (see
Figure 1 for details) participants first observed a cue signaling
what type of movement (pointing or grasping) they should
prepare (but not execute immediately). Then, they performed a
visual search task (while preparing for the cued movement), i.e.,
they detected a target item presented among other distracter
items. The target of the visual search task was defined either by
size or by luminance (and differed from the other items only by
one respective feature). Participants were asked to respond with
one key on a computer mouse for target present trials and with
the other key for target absent trials. After the completion of the
visual search task (and upon presentation of a “go-signal”), they
executed (with the other hand) the movement that they planned.
The movement consisted in either grasping or pointing to one of
the cups positioned below the computer screen, and the particular
cup was indicated by the “go-signal”.
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Figure 1. An example trial sequence. A trial started with fixation display followed by a movement cue and another fixation display. Next, a
search display was presented and participants performed the visual
search task immediately. Upon response to the search task and a blank
screen, the go-signal asterisk was presented which indicated one of
the three cups that should be grasped/pointed to. At this point, participants executed the prepared movement, which was registered by
an experimenter with a mouse key press (the experimenter observed
performance with a camera outside of the experimental chamber).
Following the experimenter’s button press, a trial ended. Note that
catch trials (30%) differed from the standard trials only in that in place
of a search display, another fixation display was presented. As participants did not need to perform a search task, a blank display
was presented during the time they would respond to the search
display in case of trials of interest. The rest of the trial following the
blank display was identical to the actual trials.
page 6 of 13
Brain Products Press Release
given that the function of the intentional weighting mechanism
is to prioritize processing of perceptual dimensions that are
potentially relevant to a planned action.
While participants were performing the tasks, we recorded EEG.
In data analysis, ERPs were locked to visual search display onset
and thus reflected processes involved in the visual search task
(occurring before movement onset). In line with the idea that
the intentional weighting mechanism operates at the level of
processing perceptual dimensions, we expected action-related
modulation of ERP components that mirror early perceptual and
attentional processes in the visual search task.
Methods
Participants and Procedure
Eighteen participants (13 women) aged from 21 to 30 years
(mean age: 24.3) participated. They were seated in a dimly lit,
sound attenuated and electrically shielded cabin 100 cm away
from the computer screen. Before the experimental session,
participants took part in a practice session on a separate day.
The experimental session consisted of 504 trials for each of the
target types (luminance or size). The target type was blocked
(order counterbalanced across participants), whereas the
movement type (grasp vs. point) and display type (target present
vs. blank) were randomized within a block. Short breaks were
introduced after each 63 trials so that participants could move
their eyes, blink and relax.
April 2013, Volume 46
re-referenced offline to the average of all electrodes. Electrode
impedances were kept below 5 kΩ. Sampling rate was 500 Hz
with a High Cutoff Filter of 125 Hz. The EEG signal was amplified
with two DC amplifiers (BrainAmp DC, Brain Products GmbH)
and data were recorded using BrainVision Recorder 1.02 (Brain
Products GmbH).
EEG analysis
EEG data was processed with the use of BrainVision Analyzer
2.0.1 (Brain Products GmbH). EEG was averaged over 600-ms
epochs including a 200-ms pre-stimulus baseline, locked to
search display onset. Trials with eye movements and blinks on
any recording channel (indicated by any absolute voltage
difference in a segment exceeding 80 μV or voltage steps
between two sampling points exceeding 50 μV) were excluded
from analyses. Additionally, channels with other artefacts were
separately excluded if amplitude exceeded +/- 80 µV or any
activity was lower than 0.10 μV for a 100 msec interval. Raw
data was filtered offline 40-Hz high-cutoff filter (Butterworth
zero phase, 24 dB/Oct). Only trials with correct movement and
search responses were analyzed. Responses in the search task
deviating more than +/- 3 SD from mean RT (calculated separately
for each participant and target type) were categorized as
outliers and excluded. One participant was excluded from
analyses due to extensive eye blinks, two due to extensive
EEG recording
EEG was recorded with Ag-AgCl electrodes from 37 electrodes
mounted on an elastic cap (EASYCAP GmbH, Germany).
Horizontal and vertical EOG were recorded bipolar from the outer
canthi of the eyes and from above and below the observer’s
left eye, respectively. All electrodes were referenced to Cz and
Figure 2. Mean reaction times (RTs) as a function of visual search target
type (luminance vs. size) and prepared movement (grasp vs. point) in
target-present trials. Error bars represent the standard errors of the
mean. Action-perception congruency effects are observed as faster
detection RTs for each of the dimensions when presented in the actioncongruent condition (size-grasp and luminance-point) as compared to
incongruent condition (size-point and luminance-grasp). Interaction
of movement type and target type was significant for target trials,
F (1, 12) = 16, p < .005, hp2 = .58; and the difference between grasping
and pointing conditions was significant in the luminance task,
t(13) = 2.1, p < .05 (one-tailed) and marginally significant in the size task,
t(13) = 2.1, p = .06 (one-tailed).
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Figure 3. The ERP waveforms for pooled channels O1, O2, PO7, PO8,
locked to the onset of the visual search display as a function of target
dimension: luminance (3A) and size (3B) and prepared movement type:
pointing – solid line and grasping – dashed line. The grey outline boxes
represent the time window of statistical analysis of the mean amplitude
of the P1 component (70-130 ms, determined around +/- 30 msec the
grand average peak latency). Interaction of target type and movement
type was significant, F (1, 13) = 6.2, p < .05, hp2 = .32. P1 was more enhanced for the congruent movement condition, relative to the incongruent
condition for luminance targets, t (13) = 2, p < .05, one-tailed (3A) but
not for size targets, p > .25, one tailed (3B). The scalp distribution of the
mean amplitude of the ERPs within the 70-130 ms time window (P1) is
shown on the right. Note that the scalp distribution indicates a larger
positivity on the right electrode sites, independent of condition. This
might be related to the fact that attentional networks are located mostly
in the right cerebral hemisphere [9-10], and is in line with previous findings on attentional orienting that showed validity effects in a cueing
paradigm also predominantly on right lateral electrodes [11].
page 7 of 13
Brain Products Press Release
April 2013, Volume 46
Figure 4. The N2pc for the PO7/8 electrode pair, locked to the onset of
the visual search display as a function of target dimension: size (4A)
and luminance (4B), movement type (grasping: left; pointing: right) and
electrode site: contralateral to the target – solid lines; ipsilateral to the
target – dashed lines. The solid grey outline rectangle represents the
time window of analysis of the N2pc mean amplitudes (230–300 msec,
around +/- 35 msec the grand average peak latency of the difference
wave between contra and ipsilateral channels). For size targets, interaction between laterality (N2pc) and movement type was significant, F (1, 13)
= 5.2, p < .05, hp2 = .28. No such differential effect was observed for the
luminance condition, all p > .15. The dashed grey outline rectangle indicates the earlier time window of analysis (160-230 ms) in which a laterality
effect was found for both dimensions, F (1, 13) = 11, p = .01, ηp2 = .45, but
no modulation by movement type was observed. All other interactions and
effects were non-significant, all p > .6.
Figure 5. Topographical maps of the ERP voltage distribution for the
N2pc time window (230-300 ms) for size targets (upper panel) and luminance targets (lower panel) in the grasping condition (left) and pointing
condition (right) presented from posterior view (larger images) and top
view, all channels (smaller images, front plotted upwards). The voltage
distribution maps represent waveforms in the respective conditions for
targets presented in the left and right visual hemifields. The maps clearly
show target-related laterality effects (that is, enhanced activity contralateral to the target: the N2pc) for size targets in the grasping condition (upper
left), while laterality was present but less pronounced in the pointing condition (upper right). In the luminance condition (lower panel), negativity
was less pronounced in the grasping condition compared to pointing. In
grasping trials, there was no difference in negativity for contra- and ipsilateral sites (lower left) yet a slight difference (nonsignificant) is observed
in the pointing condition for target presented in the right hemifield (lower
right).
alpha waves and one due to poor performance in the movement
task (14% of errors in the pointing condition; other participants
did not exceed 7%). The analyses focused on O1, O2, PO7, PO8
electrodes for an early perceptual ERP component (P1, typically
in the time window of 100-130 ms), as well as on the PO7/8
electrode pair for the attention-related N2pc. The N2pc is
measured at posterior sites within the time window of ca. 180300 msec and is more negative on contralateral electrode sites
compared to ipsilateral sites relative to an attended object
presented in the left or right visual hemifield [7-8]. In
order to extract search-locked ERPs from the cue-locked
ERPs, catch trials were introduced (30% of all trials,
randomly intermixed with standard trials), in which
no search display was presented (trials consisted of only
movement task). Catch trials were subtracted from “actual” trials
on epoched data, separately for each cue type, time locked to
search display onset.
ms post-onset of the visual search display) for the luminance
target in the pointing (congruent) condition, as compared to the
grasping (incongruent) movement, see Figure 3. Although an
analogous effect on the P1 was not observed for size targets,
the size-grasping (congruent) condition elicited a larger N2pc as
compared to size-pointing (incongruent), see Figure 4 for grand
averages of the N2pc and Figure 5 for scalp distribution of the
N2pc effects.
Results
Behavior. We observed action-perception congruency effects:
size targets were detected faster when the grasping (congruent)
movement was prepared (relative pointing) while luminance
targets were detected faster when the pointing (congruent)
movement was prepared (relative to grasping), see Figure 2.
ERPs. The ERPs showed a more enhanced P1 component (ca. 100
www.brainproducts.com
Conclusion
The aim of this study was to investigate behavioral
manifestations and electrophysiological correlates of the
action-related intentional weighting mechanism imposed on
perceptual selection processes. We observed that perceptual
dimensions were processed with priority in the action-congruent
conditions, as compared to action-incongruent conditions,
as indicated by behavioral data and modulatory effects on P1
(luminance targets) and N2pc (size targets). Therefore, this
study provided striking evidence that the intentional weighting
mechanism operates at early stages of perceptual processes
and attentional selection; and biases processing of stimuli
with respect to action plans. Our findings support the idea that
perception and action are tightly coupled and that perceptual
selection is tuned to intended actions. In other words, what
we see is tuned to how we intend to act!
page 8 of 13
Brain Products Press Release
April 2013, Volume 46
References
[1] Hommel, B., Müsseler, J., Aschersleben, G., & Prinz, W. (2001). The
Theory of Event Coding (TEC): A framework for perception and action
planning. Behavioral and Brain Sciences, 24, 849-937.
[7] Eimer, M. (1995). The N2pc component as an indicator of attentional
selectivity. Electroencephalography and Clinical Neurophysiology, 99,
225-234.
[2] Hommel, B. (2010). Grounding attention in action control: the
intentional control of selection, in A new perspective in the cognitive
science of attention and action: Effortless Attention, ed. B. Bruya,
(Cambridge, MA, MIT Press), 121-140.
[8] Luck, S. J., & Hillyard, S. A. (1994) Spatial filtering during visual search:
evidence from human electrophysiology. Journal of Experimental
Psychology: Human Perception and Performance, 20, 1000-1014.
[3] Memelink, J., & Hommel, B. (in press). Intentional weighting: A basic
principle in cognitive control. Psychological Research.
[4] Wykowska A., Schubö A., & Hommel B. (2009). How you move is what
you see: Action planning biases selection in visual search. Journal of
Experimental Psychology: Human Perception and Performance, 35,
1755-1769.
[5] Wykowska, A., Hommel, B., & Schubö, A. (2012). Imaging when
acting: picture but not word cues induce action-related biases
of visual attention. Frontiers in Psychology, 3:388. doi: 10.3389/
fpsyg.2012.00388.
[9] Heilman, K. M., & Van Den Abell, T. (1980). Right hemisphere dominance
for attention: the mechanism underlying hemispheric asymmetries of
inattention (neglect). Neurology 30, 327–330.
[10] Thiebaut de Schotten, M., Dell’Acqua, F., Forkel, S. J., Simmons, A.,
Vergani, F., Murphy, D. G., et al. (2011). A lateralized brain network for
visuospatial attention, Nature Neuroscience, 4, 1245-6.
[11] Mangun, G. R., & Hillyard, S. A. (1991). Modulations of sensory- evoked
brain potentials indicate changes in perceptual processing during
visual-spatial priming. Journal of Experimental Psychology: Human
Perception and Performance, 17, 1057–1074.
[6] Wykowska, A., & Schubö, A. (2012). Action intentions modulate
allocation of visual attention: electrophysiological evidence. Frontiers
in Psychology, 3: 379. doi: 10.3389/fpsyg.2012.00379.
New Products
BrainVision Recorder Remote
by Stefanie Rudrich
In time for Christmas 2012, we released our Remote Control tool
for BrainVision Recorder. Many of you have already downloaded
BrainVision Recorder Remote which allows you to control
selected features of BrainVision Recorder via a smartphone
(iPhone, Android based phones). If you haven’t tried it yet, go to
www.brainproducts.com/downloads.php?kid=36 and get your
Recorder Remote for free now!
BrainVision Recorder Remote is a web browser-based application
and requires only a TCP/IP connection between your smartphone
and the monitoring PC. Starting Recorder Remote on the PC
automatically calls up BrainVision Recorder. Then you can either
scan the displayed QR code with your smartphone or enter the
IP address directly into your Web browser. And there you go …
your smartphone is a remote control.
Once the smartphone is connected to the web server you can
execute the following features of BrainVision Recorder remotely:
•
Start monitoring or test signal
•
Start impedance measurement
> if the amplifier has several impedance groups
- data, references, and ground - you can also
switch groups with this button
•
Stop all operations
www.brainproducts.com
Why BrainVision Recorder Remote?
You might already have experienced
a situation like this: The computer on
which BrainVision Recorder is running is
located outside the (shielded) EEG cabin
where you conduct your experiments.
In this case you often need to run
between the computer and the subject,
to start the monitoring, to check/re-check
impedances, or to re-prepare the electrodes. That is all history
now. Recorder Remote enables you to accompany the subject
into the experimental cabin and start the monitoring directly
from there. You can also check impedances, re-prepare the
electrodes that show unsatisfactory impedances, and re-start
the monitoring from where you are, using the monitor in the EEG
cabin to display the Recorder Software. Give it a try - it’s easy
and it’s free! Download Recorder Remote now!
•
Adjust the scaling of the signal
or the range of the impedance measurements
> using the (+) and (-) buttons
•
Confirm or terminate messages of BrainVision Recorder
> using the OK button (green tick)
and Cancel button (red cross)
page 9 of 13
Brain Products Press Release
April 2013, Volume 46
Brain Products Inside
Changes in company management
by Alexander Svojanovsky
Effective January 3rd 2013, Dr. Achim Hornecker is no longer
Managing Director of Brain Products and he has left the company.
In addition to managing the Freiburg branch and the execution
of smaller software projects, his main remit was controlling the
development of the BrainVision Analyzer software.
Achim had already supported the former head of development
and co-founder Henning Nordholz when the company was
started up in 1997. He then succeeded Henning and
acquired company shares. Under his management, the Freiburg
branch expanded and Analyzer 2 was developed. Achim
initiated the start of further software projects, some of which
will partly be completed in the coming months and years. He
also established a technical writing department which produced and updated the user manuals for all company products.
Achim will continue to be a partner, external adviser and service
provider - and he will remain a welcome guest at any time!
We thank Achim for his time at Brain Products, where he has not
only left his mark on various software projects but also leaves a
legacy in the form of the team that he built up in Freiburg. He will
have a lasting place in the history of the company and we respect
him as an outstanding personality.
The software projects will now be continued under the leadership
of our Technical Manager Dr. Manfred Jaschke, while the new
head of the Analyzer project is Tomasz Kucinski. Markus
Kölble will be in charge of a further project in Freiburg.
Let me use this occasion also to welcome Jens Grunert and
Patrick Britz as new shareholders.
Brain Products Inside
Who is who..? - Ratko Petrovic
I’d like to introduce myself as a new software developer for
Brain Products GmbH. After obtaining a degree in electrical
and computer engineering at University of Technical Science
in Novi Sad (Serbia), I continued to work at the Department of
Biomedical Engineering as a teaching and research assistant.
My main research area was functional electrical stimulation
and neural prosthetics, as well as biosignal processing and
analysis of ECGs and EMGs.
After receiving my Master’s degree in biomedical engineering
for a topic related to recording and analyzing ECG signals,
I accepted an offer from the German company Biosigna
GmbH, the leading developer for the ECG diagnostic
algorithms, to work on developing their products. For almost
5 years I worked for the company on improving existing
ECG algorithms, on the development of long-term,
biomarker-based ECG diagnostic algorithms, and leading the
client’s specific needs project. In 2010
I decided to expand my engineering
experience, and moved on to Noser
Engineering AG, where I had the opportunity to work as a software engineer in
various fields, like automotive, chemical
and mobility engineering.
Ratko Petrovic
After two years I realized that the medical engineering is
my biggest interest, which brought me to Brain Products.
From the first visit to the company I was thrilled with
the products and projects that were designed and developed
within the company, and the support that Brain Products
provides in the field of BCI and neural research. Now, I am
pleased to become a member of the innovative team of Brain
Products and to contribute with my knowledge and experience
to the development of new innovative solutions.
Brain Products Inside
Who is who..? - Manuel Hohmuth
I am Manuel Hohmuth, and since September 2012 I have been
Assistant Production Manager at Brain Products.
Previously I had worked as an electrician in various production
companies such as SGB Sächsische-Bayerische StarkstromGerätebau GmbH and Siemens AG. I am now working in
Production and Repairs, producing electrodes, testing actiCAP
ControlBoxes, actiCHamps, MOVE2actiCAP adapters, and
much more besides. In addition I carry out repairs on devices
sent in by our customers.
www.brainproducts.com
This work can range from simple jobs such
as changing electrodes through to carrying
out complete upgrades. And since no
production is possible without materials,
I also place all the necessary orders.
I applied to Brain Products because I
Manuel Hohmuth
wanted a career change. That is what
I have achieved with the move to Brain Products and I am really
enjoying my job!
page 10 of 13
Brain Products Press Release
April 2013, Volume 46
Brain Products Distributors
Two Job Offers at Brain Vision LLC
by Dr. Florian Strelzyk, Brain Vision LLC
Brain Vision is the US distributor of Brain Products GmbH. Our
products are used by scientists in neuroscience in leading
research institutes.
We know that long lasting customer relations can only be based
on trust in our solutions, service and products.
Scientific Consultant / Full-time
We establish and maintain this trust by providing reliable
products, matching solutions and outstanding support and
are regularly engaging with key scientists and other companies
in valuable collaboration projects.
These are our current job offers:
Assistant Support and Sales Manager / Full-tim
Place of Employment:
Morrisville, NC / USA (Brain Vision‘s Headquarter)
Place of Employment:
Morrisville, NC / USA (Brain Vision‘s Headquarter)
Job description:
Job description:
Our ideal candidate will have a deep understanding of EEG research but will also enjoy working with key researchers and opinion makers of our immediate and other field(s) of neuroscience.
Our ideal candidate will have a deep understanding of EEG research and related scientific fields and will enjoy advising
on various collaborations with scientists and industry leaders.
Your duties will include:
Your duties will include:
•
Supporting the activity and be a part of the consulting and
support team.
•
Finding the best solution for our customers!
•
•
Working together with Brain Products to
comprehensive technical and scientific support.
Supporting the activity and be a part of the support, sales
and consulting team.
•
Attending conferences (e.g. SPR, HBM, CNS, HCI).
•
Attending conferences (e.g. SPR, HBM, CNS, HCI).
•
•
Traveling throughout the US and Canada for workshops,
installations and customer trainings (total travel time
15-20%).
Frequently traveling throughout the US and Canada for
workshops, installations and customer trainings (total travel
time 20-25%).
•
Maintaining up-to-date knowledge in EEG & ERP research.
provide
Applicant requirements are:
•
Academic degree (Masters or PhD preferred) in a relevant
field of neurosciences, psychology, physics, biophysics,
biomedical technology or a related field.
•
Business experience is a plus.
•
Experience in complex neurophysiological analyses; ideally
you are a user of our hard- and software solutions.
Experience in neurophysiological analyses; ideally you are a
user of our hard- and software solutions.
•
High level of analytical skills including quick comprehension
and satisfaction in finding solutions.
High level of analytical skills including quick comprehension
and satisfaction in finding solutions.
•
Possess excellent communication skills; specifically
you should enjoy frequent interaction with our customers.
Possess excellent communication skills; specifically you
should enjoy frequent interaction with our customers.
•
Ability to effectively communicate complex scientific topics
to varied audiences.
Confidence in your capacity to take initiative and work
independently.
•
Outstanding written and spoken English.
Applicant requirements are:
•
•
•
•
•
Academic degree (Masters or PhD preferred) in a relevant
field of neurosciences, psychology, physics, biophysics,
biomedical technology or a related field.
•
Confidence in your capacity to take initiative and work
independently.
•
Outstanding written and spoken English; proficiency in an
additional language is a plus.
Benefits include working in a fast growing company within a pleasant and skillful team, competitive salary, and full benefits (medical,
dental, vision, and vacation/holiday time).
How to apply:
If you meet the skills requirements and wish to explore the opportunity of joining Brain Vision LLC please send your application
documents (cover letter, curriculum vitae) by email to Dr. Florian Strelzyk ([email protected]).
Brain Vision is an equal opportunity/affirmative action employer. We base all hiring decisions on nondiscriminatory factors.
www.brainproducts.com
page 11 of 13
Brain Products Press Release
April 2013, Volume 46
User Workshops
ERP Workshop for beginners and Advanced EEG & fMRI Workshop in Guangdong (China), November 27-30, 2012
by Alyssa He, Hanix Shenzen
Advanced EEG & fMRI workshops are gaining in popularity in
China. The 8th annual China workshop was attended by over 100
Brain Products users in Guangdong last November, in an event
timed to fit in with the annual national psychology aca-demic
conference of China. The workshop was booked out within weeks
of its announcement, with workshop participants coming from
different provinces and Hong Kong.
The workshop started with scientific talks by prominent
Chinese scientists working in various areas of neuroscience.
The psychologists Yaojia Luo and Xiaolin Zhou presented
psychological studies focused on ERPs. Dr. Ruiwang Huang,
a professor mainly working with fMRI, gave a comprehensive
theoretical talk in which many applications in cognitive
neuroscience were described.
Dr. Yong Li gave a lecture about an investigation on movement
disorders performed in the fMRI with Brain Products BrainAmp
ExG system and GSR MR sensor.
international conferences in 2012. The winning poster had the
title “ERP study about intuitive processing category decisions”
and was chosen by a committee of experts selected by Shenzhen
Hanix.
The event was organized with the help of the Guangzhou
University. We are very grateful to President Haosheng Ye
and his enthusiastic team for their support throughout the
workshop. We also appreciate the organizational assistance
from Brain Products. The help from them was central to the
event’s success. This workshop was the latest successful
collaboration between Brain Products, Shenzhen Hanix
and Guangzhou University, and we hope there will be many
more to come.
We are now collecting posters for the 9th Advanced EEG & fMRI
workshops in 2013, and again an award will be given for the
best submission. We believe that the next Advanced EEG & fMRI
workshop will once more be a real success!
The workshop continued with some practical demonstrations,
including a 1-day ERP workshop for beginners, a set of
measurements with the MOVE wireless system, and a 2-day
comprehensive course on EEG/fMRI co-registration. The latter
included a practical measurement in the MRI scanner at the
Affiliated Hospital of Sun Yat-Sen University as well as hands-on
sessions in which the participants corrected and analyzed the
recorded data
At the end of the workshop, Brain Products’ CEO Pierluigi
Castellone, awarded a prize to Dr. Zhiwen Tang of the
Management School JiNan University for the best scientific
poster presented by Brain Products customers at national and
Brain Products Inside
Who is who..? - Nicola Soldati
It is a pleasure to introduce myself as a new scientific
consultant for the Sales department at Brain Products.
My name is Nicola Soldati, and my background is in
telecommunication engineering, specializing in advanced
signal processing, machine learning and pattern recognition
applied to human brain neuroimaging.
Since 2007, I have been working on the acquisition of
neuroimaging data and the development of experimental
protocols for the investigation of human brain dynamics and
I hold a PhD in Cognitive Neuroscience at the CIMeC, University
of Trento, Italy. In particular my focus has been on developing
novel algorithms and software both for single modality
real-time fMRI and for multimodal data fusion of EEG and
fMRI data. Studying these arguments I became familiar with
Brain Products solutions for the simultaneous acquisition of
EEG and fMRI data, and I made extensive use of these.
www.brainproducts.com
While working on my PhD, I also gained
experience in neurofeedback effects for
the non-invasive study of human brain
plasticity and causality. I also got familiar
with state of the art analysis techniques
for intelligent biomedical signal processing
Nicola Soldati
and nonlinear system theory approaches
in computational neuroscience. My work gave me the chance
to collaborate with and visit some of the more advanced and
recognized laboratories across the world, such as the Mind
Research Institute at the University of New Mexico (USA), the
Martinos Imaging Center, MIT, and the laboratory of Advanced
Brain Signal Processing (RIKEN BSI, Japan).
I find the possibility of collaborating to find optimal solutions
that suit research problems very stimulating, and I hope that my
experience and my problem-solving oriented approach can help
to foster scientific research with Brain Products solutions.
page 12 of 13
Brain Products Press Release
April 2013, Volume 46
News in brief: Conferences
CNS Annual Meeting
San Francisco (USA), Apr 13th to 16th, 2013
in cooperation with Brain Vision LLC
Cognitive X
Istanbul (Turkey), Apr 19th to 21st, 2013
in cooperation with Inter Bilgisayar Elektronik
San Dıs Tic.Ltd.
ISMRM 2013
Salt Lake City (USA), Apr 20th to 26th, 2013
in cooperation with Brain Vision LLC
Aging and Cognition
Dortmund (Germany), Apr 25th to 27th, 2013
in cooperation with MES Forschungssysteme
NEURONUS 2013
Krakow (Poland), May 9th to 11th, 2013
in cooperation with ELMIKO MEDICAL sp. z.o.o.
DGPA (Psychologie & Gehirn) 2013
Wuerzburg (Germany), May 30th to Jun 1st, 2013
in cooperation with MES Forschungssysteme
and EASYCAP
SLEEP 2013
Baltimore (USA), Jun 1st to 5th, 2013
in cooperation with Brain Vision LLC
International BCI Meeting
Pacific Grove (USA), Jun 3rd to 7th, 2013
in cooperation with Brain Vision LLC
Human Brain Mapping
Seattle (USA), Jun 16th to 20th, 2013
in cooperation with Brain Vision LLC
30th Intern. Epilepsy Congress
Montreal (CAN), Jun 23rd to 27th, 2013
in cooperation with Brain Vision LLC
For more information on the conferences we are about to attend, please visit our website at www.brainproducts.com/events.php
News in brief: Downloads, Programs and Updates
Feb 22nd, 2013 / RDA Client for MATLAB®
A 64bit version of the RDA Client for MATLAB® is now also available in the Recorder Downloads Section:
www.brainproducts.com/downloads.php?kid=2&tab=5
All Updates and New Modules can be downloaded on our website at www.brainproducts.com/downloads.php. If you‘d like us to keep you posted
on any new Update for BrainVision Analyzer 2, please register for our Analyzer 2 Newsflash at www.brainproducts.com/a2_newsflash.php
This Press Release is published by Brain Products GmbH, Zeppelinstrasse 7, 82205 Gilching, Germany.
Phone +49 (0) 8105 733 84 0, www.brainproducts.com
Notice of Rights
All rights reserved in the event of the grant of a patent, utility model or design. For information on
getting permission for reprints and excerpts, contact [email protected]. Unauthorized
reproduction of these works is illegal, and may be subject to prosecution.
Notice of Liability
The information in this press release is distributed on as „As Is“ basis, without warranty. While every
precaution has been taken in the preparation of this press release, neither the authors nor Brain
Products GmbH, shall have any liability to any person or entity with respect to any loss or damage
caused or alleged to be caused directly or indirectly by the instructions contained in this book or by
the computer software and hardware products decribed here.
Copyright © 2013 by Brain Products GmbH
www.brainproducts.com
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