Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
Contents xiii
26 EEG in Focal Epilepsy and Its Role in the Management
of Adult Patients with Drug-Resistant Epilepsy . . . ............. 351
Boyuan Song, Umair J. Chaudhary, and Louis Lemieux
27 EEG Applications in Neonatal and Paediatric Clinical
Neuroscience . . . . . . . . . . . . . . . . . . ........................ 365
Jayvian Mavi and Kimberley Whitehead
28 Sleep . . . . . . . . . . . . . . . . ................................ 385
Masako Tamaki
29 Mobile Electroencephalography . . . . . . . . . . ................. 403
Olave E. Krigolson, Mathew Rocha Hammerstrom,
Katherine Boere
and
30 Understanding the Creative Brain in Action . . . . . . . . .......... 425
Aime J. Aguilar-Herrera, Maxine Annel Pacheco-Ramírez,
Yos
hua E. Lima-Carmona, Lianne Sanchez-Rodriguez,
and Jose L. Contreras-Vidal
Part VI Multimodal EEG Applications
31 Combining EEG and Transcranial Electric Brain Stimulation . . . . 45
1
Heiko I. Stecher, Sreekari Vogeti, Daniel Strüber,
Christoph S. Herrmann
and
32 TMS–EEG: A Tool to Probe Key Features of Human
Thalamocortical Circuits . . . . . . . .......................... 479
Silvia Casarotto and Mario Rosanova
33 Combining EEG and fMRI . . . . . . . . . . ..................... 503
David W. Carmichael, Rebecca Meagher, Anna Sadilova,
Tracy
Warbrick, and Cilia Jaeger
34 Bridging Brain and Body: Complementing EEG
with Peripheral Physiological Signals . . . . . . ................. 533
Ignacio Rebollo, Daniel S. Kluger, and Marie Loescher
Part VII Writing and Reading EEG Research Papers
35 Writing Up Your EEG Research . . . . . . . . . .................. 557
Shivakumar Viswanathan and Tracy Warbrick
36 How to Evaluate an EEG Research Paper . . . . . . . . ............ 569
Tracy Warbrick and Michael Hoppstädter
Index . . . . . . . . . . . . . . . . . . . ................................ 587
List of Figures
Fig. 1.1 Comparison of neuroimaging methods .............................. 6
Fig. 2.1 Representation of the generation of EEG signal during EPSP.
(a) A pyramidal neuron receives excitatory neurotransmitters at
synapses on different sites of the apical dendrite’s tuft. This
induces a positive charge inside the neuron due to the entrance of
positive sodium ions. The positive charge is propagated through
the apical dendrite towards the negatively charged soma of the
neuron. (b) Negative charges are produced in the extracellular
space close to the synapses. The negatively charged region forms
a dipole with the positive areas along the exterior of the neuron.
(c) The dipoles from multiple neurons aligned in parallel summate
to act as a larger dipole that can be detected on the scalp ......... 16
Fig. 2.2 Representation of dipoles produced in the cortex shown as double-
headed arrows and their orientation with respect to the surface. (a)
Radial dipoles produced in a gyrus are able to reach the surface,
and either their positive or negative side is recorded in EEG. (b)
Radial dipoles generated within the floor of a sulcus are deep and
not normally detectable with scalp EEG. (c) Tangential dipoles
produced at the walls of a sulcus that are faced with a dipole from
the opposing wall cancel out and cannot be recorded by EEG. (d)
A tangential dipole with the appropriate orientation that is not
opposed by another dipole is able to reach the surface; the two
poles would appear on opposite sides of the scalp ................. 18
xv
xvi List of Figures
Fig. 2.3 Sample EEG of single trials during visua l stimulation with a
checkerboard pattern reversal at 2 Hz frequency. The
corresponding ERP from the average of all trials is shown at the
bottom, known in this case as the visually evoked potential (VEP),
with its components N75, P100, and N145. The VEP is also
shown overlaid on the single trials to highlight how each trial
contains the ERP signal mixed with noise. The averaging process
reduces the randomly fluctuating noise in the trials, while
retaining the more consistent activity related to the brain’ s
response to form the ERP. Modulations in the amplitude and
latency of the ERP components can be used for research or clinical
purposes . ... .... ... ... .... ... ... . ... ... ... . ... ... .... ... ... ... .... ... ... 21
Fig. 2.4 Filtered EEG signals in the range of the different canonical bands
organized in descending order. The exact band range can differ
between authors ................................. ...................... 24
Fig. 3.1 (a) Organization of the central and perip heral nervous systems.
The CNS consi sts of the brain and spinal cord. The PNS consists
of an elaborate system of nerves that connect the periphery to the
CNS (read more in Chap. 4: “Basic Anatomy: Peripheral Nervous
System”). (b) Schematic of the pathway of the optic nerve (part of
the PNS) that carries visual information from the retina to the
occipital lobe at the posterior of the brain . .. .. ... .. .. ... .. .. ... .. .. 32
Fig. 3.2 (a) Chart showing the hierarchical organization of the human
brain. (Adapted based on Ward, 2015). The inset shows the main
lobes of the human brain. (b) Left: Gray-scale MRI image of an
adult human brain along three standard orthogonal planes
(sagittal, coronal, axial) of a coordinate system with an origin at
the center of the brain. The gray matter and white matter are
shown with darker and lighter shades of gray respectively. Right:
Fiber tracts of the corpus callosum linking the left and right
hemispheres. Tracts are colored by segment. (From Rosenbloom
& Pfefferbaum, 2008). (c) Electrode position layout on the scalp
according to the international 10–20 system ... ..................... 34
Fig. 3.3 (a) Example
of a Talairach coordinate grid placed on a brain slice.
(From Woodward et al., 2008). (b) Example of overlaid
geographic maps with different information enabled by a shared
coordinate system. (c) Map of cytoarchitecture properties at
different locations across the brain as described by Brodmann
(1909), now referred to as Brodmann areas ........................ 36
Fig. 4.1 Over
composed of the CNS (purple) and the PNS (black). The PNS can
be further divided into the somatic nervous system, consisting of
sensory input and motor outputs, and the autonomic nervous
system, which is composed of the sympathetic, parasympathetic,
and enteric nervous systems ... . ... ... ... . ... ... .... ... ... .... ... ... . . 42
view of the nervous system divisions. The nervous system is
List of Figures xvii
Fig. 4.2 Somatic nervous system. (a) Receptors from the peripheries
convert a stimulus into an electrical signal that propagates along
the myelinated sensory neuron to the dorsal root of the vertebrae.
From there, the signal can be directly transmitted to the efferent
motor neuron that innervates the effector muscle. The signal from
the afferent sensory neuron also synapses onto a secondary
sensory neuron in the spina l cord that transmits the signal to the
brain. (b) Afferent pathways consist of either the dorsal column or
the spinothalamic tracts. Efferent pathways are divided into the
anterior and lateral corticospinal tracts . ... .. . .. ... ... ... ... .. ... ... . 47
Fig. 4.3 Autonomic nervous system. The autonomic nervous system
consists of the sympathetic and parasympathetic nervous systems
that innervate different organs of the body. The efferent pathways
of the autonomic nervous system consist of two neurons: the
preganglionic and the postganglionic neurons. The preganglia
synapse onto the postganglia, whose cell bodies are located in the
paravertebral sympathetic ganglion chain ........................... 48
Fig. 4.4 Reflex arc and neurofeedback in electrophysiology. Nociceptors
in the skin of the hand detect sudden temperature changes. This is
converted to an electrical signal that is transmitted along the
afferent sensory pathway to the dorsal horn. Interneurons in the
vertebrae then transmit the electrical signal to the efferent motor
pathway. The signal is transmitted to the neuromuscular junction,
which causes contraction of the muscles in the forearm, effectively
pulling the hand away from a hot source. Electrodes on the
forearm can be used to measure muscle contraction in response to
the nociceptive stimulus. The painful stimulus is also transmitted
to the CNS, where it is consciously perceived and can be
modulated. For example, in neurofeedback training, the pain
response can be mindfully downregulated. The CNS transmits an
inhibitory signal along efferent pathways that activate inhibitory
neurons in the ventral horn and inhibit muscle contraction of the
forearm . ................................................................ 52
Fig. 5.1 Simp
lified schematic of the tissues surrounding the brain ... . .. ... 57
xviii List of Figures
Fig. 6.1 EEG features to characterize brain states. ( a ) Temporal or spectral
features can be extracted from the raw EEG data. In the timedomain, the voltage changes evoked in response to a stimulus are
averaged together across several repeated trials to measure the
event-related potential evoked by the stimulus. The raw EEG data
can also be broken down into its component frequencies to
investigate activity across neural oscillations. An event-related
time-frequency analysis can also be applied to study how the
brain’s oscillatory activity changes over time in response to a
stimulus. (b) EEG features can also be extracted spatially by either
looking at the spatial distribution of EEG signal properties across
scalp topography maps or by source localizing specific EEG
features . ................................................................ 65
Fig. 6.2 The circadian rhythm. The suprachiasmatic nucleus (SCN) of the
hypothalamus acts as the principal clock that regulates rhythmic
bodily processes such as sleep–wake cycles and appetite, as well
as rhythmic processes of the autonomic nervous system that are
important for homeostasis. The SCN neurons have a 24-h rhythm
that is generated through a feedback loop of clock gene
transcription and protein inhibition. The SCN then transmits
electrical signals to other brain regions, which are involved in
cognition, mood, and other neural processes ... .. .. . .. .. ... .. .. ... . 67
Fig. 6.3 Brain stat es characterized by differences in neural activity patterns
in the beta frequency. (a) Changes in beta power, within-area, and
between-area coherence have been observed in different brain
regions and are also linked with different cognitive processes. (b)
The location and type of beta-related activity change with respect
to content-specific information processing, for example, during
working memory. This figure highlights how activity patterns
across brain regions can overlap for different cognitive processes,
therefore emphasizing the importance of studying brain states and
dynamic neural activity patterns ... .. . .. ... ... ... .. . .. ... ... ... .. ... . 70
Fig. 7.1 Schema
experiment. The experimental timeline (top) shows example
events from two consecutive trials of an experiment. Event times
are shown by short vertical lines. The cognitive/neural timeline
(middle) shows intervals of neural activity (hatched lines) related
to each trial in the upper timeline. The EEG recording timeline
(bottom) shows a continuous EEG recording with one voltage
time-series (wavy line) per channel. Segments related to trials k
and k+
tic of three related timelines in a trial-based EEG
1 are highlighted .............................................. 78
List of Figures xix
Fig. 7.2 (a) Example segment timelines that are time-locked to the
stimulus (short vertical line at t = 0). On each segment, activity
immediately before (pre-) and after (post-) each event is shown
with differing hatching patterns. Time-points near the stimulus
event have comparable activity across segments, as shown for
t = +30 ms (poststimulus activity). However, this is not true
near the response event, as shown for t = +120 ms; some
segments have pre-response activity and others post-response
activity. (b) Timelines of segments time-locked to the response
event (short vertical line at t = 0). Time-points close to the
response event have comparable activity across segments
(t =-30 ms), but this is not true around the stimulus event
(t =-120 ms) ......................................................... 82
Fig. 7.3 (a) Activity based on signal magnitude at different time-points. At
time-points before ~60 ms, the signal magnitude is close to zero
(examples shown by arrows) but deviates away from zero from
~60 to 130 ms (examples shown by arrows). (b) The signal
magnitude at single time-points is close to zero and deviates away
from zero in quick succession (examples shown with arrows). (c)
The maximum signal magnitude over short time-intervals
(examples highlighted in gray) reveals a coherent pattern across
the entire signal duration (thick black line ) .. ... ... .. ... ... ... .. ... . 83
Fig. 8.1 Overview of the study design process ............................... 90
Fig. 8.2 Experimental designs considering groups and experimental
conditions and/or points of measurement ........................... 97
Fig. 9.1 Schema
tic of a typical EEG data analysis organization for
hypothesis testing. Raw data are obtained from individual
participants who are sampled from the population (left). They
undergo an individual-level or first-level analysis, which consists
of a preprocessing step followed by signal definition steps (see
text for details). The proces sed data from individual participants
are then pooled together for group-level or second-level analysis.
The group data are then used to evaluate statistical hypotheses by
applying statistical tests. The outcomes are numerical measures
evaluating these hypotheses .......................................... 108
xx List of Figures
Fig. 9.2 Schematic of statistical inference for a single variable. The
hypothesis space (upper plane) is the space of possible statistical
hypotheses where H1 and H2 are examples (black dots). For each
hypothesis, a probability is assigned to every possible dataset that
could be obtained under the effects of random chance if that
hypothesis were true. Each dataset is represented by a sample
statistic value, and the space of all possible sample statistic values
is shown in the middle plane. The shading indicates the
probability assigned to each sample statistic (dataset) by a
hypothesis. The alpha threshold for each hypothesis is shown as a
dotted line. The colored star indicates a sample statistic obtained
from the measured sample (bottom plane). It is assigned a
different probability by different hypotheses (probability < alpha
for H1 but > alpha for H2). The measured sample (bottom plane)
is a subset of the population. The sample mean (
standard deviation
(s), and sample size (N) are used to calculate
xÞ, sample
the statistic for the samp le .. .......................................... 112
Fig. 10.1 Preparing pilot testing flowchart. The flowchart illustrates the
process of preparing pilot testing, including defining the study’ s
goals, measurable outcomes, testing steps, and pilot sample
characteristics. The key experiment characteristics can be
optimized (from signal quality to questionnaires) by assessing the
suggested criteria (arrow connections). Almost all these goals can
have an impact on signal quality (except for the questionnaires).
The goals and outcomes help define the steps and the samples
used in pilot testing. Different phases are associated with different
sample groups, including lab members, control populations, and
experimental participants. These phases go from testing the
characteristics individually to simulating a data acquisition
session for thorough preparation ... .................................. 120
Fig. 10.2 Flow
chart of the main phases in pilot testing. Three phases of pilot
testing are outlined: preparation, execution, and post-testing steps.
The diagram presents the steps within each phase and their order.
The preparation phase (preparing) includes defining study goals,
evaluation criteria, steps of the testing, and samples used. The
execution (doing) requires testing individual characteristics,
partial setups, testing the experiment procedure, and then
simulating data acquisition session. It can require a return to
previous steps or even to the previous phase to prepare new pilot
tests. The steps after pilot testing involve publishing the results
(optional), preregistering the study, and then conducting the full
study. Optional steps and returns to previous steps are presented in
dashed lines ... . ... ..................................................... 125
List of Figures xxi
Fig. 11.1 The role an electronic lab notebook (ELN) can play in the research
data life cycle . ......................................................... 132
Fig. 12.1 The components of an EEG recording setup. (a) EEG electrodes
and peripheral physiology sensors are used to detect the signal. (b)
The amplifier converts the signal from analog to digital, amplifies,
and filters the signal. (c) Event markers can be co-registered with
the data using triggers. (d) Data (and triggers) are recorded using
dedicated software ... .. ... ... ... .. . .. ... ... .. . .. ... ... .. ... ... ... .. ... 142
Fig. 12.2 The 10–20 system for naming EEG electrodes (Jasper, 1958).
Electrode positions are defined by percentages of distances
between anatomical landmarks on the head. They are named with
a letter representing the lobe of the brain (F frontal, P parietal, C
central, T temporal, O occipital) and a number (odd numbers left,
even numbers right). The lines connecting anatomical landmarks,
as well as the circumference, are shown. These lengths are defined
as 100%. Electrodes are placed at distances of 10% or 20% of
these lines .............................................................. 146
Fig. 12.3 Placement of electrodes for peripheral electrophysiology
recordings. (a) Indicates placement of bipolar pair of electrodes
for vertical EOG, vEOG, recordings and horizontal EOG, hEOG;
(b) Placement of bipolar electrodes and ground (GND) electrode
for ECG recordings; (c) indicates muscle body tendon placement
for EMG recordings. The example shows the bicep muscle of the
arm; and (d) electrode placement for skin conductance with GSR
sensor . ... ... .. ... ... ... ... .. . .. ... ... ... .. . .. ... ... ... .. . .. ... ... ... .. . 147
Fig. 12.4 Referential and bipolar recording schemes for EEG and peripheral
physiology. (Part a) Shows a referential recording scheme where
the signal at each EEG electrode is recorded relative to a reference
electrode. Note that the distance differs between the reference
electrode and each recording electrode, this will influence the
signal recorded. The position of the reference electrode relative to
the EEG electrodes is therefore important. (Part b) Shows a
bipolar recording scheme for EMG. The position of the recording
electrodes relative to each other (rather than to a refence electrode
as in part a) will influence the recorded signal .. .................. 150
Fig. 12.5 Analog
to digital conversion: temporal resolution. An analog
signal perfectly resolved in time is shown in the left panel. A
digital signal is a sampled signal, obtained by sampling the analog
signal at discrete points in time. The higher the sampling rate, the
better the time resolution. The middle panel shows a signal
sampled every 0.4 s and the right panel shows a signal sampled
every 0.2 s. The signal in the right panel is closer to the original
analog signal than the digital .. .... ................................... 151
xxii List of Figures
Fig. 12.6 Analog to digital conversion: amplitude resolution. A continuous
signal gets recorded (& amplified) at regular amplitude intervals.
The higher the bits per volt, the higher the amplitude resolution.
The left panel shows the original analog signal. The middle panel
shows a signal sampled with an amplitude of 0.4. The right panel
shows a signal sampled with an amplitude of 0.2. The signal in the
right panel has a more fine-grained resolution and is closer to the
original analog signal ............................ ..................... 151
Fig. 14.1 Events, markers, and triggers in a visual paradigm. In this case, a
visual cue appearing on the screen is the event. The stimulus
presentation computer sends a trigger to the EEG amplifier at the
same time. The trigger gets recorded together with the EEG data.
The triggers are represented as markers in the recorded file ...... 164
Fig. 14.2 Effects of jitter and latency on an ERP ± confidence interval. A
stable latency changes the time of the ERPs' appearance in relation
to the event (in this case at t = 0 ms). Increasingly high jitter
directly affects the waveform. The higher the jitter, the more of the
original waveform is getting lost. The four examples demonstrate
the effects of latency and jitter, simulated with normally
distributed delays based on mean latency ± jitter in milliseconds.
(a) No latency and no jitter: the original and simulated ERPs
overlap perfectly. (b) Here, the mean latency is zero, but the jitter
has a high standard deviation of 50 ms. The waveform of the ERP
is noticeably different. (c) Constant, jitter-free latency: the ERP is
preserved but shifted on the time axis. The amplitude can also be
affected if baseline correction is used. (d) High latency and high
jitter significantly distort the original waveform and timing
information of the ERP ............................................... 166
Fig. 14.3 Trigger pulses. From left to right, the triggers shall be generat ed
on: the rising edge, caused by a stimulus presentation software
tool that sets the idle level to low and sends a short high pulse to
indicate an event; falling edge, where a button is pressed, pulling a
high level to low for a short time; on both edges, where each level
change is linked to an event . ......................................... 171
Fig. 14.4 Schema
tic of a timing verification experiment. A stimulus
presentation software is used to: generate a visual event on a
display (a), send a trigger to an EEG amplifier (b), and send an
LSL marker to the network (c). Ideally, all of these events should
happen simultaneously. The EEG data stream, including the brain
signals from the participant and the photo signal from a photo
sensor, is also sent via LSL and can be synchronized with the LSL
marker stream to analyze the timing between the events. The test
can be used with or without the LSL option ... ..................... 172
List of Figures xxiii
Fig. 15.1 EEG recording illustrating artifacts from eye blinks and horizontal
eye movements. The signal is referenced to Fz, and channel order
is from frontal to posterior regions. The data are high-pass filtered
at 0.1–40 Hz to enhance visibility of these characteristic slow
deflections. The topography of a representative saccade the typical
spatial distribution and amplitude of the related artifact, with
noticeable activity in frontal and temporal regions due to
extraocular muscle engagement . ..................................... 178
Fig. 15.2 EEG data illustrating a sequence of voluntary and spontaneous
eye blinks, recorded across 64 channels using the 10–20 electrode
montage. The signal was high-pass filtered at 0.1 Hz and low-pass
filtered at 40 Hz. The blink sequence consists of four voluntary
blinks, followed by three spontaneous blinks, and then five
additional voluntary blinks. A marked segment of the EEG trace is
shown on the right in a topographic mapping view, highlighting
frontal activity associated with blink artifacts. The topography of a
representative blink peak reveals the characteristic spatial
distribution and amplitude of the artifact, with prominent
activation in frontal regions ... ... .. . .. ... ... ... ... .. ... ... ... ... .. . .. 179
Fig. 15.3 Pulse artifact in EEG, every second or so, at nearly the same
moment across many of the channels, a sharp vertical spike
suddenly appears. These spikes are very brief, narrow, and point
upward or downward depending on the EEG’s polarity, like tall
thin peaks. They are much sharper and taller than the slower
rolling background waves, which makes them stand out clearly.
The topographic map of a single pulsation peak, displayed to the
right, illustrates the typical spatial distribution of pulse-related
artifacts . ... ............................................................. 181
Fig. 15.4 EMG artifacts in EEG—Participant was asked to clench the teeth
and mimic chewing behavior. Overall, the picture shows how
muscle contractions can dominate the recording, masking the
underlying brain activity with strong, sharp, and irregular patterns
of EMG. It clearly illustrates why muscle relaxation and
minimizing facial movements are important for obtaining clean
EEG data ... . ... ........................................................ 182
Fig. 15.5 Facial
muscle activity, such as frowning or raising the eyebrows,
generates high frequency artifacts in the EEG-data. These
movements are typically reflected as slower signal drifts, as shown
in the figure ............................................................ 184