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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

200 P. R. Bazán
Acoustic noise can also be a distraction. It is important to turn off smartphones or
put them in silent mode (vibrations should also be avoided). Further, having the
experiment room close to loud areas (kitchen, bathrooms, a parking lot, areas with a
high circulation of people) will likely impact the data acquisition. If this can’t be
avoided, acoustic isolation around the room is an option. Additionally, it is a good
idea to have a sign on the lab door to remind people to be quiet and to not enter
during a meas urement.
Participants can be uncomfortable if the room is either too hot or too cold, or if
their chair is uncomfortable. This should be considered when furnishing the lab and
when deciding whether heating or air conditioning is needed.
16.2.3 Efficiency
The lab infrastructure can also help optimize data acquisition:
. Equipment setup and cap preparation—The room size and layout along with its
furniture
well as freedom of motion around the participant to prepare the electrodes.
. Transition between participants—As scheduling several participants in sequence
can
room for pre- and post-experiment activities, such as completing the consent form
and questionnaires. This allows the preparation of the next participant, while the
previous is finishing the experi ment.
. Clean up—It is necessary to have
the experiment, such as a sink. Similarly, if gel-based electrodes are used, the
participants will need to wash their hair after the experiment. Having facilities
such as a hair washing chair, a large sink, or a shower close to the lab is a
good idea.
. Data storage and backup—The lab infrastructure should consider the proper
resour
or a cloud system for data storage.
should allow for easy access to needed equipment and consumables, as
be an effective strategy for data acquisition, it is helpful to have a separate
a place close by for cleaning the electrodes after
ces for data storage and backup, either by having an internal backup server
16.2.4 Safety
Data privacy and protection must be considered when defining the method for data
storage and backup. Please consider your local regulations, especially when using
cloud storage services for backup or for data processing.
We also
planning the lab infrastructure, aside from the common safety concerns for buildings, like having an emergency exit route and clear safety protocols, there are some
tips related specifically to the EEG. For example, proper grounding of the electrical
need to ensure the physical safety of the experiment participants. When

16 Practical Aspects of EEG Data Acquisition 201
installation is important to avoid electrical discharges to the participant, especially if
the EEG system is not battery powered (has a mains connection). Additionally, if
possible, the electrical installation of the room should be planned to provide enough
outlets in appropriate locations, to avoid the use of cord extensions.
From the perspective of EEG maintenance, it is important to avoid metal furniture. If the electrode touches metal surfaces, this can lead to the formation of an
alloy. Therefore, avoid metal sinks, or have a dedicated plastic bucket to clean the
electrodes. Also, make sure to have proper storage for the equipment, regarding
temperature and humidity, as indicated by the EEG manufacturer.
16.3 Position of the Equipment and Accessories
Once we have the proper infrastructure, we can focus on how to optimize the
position of the equipment in the lab and the electrode cable routing. When
attempting to optimize the position of the equipment, there are two common
approaches:
. Fixed position of equipment—In other words, the equipment is stored in the
position
equipment is always ready. This is usual ly recommended for stationary setups
with bigger and heavier equipment. However, this requires having a single
application for the data acquisition room, which sometimes is not the case.
Using cable organizers, such as wraps and sleeves, is a good option for fixed
position setups.
. Flexible positions—In this case, the equipment has a dedicated position for
storage but has to be set up for each data acquisition. This setup is more suitable
for data acquisition in different locations, even outside the lab (e.g., naturalistic or
ecological studies). Further, it can be used when the acquisition rooms are not
dedicated to a single device or experiment type. Furniture with wheels that allow
the system and its accessories to be easily moved is very useful in this kind of
setup.
where it is going to be used. This can be faster and simpler as the
Regardless of whether you have fixed or flexible positions, there are some things
should pay attention to. Here we will again consider our four optimization
you
points:
. Signal quality
that are needed for the study but that can generate noise. This is more relevant
when working with dry or passive electrodes, as active electrodes are less
susceptible to this type of noise. Proper cable routing also helps minimize artifacts
related to the motion of the electrodes, as entangled cables can pull and move the
electrodes, therefore, the cables should be as stable as possible. Cable routing is
—We should position the EEG amplifier away from other devices

202 P. R. Bazán
especially important in applications where a significant noise induction occurs,
such as EEG-TMS (Chap.
EEG
and fMRI).
. Control over the experimental environment—Ideally, the equipment and cables
d be outside of the field of view of the participant, for example, being
shoul
directed to the back of the participant, as we want to avoid status lights from the
devices to become distractors. Therefore, having a table, a cart, or a cabinet
behind the participant is helpful to place the equipment. Alternatively, the
equipment can also be positioned to the side of the participants, outside of their
field of view.
. Efficiency—The equipment position needs to allo
researcher around the participant throughout the experiment. Aside from the
main equipment, it is helpful to plan the positioning, organization, and use of
common accessories . For example, when using gel-based or sponge-based elec-
trodes, paper and cloth towels will be helpful, so this should also be within the
reach of the researcher. A recommended alternative for flexible setups is to have a
cart or moving cabinet for each main device, with drawers for its accessories.
. Safety—Being able to move around the participant is also important for safety.
We want to minimize the chance of accidents relat ed to bumping, pulling, or
tripping on the equipment and cables. Proper cable organization should leave
some margin for motion for the participant and should not impede the movement
of the researcher. Further, the risk of damage to the cables can be reduced by
avoiding cable entanglement.
32, EEG-TMS) and EEG-fMRI (Chap. 33, Combining
w proper movement of the
The best position can be evaluated during the pilot testing phase (Chap. 10, Pilot
ng).
Testi
16.4 Lab Procedures
Often, there are simultaneous projects running in parallel and different users working
with the same EEG equipment in a lab. Consequently, it is very important to have
clear procedures that everyone can follow. These procedures should cover the safety
aspects of the building overall (e.g., emergency procedures), the specific aspects of
handling the equipment, and data storage and backup. One option is to have quick
guides as posters regarding these procedures, but it is also important to have
structured training for the lab members (Boudewyn et al.,
For further details on how to establish good lab management procedures and
study protocols, see Chap. 12, Study Workflow and Lab Management. Below, we
provi
de suggestions for specific setups.
2023).

16 Practical Aspects of EEG Data Acquisition 203
16.5 Practical Aspects Associated with Specific Setups
As some EEG setups can present specific challenges, we consider different scenarios
and offer tips to deal with these challenges.
16.5.1 Mobile Setups
The previous considerations apply to stationary setups and, to some extent, mobile
setups. But mobile setups have special characteristics that must be considered. There
are usually two main options when working with mobile EEG: headsets or caps
connected to portable EEG amplifiers. When working with headsets, it is important
to consider their weight and comfort according to the length of the experiment.
Additionally, depending on head motion associated with the task, the headset may
move out of position, so it is important to ensure stability if stronger movements are
expected. Therefore, headsets are a nice alternative for shorter experiments that
require a fast setup and do not have a lot of head movement.
Caps are usually more stable in this sense; however, they require special attention
to the lead wires. Using cable guides and Velcro straps attached to the cap can be
helpful to organize and stabilize the cables. Using overcaps might help stabilize the
cables, but they can increase sweating, which also causes artifacts. Better alternatives are tubular elastic net dressing retainers or sports pre-wrap tapes (Emmerling &
Kreilinger, 2018). Gel-based active electrodes will also be a good option, as they
minimize artifacts from cable movement. The portable EEG amplifier should be
placed in a comfortable and stable position as well. For this, a harness can be used;
alternatively, some caps also have a pouch to carry the EEG amplifier. Several
mobile EEG amplifiers have built-in acceleration sensors, which can provide important information for dealing with motion artefacts. However, these will be limited to
the position of the EEG amplifier—consider this when defining the amplifier position in your study. Alternatively, some systems allow additional acceleration sensors
to be connected, to capture motion information from other parts of the body.
Wireless
missing data points during mobile recordings, test and adjust the range during the
pilot phase (Chap.
recei
ences in the data transmission, reduce the number of devices that use wireless data
transmission to the minimum necessary—check if cellphones and computers are
using similar communication frequencies. Reducing the sampling rate can also
increase stability. To ensure wireless trigger reliability, send a pattern of triggers
instead of a single trigger for an event. This will allow you to recover the information
if one of the triggers is lost (Chap.
data transmission is also an important factor in mobile EEG. To avoid
10, Pilot Testing). Having a clear line of sight between the
ver and the transmitter will help if longer ranges are needed. To avoid interfer-
14, Triggers).

204 P. R. Bazán
16.5.2 Electrode Types
As there are several types of electrodes, it is important to follow the manufacturer’s
guidelines to achieve the best signal and protect the equipment. The main points are
the target impedance values, the procedures to lower the impedances, and how to
clean and maintain the electrodes. Before we present specific tips, the following
apply to all electrode technologies:
. Ground and reference electrodes (if available) must have a low impedance, as
affect all channels.
they
. After lowering the impedances, check the signal quality by looking for standard
signa
ls (e.g., blinks, alpha waves).
. Confirm which parts of the system can be wet for cleaning.
. Avoid strong disinfectants.
16.5.2.1 Passive Sponge-Based Electrodes
Sponge-based electrodes offer a very fast setup, and they leave almost no residuals
for
the participant to clean after the experiment; this makes them very useful for
children and some clin ical populations. However, as the sponge can dry over time,
they are more suitable for shorter experiments (up to around 60–90 min). To
optimize the length of the experiment, control the room’s ventilation, temperature,
and humidity, and plan short pauses to wet the sponges with a pipette. Further,
special attention to environmental and motion noise is required, as these electrodes
work with higher impedances (60–100 kOhms).
When preparing the participant, a towel can be used to gently absorb the excess
water
in the electrodes after removing them from the saline solution. To help lower
the impedances, a pipette can be used to move the hair away from the sponges.
Considering safety, prepare towels to protect the water-sensitive parts of the system.
Similarly, a towel can be placed over the shoulders of the participants, to avoid
getting their clothes wet. To ensure the durability of the sponges and electrodes, use
salt with high purity when preparing the saltwater solution, following the manufacturer’s recommendations.
16.5.2.2 Gel-Based Electrode
The mai
for better contact with the scalp . One thing to consider is that the impedances
continue to improve for a few minutes before stabilizing. Therefore, instead of
working on an electrode until the target impedance is reached, it is better to go
over all electrodes applying the gel; then go back and work more carefully on those
that still have higher impedances. There are two types of gel-based electrodes:
n advantage of gel-based electrodes is the high signal quality, as gel allows
s

16 Practical Aspects of EEG Data Acquisition 205
Passive electrodes—offer very good signal quality and allow longer experiments
(e.g., sleep studies) but require lower impedance values (5–10 kOhms). To
achieve low impedance values, a light abrasion of the scalp to clean it is
necessary. This can be done either as a prior step, or directly with an abrasive
gel. It is good to show the participant the gel and explain the abrasion procedure.
Working continuously or too long on a single electrode can cause discomfort or
even harm the participant. Ask the participant for feedback to avoid excessive
abrasion of the skin. As the preparation with passive electrodes can take a little
longer it is good to keep the participant relaxed and entertained, with a conversation or a short movie, for example.
Active electrodes—offer both relatively fast preparation time and high signal quality.
dances around 25 kOhms should provide a good signal. However, specific
Impe
applications can require very low impedances even with a ctive electrodes, such as
EEG-TMS. When using active gel-based electrodes, it is common to apply gel
using syringes with blunt needles. To reassure the participant it is safe, open the
needle in front of them and touch the back of their hand with the blunted tip.
16.5.2.3 Dry Electrodes
Dry electrodes require neither gel nor saltwater. Therefore, they make EEG record-
easier for experiments outside of the lab: fewer accessories for preparing and
ing
cleaning are needed. However, as the electrodes need to be in contact with the scalp,
the pressure needed for a good contact can cause discomfort over time. This
technology is mainly for shorter experiments. It works with higher impedances
(up to 2500 kOhms), therefore, the bandwidth of the usable EEG signal is usually
limited—check if this is suitable for your research. When preparing the electrodes,
make sure the participant is comfortable with the electrode pressure. Any small
discomfort will tend to increase and become an issue over time. Some dry electrodes
and their components (headsets) cannot get wet, so double check before cleaning—
isopropyl alcohol swabs can be a good option.
16.5.3 Special Populations
When working with specific populations, their needs must be evaluated, and the lab
environment and EEG procedures should be adapted accordingly (Kappenman &
Luck,
2015). Here, children will be our example population, but similar consider-
ations
could be made for other populations.

206 P. R. Bazán
16.5.3.1 Children
When working with children, providing a welcoming environment is important
(Hervé
et al., 2022). The staff should be properly trained to communicate with
appropriate language to the children and to their parents, paying attention to any
needs they may have (Cotter et al., 2002). For example, when working with infants,
it
is important to check if they need to be fed before the experiment, and make sure
that they feel comfortable with the lab environment, so consider having extra time
for the children to get used to the data acquisition room (Turk et al.,
ts the chance of a device acting as a distractor is bigger, and the child may want
infan
2022). With
to reach and grab it, which could cause accidents, which highlights the need for the
proper equipment positioning (out of the field of view of the participant). Further, the
lead wires should also be organized to avoid accidental grabbing by the infant (Hinz
& Pletti,
that
2021). Additionally, consider using electrode technologies with faster setup
also provide comfort such as sponge-based and active gel-based electrodes.
Video data synchronized with the EEG data can be very useful, as some of the
behaviors can be better identified with the video, especially in interaction studies
(Turk et al., 2022). Eye tracking can also add valuable information to EEG with
children
(Kulke, 2024).
16.6 Concluding Summary
In summary, recording high-quality EEG data requires optimizing the signal quality
and controlling the experimental environment. Additionally, this should be achieved
in a safe and efficient way. One important aspect is the lab infrastructure, from the
planning of the room and its surroundings to optimizing the devices present in the
data acquisition room. The room should have proper electrical installation and only
include the necessary equipment, providing comfort without offering distractions.
Adjusting the position of the equipment in the lab can also improve EEG data
acquisition, by placing the EEG further away from noise sources and out of the
participant’s sight, in an accessible and safe way. Further, the lab should have welldefined procedures for data acquisition and for training members to do it. Although
each setup will have its own challenges and opportunities, the tips provided here can
be a starting point for optimizing your own experiment.
References
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Part IV
Processing EEG Data

Chapter 17
EEG Preprocessing and Artifact Handling
Yin Fen Low and Ramon Martinez-Cancino
Abstract Transforming raw EEG recordings into interpretable data is a complex
process
their methods, these can be difficult for researchers new to EEG analysis to fully
grasp. This chapter aims to bridge that knowledge gap by providing a clear overview
of essential preprocessing steps.
filtering,
improving data quality and ensuring the validity of subsequent analyses.
Preprocessing is typically considered complete when the data is ready for segmentation, which is the process of dividing continuous EEG signals into meaningful time
windows or epochs for analysis.
with the essential knowledge needed to design and adapt preprocessing workflows
suited to specific research goals. While not exhaustive, this overview is supported by
references to key resources and recent literature to guide deeper exploration.
established
refinement of preprocessing pipelines.
that demands careful preprocessing. While published studies often outline
We begin by discussing the core components of EEG preprocessing, including
re-referencing, and artifact handling. These steps are foundational for
Rather than advocating for a single, standardized pipeline, this chapter equips you
To facilitate practical implementation, the final section presents a compilation of
EEG processing toolboxes and software to support the initiation and
Keywords Preprocessing · Data transformation · Artifact handling · Artifact
attenuation
Y. F. Low (*) · R. Martinez-Cancino
Brain Products GmbH, Gilching, Germany
e-mail:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
T. Warbrick
https://doi.org/10.1007/978-3-032-20450-9_17
· Artifact rejection
yinfen.low@brainproducts.com; ramon.martinez@brainproducts.com
(ed.), The EEG Handbook,
211
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