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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
xxxiv List of Figures
Fig. 31.4 Schematic of a closed-loop brain stimulation system: A
participant or patient receives ongoing brain stimulation.
Neuroimaging data (e.g., EEG) is concurrently recorded and fed to
a control computer via remote data access, Lab Streaming Layer
(LSL), or a similar protocol. The control computer performs a
rapid analysis of brain activity, like curren t state, or power,
frequency or phase of a brain rhythm. Based on the extracted
features, the control computer either triggers the application of a
predefined waveform or updates a waveform and feeds it to the
attached stimulator via a digital-to-analog converter ............... 466
Fig. 31.5 Example of a tACS-artifact size in an EEG recoding. (a) A 110
seconds EEG recording during onset of tACS at 2 mA and 10 Hz,
recorded from electrode Pz, with stimulation applied over Cz and
Oz. The normal EEG signal is situated in the μV range, while the
tACS-artifact reaches a peak-to-peak amplitude of 10mV. The
gray regions depict 10 second windows for the FFT-spectra
below. (b) Semi-logarithmic plot of a normal EEG spectrum with
a visible alpha peak at 10.9 Hz. (c) Semi-logarithmic plot of an
EEG containing tACS. Notice the peak at the stimulation
frequency that is 3 orders of magnitude above any physiological
activity. Harmonics are visible at 20 and 30 Hz .................... 469
Fig. 31.6 Example of EEG signal drifting (naturally or by applied
stimulation into the limit of the amplifiers A/D range). In the
clipped region, all recorded samples have the same value. Any
physiological information is lost from this time period ... ... ... .. . 470
Fig. 31.7 Example of nonlinear component response in a stimulation + EEG
setup. The gray line shows the perfect digital square-wave, before
passing through the digital/analog converter, the stimulation
device, rubber electrodes, EEG electrodes, and the EEG amplifier.
The black line shows how this square was recorded in the EEG.
Note the distortions at both rising and falling edges of the
square . ... ......................... ..................................... 470
List of Figures xxxv
Fig. 32.1 Panel A (left; modified from Ziemann et al., 2026 10.1016/j.
clinph.2025.2111487) shows a schematic representation of the
main elements used to acquire TMS–EEG signals: a
neuronavigated TMS coil, a TMS-compatible EEG amplifier
receiving signals from an EEG cap with TMS-compatible
electrodes designed to reduce TMS-induced eddy currents, a
Graphic User Interface to visualize TEPs in real time, and
TMS-compatible ergonomic in-ear earphones playing noise
masking generated by a specifically devised tool Panel A (right;
modified from Ziemann et al., 2026 10.1016/j.
clinph.2025.2111487) displays an example of TEPs recorded
from the posterior parietal cortex (modified from Rosanova et al.,
2009), shown for one channel (thick light-green trace) and across
all channels as a butterfly plot (dark-green shaded area). Positive
(blue circles) and negative (red circles) peaks corresponding to
TEP components are marked. Background colors indicate the
prestimulus baseline (light blue), early components (light red),
and late components (light yellow). A symbolic representation of
oscillatory activity in four frequency bands (Fb1–Fb4, increasing
in frequency) is shown as rectangles with alternating light and
dark green shading. The inset illustrates immediate TEP
components recorded over M1. Panel B (left to right; modified
from Ziemann et al., 2026 10.1016/j.clinph.2025.2111487)
illustrates:Excitability measures—examples include the peak-topeak amplitude and slope of the first TEP component at the singlechannel level. Similar measures can be derived from the Local
Mean Field Power (LMFP) within the first 50 ms, such as the
Immediate Response Area (IRA) and Immediate Response Slope
(IRS) (modified from Casarotto et al., 2013; 10.1007/s10548-0120256-8). Cortical excitability can also be estimated at the source
level using indices such as the Significant Current Density (SCD;
modified from Casali et al., 2010) Oscillatory activity—schematic
representation of methods used to extract evoked and induced
TMS-related oscillations (time–frequency plots modified from
Rosanova et al., 2009; pipeline modified from Harquel et al.,
2024)Causality and connectivity—examples of local causality
measures (Phase Locking Factor, PLF) and connectivity indices
computed at the sensor level (directed Weighted Phase Lag Index,
dWPLI) and at the source level. (Significant Current Scattering,
SCS; modified from Casali et al., 2010) Perturbational
Complexity Index (PCI)—schematic representation of the main
computational steps ................................................... 482
Fig. 33.1 A general
scheme of the main EEG-fMRI data integration
approaches proposed in the literature, including purely
comparative, asymmetrical, and symmetrical techniques .......... 507
xxxvi List of Figures
Fig. 33.2 Schematic of EEG-informed fMRI. For an event of interest (e.g.,
the occurrence of a response error), a parameter value of an EEG
feature (e.g., the amplitude of an ERP) is extracted from every trial
that includes this event. With respect to fMRI, the onsets of these
events during the course of the experiment are known, and the
fMRI signal changes caused by hemodynamic responses
following the events are mathematically modelled. This is done
via convolution: the multiplication and summation of a vector of
zeros and ones representing the event onsets and the
hemodynamic response function (mathematically modelled fMRI
signal changes following an event). The result is the predicted
time course of fMRI signal changes which can then be statistically
compared with the observed fMRI signal for each voxel (volume
element) of a brain scan. In the case of EEG-informed fMRI, not
only is this model determined by event onsets and the
hemodynamic response function (blue model prediction), but the
expected hemodynamic responses are additionally parameterised
using the extracted EEG feature: signal changes following events
on trials with a large EEG response are scaled up as compared
with trials with smaller EEG responses (green model prediction).
Although beyond the scope of this chapter, for this analysis
knowledge of the hemodynamic response function is required and
maybe less certain in some applications such as epilepsy or in
neonates . ............................................................... 508
Fig. 33.3 Illustr
ation of fMRI-informed EEG source reconstruction. To
estimate the location and activity of active cortical patches in the
brain that lead to measurable EEG signal changes on the scalp,
forward or head models are constructed from individual MR
images. Here, volumes representing skin, skull, and brain tissue
have been extracted. Based on such a model and the EEG time
courses as well as corresponding scalp topographies (the patt ern
of EEG a ctivity as recorded on a participant’s head), EEG sources
can be inferred (inverse modelling). Statistical maps from a
standard fMRI analysis are used to further constrain possible
source constellations. The procedure used here for inverse
modelling computes a high number of dipolar sources distributed
across the brain, each of which is characterised by its position,
orientation (pointing direction of an arrow), and strength (as
indicated by colouring) . .............................................. 509
List of Figures xxxvii
Fig. 34.1 Recording of cardiac activity and heartbeat-evoked potentials
(HEPs). (a) Modified Einthoven’s triangle electrode placement
with the resulting bipolar leads I-III for the recording of the ECG,
and pulse oximeter finger clip for the recording of the PPG. (b)
Schematic ECG waveform as recorded by lead II, with PQRST
components marked. Each cardiac cycle can be divided into two
phases, systole (green) and diastole (blue). We show the
recommended ventricular definition, with phases lasting from
R-peak to end of T and from end of T to next R-peak (Aufan et al.,
2023; Caparco et al., 2025). (c) Schematic PPG waveform and
main components. The systolic peak appears delayed compared to
the ventricular depolarization marked by the ECG’s R-peak due to
the pulse arrival time. (d) Computation of the neural HEP: neural
(e.g., EEG) recordings are epoched into short time-windows timelocked to an event within the ECG such as the R-peak (left). Right
bottom: The average over epochs yields the HEP. Right top:
Locations within the cortex in which HEPs have been reported
(Engelen et al., 2023). Abbreviations: ECG electrocardiogram,
PPG photoplethysmography, EEG electroencephalography, HEP
heartbeat-evoked potential ........................................... 536
Fig. 34.2 Princi
ples of respiration-brain coupling. (a) In studies of
respiration-brain coupling, time-locked neural, respiratory, and
behavioural signals can be recorded simultaneously. (b) Neural
time series are oftentimes transformed into frequency space to
investigate neural oscillations (top). Respiratory phase is extracted
from the raw breathing signal (green and black lines in the middle
panel, respectively). Both neural and behavioural measures can
then be analysed according to the respiratory phase at which they
occurred (bottom). (c) Green arrows show the feedforward
initiation of respiratory dynamics originating in the preBötzinger
complex of the brainstem. Red arrows illustrate the feedback
connection by which the circulating airstream in the nasal cavity
triggers mechanosensory neurons. This phase-phase coupling (top
inset) is then propagated through higher-order brain areas so that
the phase of respiration drives the amplitude of neural responses
throughout the cortex .............. ................................... 541
xxxviii List of Figures
Fig. 34.3 Gastric anatomy, signal characteristics, and EGG recording
approaches. (a) Schematic of the human stomach showing the
location of the pacemaker region in the corpus, where interstitial
cells of Cajal initiate the gastric rhythm that propagates towards
the antrum and pylorus. ( b) Example of raw electrogastrography
(EGG) recording (black trace) and extracted gastric rhythm (blue
trace). The corresponding amplitude envelope (upper blue trace)
and instantaneous phase (bottom panel) are derived using the
Hilbert transform. (c) Power spectral density of EGG signals
across electrodes. The dominant peak at ~0.05 Hz reflects the
gastric slow wave, within the normogastric range (0.033–0.066
Hz). (d) Electrode configurations used in EGG research, ranging
from classic single bipolar montages (left), to multi-channel
arrangements (middle), to high-density electrode arrays (right) for
mapping gastric activity with higher spatial resolution . .. .. .. .. .. . 545
Fig. 35.1 Timeline of a typical peer review process. Authors (upper
timeline) submit the manuscript (1). The editor (middle timeline)
assesses the manuscript to determine suitability for the journal (2).
The manuscript can be rejected at this stage or sent onward to
reviewers (3). The reviewers (bottom timeline) evaluate the
manuscript (4) and send their recommendations to the editor.
Based on the recommendations, the editor can reject, accept (not
shown), or return to the authors for revisions (thick line) (5).
Authors revise the manuscript based on reviewer suggestions (6)
and resubmit it (1*). The process of evaluation/review/revision is
iterative and proceeds until the manuscript is either accepted or
rejected. The accepted manuscript is sent for production and
publication ............................................................. 560
Fig. 36.1 Example plots for event-related time domain data. The left panel
(a) shows an ERP at three midline electrodes (Fpz, Cz, Pz). Two
conditions of an oddball task are displayed as an overlay, targets
as a solid line, and standards as a dashed line. The right panel (b)
shows a selection of five individual trials of the target condition at
electrode Cz. The single trials are noisier and show more
variability ..................... ......................................... 578
Fig. 36.2 Exa
mples for topographies of EEG data based on the difference
between two ERPs (targets minus standards) in an oddball
experiment. The data is averaged within the time window
240–360 ms. The top panel (a) shows a 2D topography as a view
from the top in which all scalp electrodes are projected on the
same plane. The bottom panel (b) shows the same data projected
onto a 3D head model from two different angles. Positive values
are represented by warmer colours .................................. 580
List of Figures xxxix
Fig. 36.3 Classical example plots for (time-)frequency domain analyses.
The left panel (a) shows a typical frequency spectrum during a
resting task with closed eyes. The power in the alpha band (in
green) is elevated at occipital electrodes (Oz is displayed). The
right panel (b) shows a time-frequency representation based on
wavelet analysis. The x-axis refers to time, the y-axis to
frequency. The example shows ongoing activation in the alpha
band that is suppressed when a stimulus is presented (at 0 ms).
Higher power values are represented by warmer colours ... ... ... . 581
Fig. 36.4 Example plots for connectivity results. The left panel (a) shows
connectivity values plotted onto a 2D scalp map. Each value is
represented by a line between the respective electrode locations.
Darker line colour represents higher connectivity. The right panel
(b) shows the same data displayed as a connectivity matrix. Both x
and y-axis represent the channels and the connectivity value for
each channel pair is represented by colour, whereby yellow means
higher connectivity ... ... .............................................. 582
Fig. 36.5 Exa
mple plot from an EEG source analysis with the LORETA
algorithm. Three slices from different directions of the MNI 305
standard template are shown with the source activation displayed
as overlay. Higher activation is shown in more intense red ....... 583
List of Tables
Table 1.1 Common multimodal EEG applications .......................... 9
Table 4.1 Cranial nerves ... ... .. . .. ... ... ... .. . .. ... ... ... ... ... .. ... ... ... ... . 43
Table 7.1 Example of trial-specific structured information from a
participant in a hypothetical visual perception task (see text) . . . 76
Table 8.1 Trial and block checklist ... ........................................ 98
Table 9.1 Illustrative example of a numerical table prepared for a within-
subject t-test to evaluate a statistical hypothesis about the mean
difference in ERP magnitudes between two conditions (e.g., left,
right) at channel Pz at time + 200 ms ............................. 111
Table 11.1 Example of simple information that could be included in a lab
logbook . ... ... .. . .. ... ... ... .. . .. ... ... ... .. . .. ... ... ... .. ... ... ... .. 131
Table 12.1 Target impedance, estimated preparation time, and approxi mate
recording duration for commonly used combinations of
electrode type and conduction method . ........................... 144
Table 14.1 Three examples of how to divide and group 8 bits: One single
group of 8 bits to decode 0–255 values, two groups of 4 bits to
decode values of 0 – 15 each, and a 7-bit group with a single bit to
decode 0–127 values and an on/off state .. . ....................... 169
Table 15.1 Eye artifacts and tips for reducing their occurrence ... ........... 180
Table 15.2 EMG artifacts and suggested solutions ............................ 183
Table 15.3 Other physiological artifacts and optional solutions ............. 186
Table 15.4 Cable and electrode artifacts with troubleshooting strategies . . . 188
Table 19.1 Examples of early sensory ERP components ..................... 247
Table 19.2 Examples of long-latency sensory ERP components ............ 248
Table 19.3 Examples of later cognitive ERP components ... .. . .. ... ... ... ... 250
xli
xlii List of Tables
Table 20.1 Common modeling approac hes to solve the forward problem of
the EEG .............................................................. 258
Table 20.2 Shows a non-exhaustive collection of popular inverse methods
from the RLS family ................................................ 262
Table 22.1 Recommended filter settings from Zhang et al. (2024b) for the 7
ERP components in the ERP CORE (in Hz, with a slope of 12
dB/octave), separately for each of four different scoring
methods ................ .............................................. 294
Table 26.1 Ictal onset patterns on scalp EEG .................................. 354
Table 35.1 Typ
ical sections of a scientific article ............................. 559
Editor and Contributors
About the Editor
Tracy Warbrick is Head of Education and Scientific Communication at Brain
Products GmbH (Gilching, Germany). She holds a PhD in Psychology and has a
multidisciplinary background spanning psychology, sport and exercise science,
neuroimaging, and research methods. She has published widely in the fields of
cognitive neuroscience and neuroimaging and has extensive experience in experimental design, data acquisition, and analysis of EEG and fMRI data. She is also
strongly committed to teaching and learning and holds a postgraduate qualification
in further and higher education. In her current role, she leads the develo pment of
high-quality educational resources for the EEG and neurophysiology research community, working closely with researchers to foster collaboration between academia
and industry.
Contributors
Aime J. Aguilar-Herrera Department of Electrical and Computer Engineering,
University of Houston, Houston, TX, USA
Paulo Rodrigo Bazán Brain Products GmbH, Gilching, Germany
Katherine Boere Department of Exercise Science, Physical and Health Education,
The
University of Victoria, Victoria, BC, Canada
David W. Carmichael School of Biomedical Engineering and Imaging Sciences,
’s College London, London, UK
King
xliii
xliv Editor and Contributors
Silvia Casarotto Department of Biomedical and Clinical Sciences, University of
Milan, Milan, Italy
IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
Umair J. Chaudhary UCL Queen Square Institute of Neurolog y, University
Colleg
e London, London, UK
National Hospital for Neurology and Neurosurgery, University College London
Hospitals NHS Foundation Trust, London, UK
Jose L. Contreras-Vidal Department of Electrical and Computer Engineering,
Univers
ity of Houston, Houston, TX, USA
Fernando Cross Villasana Brain Products GmbH, Gilching, Germany
Diana E. Gherman-Nagy Neuroadaptive Human-Computer Interaction, Branden-
University of Technology Cottbus-Senftenberg, Cottbus, Germany
burg
Mathew Rocha Hammerstrom Department of Exercise Science, Physical and
Health
Education, The University of Victoria, Victoria, BC, Canada
Christoph S. Herrmann Exp erimental Psychology Lab, Department of Psychol-
Carl-von-Ossietzky Universität, Oldenburg, Germany
ogy,
Cluster for Excellence “Hearing for All”, Carl-von-Ossietzky Universität, Oldenburg, Germany
Research Center Neurosensory Science, Carl von Ossietzky Universitßt, Oldenburg,
Germany
Michael Hoppstädter Brain Products GmbH, Gilching, Germany
Cilia Jaeger Brain Products GmbH, Gilching, Germany
David Kadlec Brain Products GmbH, Gilching, Germany
Marius Klug Neuroadaptive Human-Computer Interaction, Brandenburg Univer-
of Technology Cottbus-Senftenberg, Cottbus, Germany
sity
Daniel S. Kluger Institute for Biomagnetism and Biosignal Analysis, University of
Münst
er, Münster, Germany
Alex Kreilinger Brain Products GmbH, Gilching, Ger many
Hannah Kreilinger Brain Products GmbH, Gilching, Germany
Olave E.
Krigolson Theoretical and Applied Neuroscience Laboratory, School of
Exercise Science, University of Victoria, Victoria, BC, Canada