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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
xxiv List of Figures
Fig. 15.6 Facial muscle activity associated with smiling and laughing
produces characteristic artifacts in EEG recordings. Typically
showing as slow signal drifts. The figure illustrates these
distortions across a subset of 64 EEG channels, highlighting their
widespread impact on scalp recordings. The artifacts are most
prominent in frontal and temporal regions, where the muscle
activity interferes with the EEG bandwidth ......................... 185
Fig. 15.7 EEG signal distortion caused by higher impedance values,
manifesting in increased sensitivity to high-frequency
interference. Electrode and cable movements due to whole body
movement introduce random waves of higher amplitudes, which
are further masked by 50 Hz line noise. A few EEG channels with
these described artifacts are marked ............... .................. 189
Fig. 15.8 Regular bumps in EEG recordings during running reflect head
motion-induced artifacts, with their amplitude and frequency
closely tied to the intensity and acceleration of movemen t.
Channels are arranged from frontal to posterior regions,
progressing from the left to the right hemisphere. The artifact is
especially visible in the second half of the window on the frontal
electrodes ....... ....................................................... 190
Fig. 15.9 Cable movement artifacts, compounded by 50 Hz line noise, often
result from higher electrode impedance. Mechanical disturbances,
like vibrations, tugging, or loose connections, can introduce
voltage shifts ... .. . .. ... ... .. . .. ... ... .. . .. ... ... .. ... ... ... .. ... ... ... 191
Fig. 17.1 Magnitude response plot of a 4th-order IIR Butterworth low-pass
filter, based on the half-power cutoff definition. About 70.7% of
the original amplitude is retained at the cutoff frequency ......... 216
Fig. 17.2 Topographic maps of blinks and eye movements. (a) Blinks are
prominent bell-shaped deflections over the data. They show a
predominantly frontal topography. (b) Lateral eye movements
show a box-shaped morphology, appear strongest in frontopolar
and frontotemporal channels, and typically show opposite polarity
on each side of the scalp. Note: the interval between the vertical
green lines is 1 s .. . .. ... .. . .. ... ... .. ... .. . .. ... ... .. ... ... .. ... ... .. . 217
Fig. 18.1 Pendulu
m motion and corresponding sinusoidal oscillation. (a)
Pendulum oscillating in a vertical plane, with its bob’s
displacement on the x-axis restricted to the range -A to A. (b )A
sinusoidal function describes the pendulum’s movement along the
x-axis within this range .......................................... ..... 229
List of Figures xxv
Fig. 18.2 Composite of sinusoids at 5, 10, and 15 Hz and corresponding
frequency representation (a). Sinusoids at three different
frequencies: 5, 10, and 15 Hz. (b) Composite signal obtained by
summing the three sinusoids in panel a. (c) Power spectrum of the
composite signal shown in panel b, showing peaks at the
corresponding frequencies ... ......................................... 231
Fig. 18.3 Dot product between two vectors at different angles. Each panel
illustrates the dot product between vectors A (red) and B (blue).
The green vector represents the projection of A onto B,
quantifying the component of A that lies in the direction of B. As
the angle θ increases, the projection shortens and can become
negative, as seen in panel c. (a ) θ = 30 ° (π/6) (b) θ = 90 ° (π/2)
(c) θ = 150 ° (5π /6) .................................................. 232
Fig. 18.4 Representation of the Fourier analysis of sinusoids. (a) A 3 Hz
sine wave (left) and its spectrum (right), showing a single peak at
3 Hz. (b) An 8 Hz sine wave (left) and its spectrum (right),
showing a single peak at 8 Hz. (c) The sum of the 3 Hz and 8 Hz
sine waves (left). The corresponding spectrum (right) displays two
peaks, reflecting the presence of both frequency components . . . . 236
Fig. 19.1 Schematic of the ERP logic. Each trial contains ongoing task-
unrelated background activity and evoked activity that is due to
the stimulation. Averaging over many repetitions reduces the
ongoing activity and amplifies the evoked activity in the final
ERP .................................................... ................ 240
Fig. 19.2 A simplified overview of the waveform pattern of ERP
components. The figure is based on an auditory oddball paradigm
and the ERP at CPz is displayed. Sensory components and a P3
are visible. Note that negative is plotted downward. The
approximate time ranges in which the different classes of ERP
components fall are indicated at the bottom ........................ 245
Fig. 19.3 Overview of typical measures extracted from ERPs based on a P3
component. From the peak of a component, both the peak
amplitude (a) and the peak latency (b) can be determined.
Aggregated measures like the mean amplitude (c) or the area
under the curve (d) can be computed within a defined time
window around the component. S Stimulus, A Amplitude,
t Time .................................................................. 252
Fig. 20.1 Schema
The right-to-left arrow represents the EEG voltage generation
model; source dipole currents on the right spread throughout the
tissue, producing a voltage pattern on the left. The left-to-right
arrow indicates the inverse problem: the estimation of the most
likely current source pattern that could h ave generated the
observed EEG voltage scalp pattern ................................. 259
tic representation of EEG forward and inverse problems.
xxvi List of Figures
Fig. 20.2 Typical EEG source analysis flowchart. The source model
determines the subsequent pipeline. If the model is discrete, we
need to go the route of dipole modeling and clustering and decide
how to perform subsequent single-participant and group analyses
on dipoles or clusters of dipoles. If the source model is distributed,
we build a participant-specific or template head model and
calculate the lead field using a forward solver (see Table 20.1).
Next, we use an inverse solver to estimate the source maps for
selected time points of interest in the EEG (see Table 20.2). Then,
we can perform connectivity analysis on the source time series.
Statistical analyses can be performed on sources or connect ivity
features at the participant level or in groups. An important caveat
for group analyses is that unless we use the same template head
model for all participants, we need to co-register participant
source spaces to ensure that the features on which we perform
statistics correspond to the same anatomical structures ............ 260
Fig. 20.3 Statistical inference in the source space. From left to right, the
figure displays a collection of EEG topographies corresponding to
the same latency but a different trial. Then we use the inverse
solver to estimate single-trial source maps. Afterwards, the
estimated source maps are collected into a matrix of sources by
trials, which can be used for statistical inference. Note that in this
case we group the single-trial EEG topographies into the columns
of matrix V; consequently, the error term uses the Frobenius norm
(kV - KJk
) instead of the Euclidean norm, and we solve for the
F
matrix J, where each column represents the corresponding single-
trial source estimate ................................................... 264
Fig. 21.1 Schematic of a basic online visualization of the acquired EEG
signals. In parallel, a classifier translates the brain activity into a
bar graph that the participant is trying to control. Successful (and
unsuccessful) control attempts are immediately shown as
feedback, which can in turn alter brain activity .................... 271
Fig. 21.2 Select
ion of possible choices when designing an experiment and
deciding what kind of hardware and software to use. The choices
can be affected by many different factors (e.g., project-related
requirements, personal experience, or simply by having access to
specific equipment). In this example, the selected setup for the
experiment is driven by practical considerations such as reputation
and availability in the lab, but also the fulfillment of certain
requirements, such as the electrode and ampl ifier type, data
quality, sampling rate, and number of channels. Raw data can be
accessed via an LSL connector provided by the manuf acturer, and
a mix of E-Prime
®
and MATLAB® is used to train, visualize, and
analyze data. Refer to Chap. 12: Hardware for Recording EEG
and Peripheral Physiology and Chap. 13: Software for Recording
EEG and Peripheral Physiology for more information . .. . .. . .. . .. 276
List of Figures xxvii
Fig. 22.1 (a) Averaged ERPs for the oddball trials for one participant in an
oddball experiment in which each participant pressed one button
for frequently occurring digits and another button for rarel y
occurring letters (or vice versa). Time zero is stimulus onset. (b)
Single-trial EEG epochs from the oddball trials that were averaged
together to create the waveform in panel A. If the amplitude of the
P3 wave is scored as the mean voltage between 300 and 500 ms in
the averaged ERP waveform, the analytic standardized
measurement error (aSME) can be computed by measuring the
mean voltage between 300 and 500 ms in the single-trial EEG
epochs and applying the equation shown between panels A and B.
(c) Illustration of the concepts of precision, accuracy, and bias for
measuring the weight of an object. (d) Conceptual approach for
understanding the SME, in which the same participant is tested in
10,000 different replications of the oddball experiment (assuming
no fatigue or learning). For each replication, the oddball trials are
averaged together, and the mean voltage between 300 and 500 ms
is measured from the averaged ERP waveform. The histogram
shows the P3 amplitude scores obtained from each of these 10,000
replications. The SME is the standard deviation (SD) of these
10,000 scores .......................................................... 280
Fig. 22.2 Example of how the effect of noise on a score depends on the
scoring method. (a) ERP waveform with no noise. (b) Sa me
waveform as A, but wi th high-frequency contamination added.
The high-frequency noise distorts the peak amplitude between
300 and 500 ms but has relatively little effect on the mean voltage
during this measurement window .. . .. .. ... .. ... .. .. ... .. ... .. .. ... . 288
Fig. 22.3 Boo
tstrapped standardized measurement error (SME) values from
the seven ERP components in the ERP CORE dataset
(Kappenman et al., 2021), as derived by Zhang and Luck (2023).
The number of trials per condition is given for each component.
The SME values were obtained via bootstrapping from the
difference wave used to isolate a given component (e.g., oddball
minus standard for P3b). Separate SME values were obtained
from each participant at the optimal electrode site for each
component, and these SME values were then averaged across
participants, with error bars indicating the standard error of the
mean SME value across participants. Separate SME values are
shown for two different amplitude scoring methods (a: timewindow mean amplitude and peak amplitude) and for two
different latency scoring methods (b: 50% area latency and peak
latency) . ... .... ... ... .... ... ... .... ... ... .... ... ... .... ... ... ... . ... ... . 290
xxviii List of Figures
Fig. 22.4 Effects of low-pass and high-pass filters on real and artificial ERP
waveforms. (a) Averaged ERP wave form from an actual research
participant, with and without the application of a noncausal
Butterworth low-pass filter with a 5 Hz half-amplitude cutoff and
a slope of 48 dB/octave. (b) Artificial ERP waveform, with and
without the same low-pass filter as in (a). (c) Averaged ERP
waveform, with and without the application of a noncausal
Butterworth high-pass filter with a 2 Hz half-amplitude cutoff and
a slope of 12 dB/octave. (d) Artificial ERP waveform, with and
without the same low-pass filter as in panel C. Note that both the
low-pass and high-pass filters reduce the size of the signal as well
as reducing the noise. Note also that the high-pass filter produce s
artifactual peaks (highlighted with blue circles) that are more
easily observed in the artificial waveforms ... .. .. ... .. ... .. .. ... .. . 293
Fig. 23.1 Example of the subtraction method applied to alpha (8–12 Hz)
power during an eyes-closed and an eyes-open condition. On top,
the spectrum plot shows a clear power reduction effect after eye
opening. When mapping alpha over the scalp, anterior alpha levels
common to both conditions occlude the alpha reduction
localization. The contrast is created by subtracting the spectra of
the eyes-closed from the eyes-open condit ion. This eliminates the
common anterior activity and isolates the alpha power reduction
effect to the occipital region ......................................... 308
Fig. 24.1 EEG features as biomarkers. Different EEG features such as
event-related potentials, frequency patterns of neural oscillations,
and EEG connectivity can be used as potential biomarkers. These
features can be used as biomarkers. Diagnostic biomarkers are
used to identify diseases within a population. Prognostic
biomarkers are classified as biomarkers that indicate the
probability of a disease occurring or progressing. A predictive
biomarker helps identify the probability of a group of individuals
that respond to a treatmen t .. ... ... .. ... ... ... .. ... ... ... .. . .. ... ... .. 320
Fig. 24.2 Exa
mple demonstrating real-time analysis of the bispectral index.
(a) The raw EEG data is analyzed in near real time to monitor the
level of anesthesia and adjust the level of drug administration. (b)
The bispectral index is a value ranging from 0 (no brain activity)
to 100 (fully awake). The BIS value increases as the brain wave
amplitude decreases and the frequency increases. The awake state
has a BIS index of 90 and is characterized by small amplitude, fast
frequency waves. Deep anesthesia occurs around a score of 30 and
is characterized by large amplitude and slow frequency
oscillations. Part B of this figure has been reproduced by Yousefi-
Banaem et al. (2020) with permission .... ........................... 323
List of Figures xxix
Fig. 26.1 The 2025 updated ILAE classification of seizure types categorise
them into further subtypes based on consciousness i.e., preserved
consciousness seizure or impaired consciousness seizure. If a
seizures starts as focal and then propagates to become a tonic
clonic seizure it is classified as focal to bilateral tonic clonic
seizure. Under unknown, the subtypes are preserved
consciousness seizure, impaired consciousness seizure, and
bilateral tonic-clonic seizure. Under generalized, the subtypes are
absence seizures, generalized tonic-clonic seizures, and other
generalized seizures ................................................... 352
Fig. 26.2 Patterns of seizure onset frequently observed on scalp EEG. (a)
Rhythmical activity evolving theta, delta, alpha frequencies; (b)
Rhythmical spiking; (c) Spike waves; (d) Electro-decremental
onset characterized by low-voltage fast activity: (e) Clinical
seizure without a clear EEG correlate ... ... ... ... .. ... ... .. . .. ... ... 355
Fig. 26.3 The eight seizure onset patterns from SEEG, with red asterisks
marking the seizure onset point. (a) Low-voltage fast activity
(LVFA). (b) Preictal spiking that transitions into LVFA. (c) A
brief burst of polyspikes—high-frequency (>12 Hz), highamplitude, short-duration activity lasting less than 5 seconds—
followed by LVFA. (d) A slow wave or baseline shift (comparable
to a DC shift), which precedes LVFA. (e) Rhythmic spikes and
spike-wave discharges at low frequencies above 6 Hz and
consistently below 14 Hz. (f) Sharp theta or alpha activity,
represented by low-frequency sinusoidal waveforms. (g) Sharp
beta-frequency activity with beta-band sinusoidal oscillations. (h)
A delta-brush pattern characterized by bursts of low-amplitude,
rapid gamma-frequency activity superimposed on low-frequency
delta sinusoidal waves ............... ................................. 356
Fig. 26.4 Exa
mples of icEEG implantations. (a) A brain model with a
mixed subdural grid, where yellow dots represent individual
contacts, and orange and blue dots indicate the position of depth
electrodes. (b) A T1 MRI image showing SEEG implantation
targeting the right orbitofrontal and mesial frontal regions and
cingulum, marked by yellow dots . .................................. 359
xxx List of Figures
Fig. 27.1 Illustrative data showing how an episode of subtle bradycardia is
preceded by a hypopnoea an d EEG attenuation. Top panel:
Heartbeat (RR) intervals (asterisks) extracted from a 19 min
recording segment from an infant of 35 weeks corrected
gestational age (gestational age + postnatal age) using the
EEG-Beats toolbox. Note the RR intervals variability, including
intermittent bradycardias with one example shaded pink. Bottom
panel: EEG, electrocardiography (ECG) and respiratory
movement (Resp) recordings associated with the shaded example
bradycardia. The infant is awake, 3 min before a transition into
REM sleep. No filters applied except 50 Hz notch filter; DC offset
removed. Midline central referential EEG montage ................ 368
Fig. 27.2 Examples of annotated events occurring during home EEG
monitoring. Annotations collated from a deidentified research
database of multiple home-video EEG sessions carried out in
paediatric populations at King ’ s College Hospital. For illustrative
purposes, annotations are superimposed onto a 20 min segment of
EEG, electrocardiography (ECG), and electromyography (EMG)
activity from a patient of 10 years of age undergoing 24-h homevideo EEG monitoring. The child is awake. Midline central
referential EEG montage . ... .. ... ... .. ... ... .. . .. ... .. . .. ... ... .. . .. . 371
Fig. 28.1 Exa
sleep. (a) An example hypnogram. The hypnogram depicts the
cyclic alternation between NREM and REM sleep roughly every
90 min. N3 predominates early in the night, whereas the
proportions of N2 and REM sleep increase in the latter half of the
night. (b) Representative recordings of EEG, horizontal and
vertical EOG, and EMG across different sleep stages, including
wakefulness, N1, N2, N3, and REM. During wakefulness, alpha
waves (8–13 Hz) appear continuously. N1 is a drowsy state in
which alpha activity diminishes and theta waves (4–8 Hz) emerge
in EEG signals, while EOG shows slow eye movements (SEMs).
N2 is characterized by sleep spindles (11–16 Hz) and
K-complexes. N3 shows large, synchronous slow waves (0.5–4
Hz). During REM sleep, EEG signals exhibit mixed highfrequency, low-amplitude activity resembling that of N1 or
wakefulness. EOG displays periodic bursts of rapid eye
movements, whereas EMG recordings show markedly reduced
muscle tone. Negative polarity is up in the scale. hEOG horizontal
EOG, vEOG vertical EOG ........................................... 387
mples of a hypnogram and physiological recordings during
List of Figures xxxi
Fig. 28.2 Asymmetric sleep related to the first-night effect in humans. (a)
Interhemispheric differences in slow-wave activity in the default
mode network. Red, left hemisphere; blue, right hemisphere. (b)
Relationship between the sleep onset latency and the asymmetry
index on Day 1. (c) Relationship between the sleep onset latency
and the asymmetry index on Day 2 .. . .............................. 391
Fig. 28.3 E/I balance during sleep. (a) The E/I balance changes during
NREM (orange) and REM (blue) sleep. During NREM sleep, the
E/I balance is significantly higher than during the wake baseline,
whereas during REM sleep, the E/I balance is significantly lower
than the wake baseline. (b) The correlation between E/I balance
changes during NREM sleep and offline performance gains. Red,
NREM + REM group. Gray, NREM-only group. (c) The
correlation between E/I balance changes during REM sleep and
stabilization of learning . .............................................. 395
Fig. 28.4 CSF dynamics during sleep. (a) CSF signal changes to slow
waves and sleep spindles during light NREM sleep. During light
NREM sleep, sleep spindles were correlated with significant CSF
signal changes at 6 s after onset, whereas slow waves preceded a
large CSF signal peak at 8 s after onset. (b) CSF signals changes to
slow waves and sleep spindles during deep NREM sleep or slowwave sleep. During slow-wave sleep, slow waves triggered CSF
signal changes peaking at 5.5 s, in which latency was significantly
shorter and smaller in amplitude. Sleep spind les preceded CSF
signal changes at 4 s after their onset, which lasted for
approximately 10 s . ................................................... 397
Fig. 28.5 Brain regions recruited during various stages of sleep and for
arousals. The red-highlighted parts indicate brain areas activated
to slow waves during light NREM (top row) and slow-wave sleep
(the second row), rapid eye movements during REM sleep (the
third row), and arousals (the bottom row ). Light NREM sleep
activates sensory and motor regions including the parietal and
visual cortices and cerebellum. Slow-wave sleep engages regions
involved in plasticity and homeostatic processing including the
prefrontal and hippocampal regions, striatum, and amygdala.
REM sleep recruits visual and plasticity circuits including the
visual areas, the thalamus, the hippocampus, the striatum, and the
amygdala. Arousals accompanied widespread brain activation not
localized to specific brain regions ................................... 398
Fig. 29.1 A “traditional” EEG system, the Brain Products actiChamp
(Brain Products GmbH, Gilching, Germany) . ... ... ................ 404
Fig. 29.2 A “mobile” EEG system, the Brain Products X.on (Brain
Products GmbH, Gilching, Germany) ............................... 405
Fig. 29.3 EEG
being recorded while riding a bike ... . .. ... ... .. ... ... .. ... ... 406
xxxii List of Figures
Fig. 29.4 The Muse S Athena. A combined EEG and fNIRS system ....... 407
Fig. 29.5 The P300 measured at Pz (top panel) TP9 and TP10 (middle
panel) with a Brain Products ActiChamp and an InterAxon Muse
(bottom panel). (From Choosing Muse, Krigolson et al., 2017) . . 412
Fig. 29.6 P300 amplitude is reduced following a simulated 12 h night on
call . .... ... ... ... . ... ... ... . ... ... ... .... ... ... ... . ... ... ... . ... ... ... ... 414
Fig. 29.7 Exercising outside enhances P300 amplitude after a 15 min
walk .......................... .......................................... 416
Fig. 29.8 Decreased EEG beta power predicts baseball batting
performance . ... .. ... ... ... .. ... ... ... .. ... ... ... .. ... ... ... .. . .. ... ... 417
Fig. 30.1 Mobile Brain/Body Imaging (MoBI) system setup, experimental
workflow in representative art-science performances. (a) Standard
MoBI configuration with 32-ch EEG (BrainAmp DC, 28 scalp + 4
EOG, 1 kHz) and IMUs (APDM Opal, 9-axis, 128 Hz), housed in
a wearable pack adapted to each performance. (b) The Slowest
Wave performance with. (c) Brain On Nature project. (d) Balinese
Gamelan, Brain, Mind and Body performance in collaboration
with Udayana University and Institut Seni Indonesia (ISI)
Denpasar. (e) Typical experimental timeline illustrating
impedance check, resting baseline (eyes open/eyes closed), and
the experimental task phase ... ... ... ... ... .. ... ... ... ... ... .. ... ... .. 428
Fig. 30.2 Mobile Brain/Body Imaging (MoBI) system architecture. A
typical setup integrating EEG (BrainAmp DC, Brain Products
GmbH, Gilching, Germany), IMUs (APDM Opal), audiovisual
recording, and a synchronization layer (SyncBox) to enable
multimodal, time-aligned data collection in naturalistic
environments. The system is adapted per study to align with task
demands, movement constraints, and aesthetic requirements . . . . . 430
Fig. 30.3 A visual
timeline illustrating the Brain + Arts collaborative
projects from 2014 to 2025 .......................................... 433
Fig. 30.4 Multim
odal data processing pipeline for MoBI studies in
naturalistic settings. The framework supports synchronized
preprocessing and artifact removal across video, EEG, EOG, and
IMU signals. It consists of four stages: Stage 0 (Data
Understanding and Annotation), Stage 1 (Eye Artifact Removal),
Stage 2 (Motion Artifact Removal), and Stage 3 (Source-Space
Artifact Removal). This full pipeline is used for offline data
cleaning and analysis. A simplified real-time implementation
using H-infinity ANC is currently employed during live
performances for visualization, but is not yet configured for
closed-loop neurofeedback or BCI control ......................... 440
List of Figures xxxiii
Fig. 31.1 Grand average power at electrode POz before, during, and after 20
min of 10 Hz tACS (B) or sham (A) during a visual oddball task.
The orange lines depict the average power before stimulation, the
gray lines depict recovered power during stimulation, and the
green lines depict the power after stimulation. While there are no
significant differences during sham stimulation around 10 Hz
between conditions during sham stimulation, during tACS an
increase is visible during (ISI) and after tACS stimulation
(adapted from Helfrich et al. (2014b) and published with
permission by Elsevier) . .............................................. 455
Fig. 31.2 Average power at electrode Pz before and after 20 min of IAF
tACS (top) and sham (bottom) during an eyes-open auditory
detection task. The dashed lines indicate the pre-stimulation
power, solid lines indicate post-stimulation power, and the shaded
areas represent the standard error of the mean. Even after sham,
alpha power increases over time from pre- to post-intervention
(blue spectra). After tACS, this increase is significantly stronger
(red spectra). This nicely illustrates why a sham group is necessary
for tACS (adapted from Neuling et al. (2013) under the Creative
Commons Attribution License (CC BY 3.0)) .. .. .. . . . . . .. .. .. .. .. . 459
Fig. 31.3 Basic
principle of dividing spectral data into periodic and
aperiodic components. (a) Original total spectrum composed of
aperiodic activity (1/f-floor) and two oscillatory signals (visible as
peaks in the spectrum). The magnitude of the peaks represents the
magnitude of the periodic activity plus the underlying noise floor.
(b) The same spectrum as viewed on a double-logarithmic scale
(blue line). On this scale the aperiodic noise floor resembles a
straight line. Through iterative fitting procedures, the total
spectrum is divided into an aperiodic fit (red) and an oscillatory fit
(yellow). (c) Subtracting the aperiodic fit from the total spectrum
(transparent blue curve) yields the corrected purely periodic
spectrum (solid blue), from which we can now extract the
corrected peak-frequency and peak-power values for further
analysis of periodic activity. (d) The aperi odic fit (red) can be used
for the characterization of the pink-noise activity: We can extract
the offset ( log
of the intercept in log-log space) and the
10
exponent (-slope in log-log space) of the pink-noise activity . . . . . 463