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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Preface
- •Acknowledgments
- •About This Book
- •Contents
- •List of Figures
- •List of Tables
- •Editor and Contributors
- •1.3 EEG and Other Neuroscience Methods
- •1.4 The Future of EEG
- •1.1 EEG Technology: Past to Present
- •1.2 What Do We Know About the EEG Signal?
- •1.5 Conclusions
- •References
- •2.1 Physiological Origins of the EEG
- •2.2 Signals of the EEG
- •2.3 Concluding Summary
- •References
- •3.1 Introduction
- •3.2 General Organization
- •3.3 Finding Your Way Around: Brain Atlases
- •3.3.2 Talairach Atlas and MNI Coordinates
- •3.3.4 Accessing and Using Atlases
- •3.4 Putting into All Together
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Overview of the Peripheral Nervous System
- •4.3 Basic Anatomical Unit of the PNS: Ganglia and Nerves
- •4.4 Anatomy of Somatic Nervous System
- •4.4.1 Receptors
- •4.4.1.1 Vision
- •4.4.1.2 Audition
- •4.4.1.3 Vestibular System and Balance
- •4.4.1.4 General Sensory Modalities
- •4.4.2 Somatic Sensory System
- •4.5 Anatomy of the Autonomic Nervous System
- •4.5.1 Sympathetic Nervous System
- •4.5.2 Parasympathetic Nervous System
- •4.6 Cranial Nerves
- •4.7 Function of the PNS and CNS as a Unit
- •4.8 Concluding Remarks
- •References
- •5.1 Introduction
- •5.2 Head Anatomy and Signal Propagation
- •5.3.1 The Eyes and Ocular Potentials
- •5.3.2 Facial Muscles and EMG
- •5.3.3 Sweat Glands and Skin Potentials
- •5.3.4 Blood Vessels and Heartbeat
- •5.4 Conclusion
- •References
- •6.1 Introduction
- •6.2.1 What Is a Brain State?
- •6.2.2 Brain States Measured with EEG
- •6.3 Examples of Brain States
- •6.3.1 Awake State Sleep State
- •6.3.2 Consciousness States: Presence Loss (Anesthesia)
- •6.4 Pathological Brain States
- •6.4.1 Traumatic Brain Injury
- •6.4.2 ADHD
- •6.5 Framework of Brain States
- •6.6 Concluding Summary
- •References
- •7.1 Introduction
- •7.3 Identifying Task Processes Within a Trial Segment
- •7.3.2 Cross-Trial Comparability and Flexible Time-Locking
- •7.4 What Does Activity Look Like on the Timeline?
- •7.5 Conclusion
- •References
- •8.1 Introduction
- •8.2 Setting a Research Question
- •8.3 Setting a Hypothesis
- •8.3.2 Testing the Hypothesis
- •8.4 Design of the Study
- •8.4.1 Contextualization of the Hypothesis
- •8.4.1.1 Experimental Paradigm
- •8.4.1.2 EEG Index
- •8.4.1.3 Group/Sample
- •8.4.2 Implementation of the Study
- •8.4.2.1 Paradigm/Task Implementation
- •8.4.2.2 Measurement Precision
- •8.4.2.3 Experimental Protocol
- •8.4.2.4 Pilot Testing
- •8.5 Concluding Summary
- •References
- •9.1 Introduction
- •9.2 Why Is Statistics Needed in EEG Research?
- •9.3 When Is Statistics Applied During EEG Data Analysis?
- •9.3.1 Raw EEG Data
- •9.3.2 Individual-Level (First-Level) Analysis
- •9.3.3 Group-Level (Second-Level) Analysis
- •9.3.3.1 Statistical Hypotheses
- •9.3.4 Application of Statistical Inference
- •9.3.5 Interpretation and Inference
- •9.4.1 Hypotheses (Upper Plane of Fig. 9.2)
- •9.4.2 Population and Sample Data (Bottom Plane of Fig. 9.2)
- •9.4.3 Sample Statistic (Middle Plane of Fig. 9.2)
- •9.5 Conclusion
- •References
- •10.1.1 Why Pilot Testing Matters
- •10.2 How to Prepare and Run the Pilot Testing
- •10.2.1.1 Signal Quality
- •10.2.1.2 Task Parameters
- •10.2.1.3 Instructions
- •10.2.1.4 Participant Experience
- •10.2.1.5 Equipment Setup
- •10.2.1.6 Procedures
- •10.2.1.7 Questionnaires
- •10.1 What Pilot Testing Is
- •10.3 Concluding Summary
- •References
- •11.1 Introduction
- •11.2 Lab Management
- •11.2.1 Admin and Organisation
- •11.2.2 Hardware and Software Maintenance
- •11.2.3 Lab Logbook
- •11.3 Keep Your Own Lab Notebook
- •11.4.1 Pre-measurement
- •11.4.2 Measurement
- •11.4.3 Post Measurement
- •11.5 Conclusion
- •References
- •12.1 Introduction
- •12.2 Components of the System
- •12.2.1 Detecting the Signal: EEG Electrode Technology
- •12.2.1.1 Passive Electrode Plus Gel
- •12.2.1.2 Active Electrodes Plus Gel
- •12.2.1.3 Passive Electrode and Saline Soaked Sponges
- •12.2.1.4 Dry Electrodes
- •12.2.1.5 Electrode Positions
- •12.2.2 Detecting the Signal: Sensors for Other Measures
- •12.2.2.1 Bipolar Peripheral Electrophysiology
- •12.2.2.2 Peripheral Physiological Sensors
- •12.2.2.3 GSR
- •12.2.2.4 Respiration
- •12.2.2.5 Photoplethysmography (PPG)
- •12.3 Conclusion
- •References
- •13.1 Purpose and Features
- •13.2 Before Starting Your Study
- •13.2.1 General Parameters
- •13.2.2 Special Applications
- •13.2.3 Real-Time Processing
- •13.3 During a Measurement Session
- •13.4 Troubleshooting
- •13.5 Conclusion
- •References
- •14.1 Introduction
- •14.2 Importance of Triggers
- •14.3 Advantages of Triggers
- •14.4 Disadvantages of Triggers
- •14.5 Alternatives to Triggers
- •14.6 Good Practice for Using Triggers
- •14.7.1 Setup
- •14.7.2 Analysis
- •14.7.3 Interpretation
- •14.8 Conclusion
- •References
- •15.1 Introduction
- •15.2 The Idea of Signal-to-Noise Ratio (SNR)
- •15.3 Sources of Artifact
- •15.4 Common Physiological Artifacts
- •15.4.1 Eye Artifacts
- •15.4.2 ECG Artifacts
- •15.4.4 Other Physiological Artifacts
- •15.5 Common Technical Artifacts
- •15.5.1 Technical Artifacts
- •15.5.2 Electrode Artifacts
- •15.5.3 Gel-Related Artifacts
- •15.5.4 Movement Artifacts
- •15.5.5 Body/Head Movements
- •15.5.6 Cable Movement Artifacts
- •15.6 Artifacts in Advanced Applications and Multi-modal Recordings
- •15.6.1 EEG and Functional MRI
- •15.6.2 EEG and Non-invasive Brain Stimulation
- •15.7 Optimizing the EEG Recording Quality
- •15.7.1 Focus on the Cap Preparation
- •15.7.2 Optimize the Recording Environment
- •15.7.3 During the Recording
- •15.7.4 Post Recordings
- •15.8 Conclusion
- •References
- •16.1 Introduction
- •16.2 Lab Infrastructure
- •16.2.1 Signal Quality
- •16.2.2 Control Over the Experimental Environment
- •16.2.4 Safety
- •16.3 Position of the Equipment and Accessories
- •16.4 Lab Procedures
- •16.5.1 Mobile Setups
- •16.5.2 Electrode Types
- •16.5.2.1 Passive Sponge-Based Electrodes
- •16.5.2.3 Dry Electrodes
- •16.5.3 Special Populations
- •16.5.3.1 Children
- •16.6 Concluding Summary
- •References
- •17.1 Introduction
- •17.2 Common Preprocessing Steps: Data Transformation
- •17.2.1 Inspecting Data
- •17.2.2 Changing the Sampling Frequency
- •17.2.3 Re-referencing
- •17.2.4 Interpolating Channels or Data Portions
- •17.2.5 Segmenting Data
- •17.3 Common Preprocessing Steps: Artifact Handling
- •17.3.1 Filtering
- •17.3.2 Attenuating Artifacts
- •17.3.2.1 Independent Component Analysis (ICA)
- •17.3.2.2 Regression Techniques
- •17.3.2.3 Template Subtraction Methods
- •17.3.3 Rejecting Artifacts
- •17.5 Tools for Processing and Analyzing EEG
- •17.6 Concluding Remarks
- •References
- •18.1 Introduction
- •18.2 Characterizing an Oscillatory Process
- •18.2.1 Fundamental Characteristics of an Oscillatory Process
- •18.2.2 From Time to Frequency and Back
- •18.3 Foundation for Spectral Analysis: The Dot Product
- •18.4 Fourier Analysis
- •18.4.1 From Vectors to Sinusoids: The Fourier Connection
- •18.4.2 The Fourier Family
- •18.4.3 Discrete Fourier Transform
- •18.4.4 Power Spectrum
- •Further Readings
- •References
- •19.1 Introduction
- •19.2 How to Get from EEG to ERPs
- •19.2.1 How to Process Your ERP Data
- •19.2.1.1 Pre-processing
- •19.2.1.2 Trial Selection
- •19.2.1.3 Baseline Correction
- •19.2.1.4 Averaging
- •19.2.2 Interpreting ERPs
- •19.2.3 Group Analysis
- •19.2.4 Single-Trial Analysis
- •19.3 Characteristics of the ERP and Its Components
- •19.4 Commonly Investigated ERP Components
- •19.4.1 Early Sensory Components
- •19.4.2 Long-Latency Sensory Components
- •19.4.3 Later Cognitive Components
- •19.4.4 ERP Components in Multimodal Recording Scenarios
- •19.5 Extraction of ERP Features
- •19.6 Conclusion
- •References
- •20.1 Introduction
- •20.2 Fundamentals of EEG Source Imaging
- •20.3 Forward Problem
- •20.4 Source Estimation
- •20.5 Statistical Inference in the Source Space
- •20.6 Source Connectivity
- •20.7 Conclusion
- •References
- •21.1 Introduction
- •21.2 Raw Data Access
- •21.2.1 How to Get Raw Data
- •21.2.2 How to Work with Raw Data Online
- •21.2.3 What Factors to Consider for Online Processing
- •21.3 Designing an Online Processing Experiment, an Example
- •21.4 Conclusion
- •References
- •22.1 Introduction
- •22.1.2 Chapter Overview
- •22.2.2 Exactly What the SME Means
- •22.2.5 Why the Scoring Method Matters
- •22.2.8 Other Potential Uses of the SME
- •22.4 Metrics of Reliability
- •22.5 Final Thoughts
- •References
- •23.1 Cognitive Neuroscience
- •23.1.1 Neuropsychology and EEG
- •23.1.2 Mental Chronometry and EEG
- •23.2 Research on the EEG Signals
- •23.3 Conclusions
- •References
- •24.1 Introduction
- •24.2.1 Clinical Research
- •24.2.2 EEG in Research for Clinical Applications
- •24.2.3 EEG as a Biomarker
- •24.3 Examples of Clinical Applications of EEG
- •24.3.1 Epilepsy
- •24.3.2 Sleep and Sleep Disorders
- •24.3.3 Anesthesia
- •24.4.2 Brain-Computer Interfaces and Movement Disorders
- •24.5 Future of EEG in Clinical Applications
- •24.6 Conclusion
- •References
- •25.1 Introduction
- •25.1.1 Why Connect Brains and Computers?
- •25.1.2 What Is a BCI?
- •25.1.3 Types of BCIs: Active, Reactive, Passive
- •25.1.4 BCIs in Neuroscience and HCI
- •25.2 Signals and Sensors
- •25.2.1 Neural Signals for BCIs
- •25.2.2 Wearable EEG and Form Factors
- •25.3 The BCI Pipeline: From Raw Signals to Decisions
- •25.3.1 Overview
- •25.3.2 Experimental Design and Labeling
- •25.3.3 Preprocessing and Artifacts
- •25.3.4 Feature Extraction
- •25.4 BCI Types Illustrated
- •25.4.1 Motor Imagery as an Active BCI Paradigm
- •25.4.2 P300 and SSVEPs as Reactive BCI Paradigms
- •25.4.3 Workload, Error, and Other Passive BCI Paradigms
- •25.5.1 Mental State Assessment as a First Stage
- •25.5.2 Open- and Closed-Loop Adaptation
- •25.6 Practical Challenges
- •25.6.1 Mobility, Artifacts, and Non-stationarity
- •25.6.2 Cross-User and Cross-Session Generalization
- •25.6.3 Evaluation in Real Settings
- •25.6.4 Ethics, Privacy, and Neurorights
- •25.7 Conclusions and Outlook
- •25.7.1 Key Takeaways
- •25.7.2 Future Trajectories
- •References
- •26.1 Focal Epilepsy
- •26.2 EEG Manifestations of Focal Epilepsy
- •26.2.1 Ictal EEG Patterns
- •26.2.2 Interictal EEG Patterns
- •26.3 Localization of Ictal and Interictal EEG Events
- •26.4 Intracranial EEG in Presurgical Planning
- •26.5 AI in EEG Interpretation
- •26.6 Conclusion
- •References
- •27.1 Introduction
- •27.2 Neonatal EEG Applications
- •27.2.2 Somatosensory States Monitoring in Neonates
- •27.3 Paediatric EEG Applications
- •27.3.1 Sleep Monitoring in Children and Adolescents
- •27.4 Future Directions and Conclusion
- •References
- •28.1 What Is Sleep?
- •28.1.1 Stages of Sleep
- •28.1.2 How Sleep Changes with Age
- •28.2 Measuring Human Sleep
- •28.2.1 The Various Forms of Sleep
- •28.2.2 Unihemispheric Sleep
- •28.3.1 NREM Sleep and Learning
- •28.3.2 REM Sleep and Learning
- •28.4.1 Active Brain Networks During Sleep
- •28.4.2 Measuring the Balance of Excitation and Inhibition in the Human Brain
- •28.4.3 The Cerebrospinal Fluid Dynamics in Human Sleep
- •28.5 Conclusions
- •References
- •29.1 What Is Mobile EEG?
- •29.2 Range of mEEG Systems
- •29.3 Technical Considerations
- •29.4 Validation
- •29.5 Application
- •29.6 Mild Cognitive Impairment
- •29.7 mEEG and Health and Exercise
- •29.8 mEEG in Sports
- •29.9 Conclusions
- •References
- •30.1 Introduction
- •30.2 General Framework and System Overview
- •30.3 Spectrum of Studies
- •30.4 MoBI+ Framework
- •30.5 Processing Multimodal MoBI Data
- •30.6 Challenges and Limitations
- •30.7 Conclusion
- •Appendix
- •List: Traveling with MoBI Equipment
- •References
- •31.1 Introduction
- •31.2 Types of Electric Brain Stimulation
- •31.3 Online Effects
- •31.3.1 Conventional Artifact Removal Strategies
- •31.3.1.1 Transcranial Direct Current Stimulation (tDCS)
- •31.3.1.2 Transcranial Random Noise Stimulation (tRNS)
- •31.3.1.3 Transcranial Alternating Current Stimulation (tACS)
- •31.3.3 Innovative Approaches to Minimize Artifacts
- •31.3.3.1 Non-Sinusoidal Waveforms
- •31.3.3.2 Amplitude-Modulated tACS (AM-tACS)
- •31.3.3.3 Transcranial Temporal Interference Stimulation (tTIS)
- •31.3.3.4 Summary of Advantages and Limitations
- •31.4.1 Spectral Power
- •31.4.2 Phase Locking/Phase Coherence
- •31.4.3 ERPs
- •31.4.4 Further Measures
- •31.5.1 Rationale
- •31.5.2 Procedure
- •31.6 Closed-Loop Systems
- •31.7 Technical Requirements
- •31.8 Conclusion
- •References
- •32.1 Introduction
- •32.2.1 Equipment

15 Getting Clean Data: Artifacts and How to Prevent Them 179
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
amplitude, while voluntary blinks tend to last longer and produce higher amplitude
signals. However, in terms of their effect on the EEG, both types of blinks generate
similar artifacts, a prominent positive deflection in the frontal electrodes (Fp1 and
Fp2). In the Fig. 15.2, you can distinguish between the first 4 voluntary blinks
ed by three spontaneous blinks, and then five further voluntary blinks.
follow
Some EEG labs, especially those with a focus on polysomnography, visual tasks,
or co-registering eye-tracking, generally place an electrode in vertical alignment
with one of the frontopolar (Fp1; Fp2) electrodes, which is best suited for
co-registering the vertical electrooculogram (EOG) and blink activity.
Eye flutter or twitching (Eye
myoclonia) doesn’t happen often, but this involuntary eyelid twitch leads to irregular and fast waves on the EEG. To get an idea of
what it looks like you can imagine rapidly occurring eyeblink artifacts, which
subside within a short time.
Rapid Eye movement (REM) is relevant in sleep EEG research. At this specific
sleep
stage, the eyes move predominantly in the horizontal direction at a higher
frequency, but lower voltage amplitude compared to intended horizontal eye movements during wakefulness. These eye movements, while technically considered
artifacts in EEG recordings, are both unavoidable and informative. Rather than
being dismissed as mere noise, they serve as an indicator of sleep stage and provide
meaningful insight into sleep studies.
Table 15.1 summarizes recom
mendations for minimizing physiological artifacts,
including strategies for preparation before recordings, guidance for instructing
participants, and procedures for managing artifacts during breaks or between trials.

180 D. Kadlec et al.
Table 15.1 Eye artifacts and tips for reducing their occurrence
Solutions
Before recording
Artifacts
Eyeblinks Blink with a natural
Vertical
eye
movement
Horizontal
eye
movement
(instruction) Before recording (setup) During (monitoring)
pattern
Keep your gaze fixed at
the
center of the screen
and avoid looking up or
down, or around the
screen.
Maintain visual focus on
the
stimulus or target
object. Avoid eye movements unless the task
requires gaze shifts
Check already during the
pilot testing
occurs frequently and
appears time-locked to the
stimuli, which can happen
in visually driven tasks.
If so, reduce the contrast,
luminance, and match the
stimuli of different conditions
Brief pauses in the paradigm can be beneficial.
During visual tasks,
maintaining focus on the
display prevents
unwanted eye movements.
To further reduce distractions, ensure background
behind the stimulation
screen remains neutral.
Gaze shift can appear
more often when the
stimuli are presented at
variable spatial locations.
Depending on the paradigm, providing a fixation
point at the center of the
screen will help keep the
participant focussed.
if blinking
Annotate and note in a
file unnatural
log
blinking behavior. For
example, if the participant is blinking each time
after a visual stimulus on
the screen.
Document any atypical
movement artifacts,
eye
such as instances where
the participant shifts their
gaze or appears to redirect attention.
Use the same approach as
vertical eye
for
movement
One can demonstrate many of these eye artifacts and thei r effect on the EEG before
the recordings. Inviting the participant to perform specific actions (i.e., intentional
blinking and eye movements in vertical and horizontal directions) induces the
artifacts. These tasks raise the participants’ awareness of their impact on EEG
recordings. However, it is important to emphasize that maintaining natural behavior
is essential.
15.4.2 ECG Artifacts
ECG and pulse artifacts are rarely seen in standard lab EEG recordings, but when
they do occur, recognizing their rhythmic patterns and peaks can be helpful for
offline analysis. There is not much one can do before and during the EEG recordings.

15 Getting Clean Data: Artifacts and How to Prevent Them 181
These artifacts are more frequent and visible in special applications, such as the
simultaneous recording of EEG and fMRI due to the magnetic field(s).
The ECG artifact originates from the heart’s electrical activity. The rhythmic
sharp waveform can spread across multiple electrodes , particularly on the left side of
the head, such as the lower temporal or mastoid electrodes, due to the heart’s
position and its relatively high voltage output. The artifact typically appears in
phase with the R-peak of the heartbeat and if an electrocardiogram signal is
co-registered with the EEG you can clearly see this.
In contrast, the pulse artifact shown in Fig. 15.3 is mechanical in origin, resulting
from
blood pulsing through the arteries beneath the scalp. If it is present at all, it
typically affects a single electrode, often in the mastoid or neck area, and appears as
slow fluctuations, time-locked to the heartbeat. This artifact indicates that the
electrode is likely placed directly above an artery, such as the temporal or carotid
artery (Amin et al.,
2023).
To reduce the impact of pulse artifact, depending on the flexibility of the cap or
the electrode can be shifted slightly in either direction to see if that lowers or
net,
eliminates the artifact. Adjusting one individual electrode without moving the EEG
cap is generally challenging. Therefore, offline processing is usually the right
approach here (see Chap. 17: Pre-processing and Artifact Handling).
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

182 D. Kadlec et al.
15.4.3 Electromyographic (EMG)—Muscular Artifacts
Muscle activity causes EMG artifacts in EEG, generating high-frequency signals
that can overlap with the entire EEG frequency range. The artifact amplitude
depends on the intensity of muscle activation and the specific muscle groups
involved. The closer the active muscles are to the EEG electrodes, the more they
affect the signal. For example, head movements or facia l muscle activity produce
much stronger artifacts than isolated muscle contractions in distant areas like the
legs. Luckily, EMG artifacts are the most easily prevented. Avoiding the muscular
artifacts is recommended because the frequency bandwidth overlaps with the entire
EEG spectrum, but whether you can achieve this will depend on your paradigm and
the task performed by participants.
Talking and chewing artifacts are generated through the muscles located mainly
under the temporal electrodes but can spread over the whole EEG map. These
actions usually happen consciously, however, in some cases the participants chew
their tongue or perform mouth movement unintentionally.
Clenching teeth artifacts, visible in Fig. 15.4, originate from the face and
jaw-muscle groups, mainly under the temporal part of the EEG caps. They have
higher frequencies with larger amplitudes and can spread across the entire scalp.
Participants may begin to experience slight muscle cramping or jaw clenching when
intensely focused on a task. Additionally, an overly tight chinstrap can contribute to
discomfort or trigger unintended movement artifacts.
Table 15.2 outlines further recommendations for minimizing artifacts similar to
those caused by chewing or talking.
Glossokinetic artifact, or tongue artifact, originates from the muscle group within
your oral cavity and can be registered with the electrodes on the participant’s scalp.
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

15 Getting Clean Data: Artifacts and How to Prevent Them 183
Table 15.2 EMG artifacts and suggested solutions
Solutions
Before recording
Artifacts
Talking/
chewing
Clenching
teeth
Ear wiggling/
smiling
&
frowning/
eyebrow
movement
Shoulder and
tensions
neck
(instruction)
Don’t talk during the
paradigm,
chew chewing gum or
tongue.
Stay relaxed especially
the
cles. Don’t press your
lips together during the
recording.
Keep the face neutral
over the course of
recordings.
Find a comfortable
seated position and
remain still. Minimize
face and head movement
to ensure clean
recordings.
and don’t
jaw and facial mus-
Before recording (setup) During (monitoring)
Brief pauses in the para-
digm can be beneficial
for participant to ask
questions or for the
investigator to provide
further instructions.
If the paradigm is
focused on speech patterns loosen the chin
strap or maybe select
chest strap instead.
Loosen the chinstrap
slightly to reduce tension
around the jaw.
Correct size of the EEG
cap and adapted recording duration can avoid
face muscle activation.
Depending on the paradigm,
consider incorporating short breaks and
allowing participants to
adjust their seating or
lying positions. Ensuring
a comfortable setup (such
as a chair with armrests
and a headrest) can help
minimize movement and
reduce artifacts.
Use annotations and
in a log file if
notes
EMG artifacts occur
frequently.
Build in breaks of sufficient length and suggest to reduce tension.
If required, a reminder
during pauses will solve
it.
Monitor for frequent
shifts. If
posture
observed, schedule
breaks and modify
setup, adjust chair
height or armrests as
needed.
Some participants might move their tongue unintentionally when focused on the
task. For demonstration purposes, it can be helpful to ask participants to say “La-lala”. This simple vocalization allows observation of muscle activity while minimizing
complex articulation. To ensure clean EEG data, participants should avoid unnecessary tongue movements during recordings.
Fascial muscle artifacts in EEG recordings are largely avoidable. While participants may attempt to relax, habitual expressions such as smiling, wiggling the ears,
raising the eyebrows, or frowning can still occur unconsciously. Even low-intensity
activation of these muscle groups can interfere with the EEG signal (see Figs.
15.5
and 15.6).

184 D. Kadlec et al.
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
A brief demonstration of how facial movements affect the EEG can help the
participants understand the importance of maintaining a neutral and inactive facial
expression during the recordings.
human anatomy, the EEG electrodes can even pick up shoulder and neck
Due to
tension, typically in the occipital channels of the EEG cap or net. It isn’t uncommon
for participants to adapt their posture and, with it, increase muscle tension once the
participant is wearing an EEG cap. Participants can start to shrug, roll, and move
their shoulders for relaxation purpose, or adjust their sitting or lying position . These
muscle-related artifacts can be minimized with a well-designed lab setup, a properly
fitted EEG cap, and suf ficient slack in the cables. For longer recordings, providing
adequate breaks, a comfortable seating position, and cushioning at pressure points
can further help reduce tension and improve data quality.

15 Getting Clean Data: Artifacts and How to Prevent Them 185
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
15.4.4 Other Physiological Artifacts
Sweat artifacts, even mild sweating, can cause a change in skin conductivity, which
in turn translates into slow voltage drifts in the EEG. Sweat and perspiration artifacts
are more common in warm, humid lab environments and can introduce slow signal
drifts in EEG recordings. As outlined in Table 15.3, such effects can be minimized
maintaining a slightly cooler room temperature during the session. Participants
by
should wear clothing appropriate for the lab environment and the expected duration
of the recording.
Respiratory artifact is a combination of sources; the deep inhalation and exhalation
cycles transfer to a mechanical movement of the EEG electrodes, which results
in rhythmic amplitudes. The se can be observed when the caps are fastened with chest
belts and/or the head is lying on the electrodes, or a headrest is in use.
Swallowing
and nets are either too tight or the strap bumps against the participant’s Adam’s
apple. In this case, the whole cap is moving, affecting most of the electrodes; the
tongue muscle also contributes to the artifact.
artifacts appear when the chinstraps and fixations of the EEG caps

186 D. Kadlec et al.
Table 15.3 Other physiological artifacts and optional solutions
Solutions
Before recording
Artifacts
Sweat and
perspiration
Respiration Breathe naturally and not
Swallowing Assess whether
(instruction) Before recording (setup) During (monitoring)
Check with the participant
confirm their comfort
to
level.
Ensure proper cap
deeply.
placement and fit.
swallowing or taking a sip
of water produces a visible artifact. If so,
adjusting the chinstrap
may resolve the issue.
Check the room temperatures
and if required
cool it down before the
EEG recordings
Place a towel or suitable
foam beneath the participant’s head to avoid
pressure points by the
electrodes and possible
friction.
Make sure the cap fits
snug
but still
comfortable.
Monitor and check with
the participant about a
comfortable temperature.
Should the artifact result
from mechanical influence, ask the participant
to lift their head from the
headrest.
If the chinstrap is too
ask the participant
tight,
to adjust it.
15.5 Common Technical Artifacts
15.5.1 Technical Artifacts
Non-physiological signal distortion can originate from a wide range of sources,
including some that interact and overlap with the desired brain signals. These
artifacts can manifest in various form s, such as sharp spikes, high-frequency noise,
high-voltage fluctuations, or slow drifts, overlaying and distorting the EEG signal.
Here we categorize technical artifacts as environmental, electrical interference,
and
mechanical:
Environmental noise/electrical interference
Electromagnetic interference is one
of the most common electrical artifacts in
EEG recordings, also called line noise. The power line noise typically occurs at
50 or 60 Hz, depending on the country’s power grid. It is often present from the
very start of an EEG session. Any electronic device powered by alternating
current, such as power strips, laptop chargers, adapters, projectors, or treadmills,
can introduce this interference into the EEG recordings. Even devices like
monitors or dimmable lights can cause localized artifacts, especially in electrodes
positioned near the source (e.g., frontal channels).
Interference from external devices
External devices can operate at frequencies other than 50/60 Hz, so it is a good
to look for noise in other frequency bands.
idea
While modern amplifiers do a terrific job of reducing noise, external devices
can
still interfere with EEG recordings. The source s mentioned previously, such
as dimmable lights, wireless devices, and medical or laboratory equipment

15 Getting Clean Data: Artifacts and How to Prevent Them 187
(including stimulation devices and ventilators), may exhibit frequency-specific
noise ranges that vary in amplitude and frequency over time. These artifacts are
often localized to specific electrodes near the source of interference.
Static discharge
Static discharges can cause brief, high-amplitude spikes in the EEG signal that
may look like biological events (i.e., epileptiform seizure spikes) and can be
misleading. This artifact can originate from hair movement, synthetic materials
(such as plastic chairs and carpets), or even clothing that builds up and releases a
static charge. In the EEG, it is visible as a sharp wave that can affect mul tiple
channels simultaneously.
These examples are summarized in Table 15.4, which outlines key sources,
charact
eristics, and improvement strategies for common technical artifacts in EEG
recordings.
15.5.2 Electrode Artifacts
Electrode artifacts, as illustrated in Fig. 15.7, result from physical or electrical
disturbances at the interface between the electrode’s conductive part and the skin.
These artifacts may mimic physiological signals or create false waveforms that
distort the EEG. Familiar sources include conductive gel/paste drying out and
loose or shifting electrodes due to head movement. Also, a poorly fitting electrode
cap can lead to electrode shifts. In Fig. 15.7, several randomly distributed electrodes
t interference at higher frequencies, highlighting the impact of such
exhibi
disturbance.
Furthermore, it’ s important to take care of your EEG equipment because corroded
electrodes
Malfunctioning components may cause random spikes, signal dropouts, or flatlines
in individual EEG channels, compromising data quality.
Loose electrode contact can lead
positions, which can suddenly cause a phenomenon called “electrode pop”. The
loose electrode’s electrochemical instability and the alteration in conductance result
in a fast voltage change. This may be triggered by a loose-fitted cap, quick body
movement, dried gel contacts, or hair pushing the cap away. The ground and
reference electrodes can also be affected by this type of artifact which would
consequently affect all other channels, so it is important to pay attention to these
electrodes during preparation.
Mechanical influence on electrodes caused by touching or applying pressure
(e.g.,
the signal. Accidental electrode touching during recordings (i.e., if the participant
spontaneously needs to scratch the head) can lead to electrical discharge and cause
electrode pop, electrical noise, or simply moving the electrode. A dry lab environment, especially during the wintertime, when heaters are running, can facilitate static
electricity (described above) and lead to frequent electrostatic discharges.
and damaged electrode leads can also cause electrode artifacts.
to slow drifts of the signal at individual electrode
by additional equipment), tugging on the cable, etc., can all cause artifacts in

188 D. Kadlec et al.
Table 15.4 Cable and electrode artifacts with troubleshooting strategies
Solutions
Artifacts
Cable
movement
Loose electrode Always check imped-
Line noise Turn off and disconnect
Defective or
malfunctioning
electrodes,
and
leads,
connectors
Before recording Before recording (setup) During (monitoring)
Gently move the cables
the cap preparation
after
to observe the effect
values and visually
ance
confirm proper electrode
contact before starting
EEG recording.
unnecessary electri-
all
cal equipment.
Regularly inspect and
test electrodes, cables,
and connectors before
recordings.
Ensure cables are
erly routed and strain
reliefs are in place. The
participant should be
able to move their head
freely while wearing the
EEG cap, net, or
headset.
Ensure that the EEG cap
fits securely. If necessary, apply an additional
fixation. Check that each
electrode has sufficient
gel and inspect for any
movement-related artifacts that may indicate
poor contact or electrode
displacement.
Eliminate unnecessary electrical devices.
Avoid coiled cables
around metallic table
frames. The participant
should be as far as possible from electronical
sources like multi power
strips, power lines, etc.
Replace any damaged or
unreliable components
to prevent unnecessary
signal loss or artifacts.
prop-
Pause the recording,
check
that cables are
securely arranged with
proper strain relief and
instruct the participant
to minimize head
movement.
If loose electrodes or
poor
contact are
detected, pause the session, reapply conductive medium, adjust the
cap for a secure fit, and
assess whether the
issue can be resolved
during a brief recording
break.
Monitor for line noise
during recordings and
keep track of which
electronic devices were
powered on in the
environment or by the
participant. It may also
be helpful to apply a
Display Filter set to the
specific interfering frequency (e.g., 50 or
60 Hz) to visualize and
assess the impact of
line noise in the EEG
signal more clearly.
Use annotations when
random spikes, signal
dropouts, or
flatlines appear in individual EEG channels
15.5.3 Gel-Related Artifacts
These artifacts can occur when insufficient gel is applied under an electrode or when
sponges have not absorbed enough saline to ensure proper skin-electrode con tact.
On the contrary, applying excessive amounts of gel or paste may lead to so-called gel
bridges between adjacent electrodes, particularly when the conductive medium is
less viscous and runs along the scalp. These bridges create an electrical shortcut
between the electrodes. Signals from bridged electrodes may appear identical,
making data interpretation unreliable.
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