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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

TracyWarbrickEditor
TheEEG
Handbook
From Principles toPractice

The EEG Handbook

Tracy Warbrick
Editor
The EEG Handbook
From Principles to Practice

Editor
Tracy Warbrick
Brain Products GmbH
Gilching, Germany
ISBN 978-3-032-20449-3 ISBN 978-3-032-20450-9 (eBook)
https://doi.org/10.1007/978-3-032-20450-9
© Brain Products GmbH 2026
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of
illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by
similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
If disposing of this product, please recycle the paper.

Preface
This book is about doing EEG research well. Our aim is to provide a trusted
companion for students learning about EEG and for researchers conducting EEG
studies. A key strength of this book is its emphasis on practical guidance. A book
that not only explains the science behind EEG but also helps you navigate the realworld challenges of performing high-quality reproducible EEG research in the
laboratory. Drawing on more than 25 years of collective experience supporting the
neurophysiology research community, the authors understand where students and
researchers commonly encounter difficulties and how best to support them. Alongside the fundamental scientific background and core principles of EEG measurement
and analysis, we explore essential skills such as how to read EEG papers critically,
how to present your own findings clearly, and how to follow good scientific practices
in neuroscience. These skills form the foundation of a successful career in EEG
research. Experienced academics also contribute their expertise on specific applications, adding depth and breadth to the book’s content. The result is a resource
designed not just to be read but to be used, whether you are preparing a course
assignment, planning your next study, or troubleshooting an EEG recording in
the lab.
Gilching, Germany Tracy Warbrick
v

Acknowledgments
I would like to thank all contributors for their hard work and comm itment in bringing
this project to fruition. The depth and breadth of the book’s content reflect their
expertise and dedication. I am especially grateful to Olga Nusser for her invaluable
assistance with figures and illustrations for the chapters authored by the Brain
Products team. Finally, I would like to sincerely thank Pierluigi Castellone, General
Manager, Brain Products GmbH, for his support in making this book possible.
vii

About This Book
Our aim is to offer a reliable companion for students and researchers conducting
EEG studies. This book is divided into seven parts each addressing a key stage in the
EEG research workflow: from basic concepts to practical lab work, data analysis,
and scientific reporting. While the book is organized to guide readers step-by-step
through the process of designing, conduct ing, and communicating high-quality EEG
research, it does not have to be read linearly from cover to cover. Instead, readers are
encouraged to dip into the parts most relevant to their current stage of learning,
coursework, or research. The seven parts are outlined below.
Part I: Fundamentals of EEG A strong foundation in EEG research begins with
tanding what the EEG signal represents and how to interpret the activity
unders
recorded. This section opens with an overview of the current state of the art in
EEG, providing context for how the field has developed and where it is heading. We
then introduce key concepts such as how EEG signals are generated, how they can be
used in practice, and how to make full use of EEG’s high temporal resolution. EEG
depends on underlying biological processes; therefore it is also essential to understand the physiological and anatomical conditions under which these signals arise.
For this reason, we cover the basic anatomy of the central and peripheral nervous
systems, EEG in relation to human physiology, and brain states.
Part II: Experimental Design and Good Scientific Practice This part focuses on
the
principles of sound experimental design in EEG research. We discuss why
statistical inference matters and why you should consider this in study design. We
also provide practical guidance on pilot testing to optimize data quality. The section
concludes with advice on establishing efficient and reproducible workflows for your
studies.
Part III: EEG Data Acquisition Planning an EEG study involves numerous
ions, from selecting equipment and recording parameters to configuring trig-
decis
gers and managing potential artifacts. This section outlines the key considerations
ix

x About This Book
for effective EEG data acquisition and offers practical tips and troubleshooting
strategies to help you optimize your experimental setup.
Part IV: Processing EEG Data There are many approaches to analyzing EEG
data. In this section, we introduce common preprocessing steps and o ffer guidance
on determining which steps are appropriate for your study. We also describe
analytical approaches in both the time and frequency domains.
Part V: What Is EEG Used For? EEG supports a wide range of applications, and
g an overview of these can provide inspiration and context for your own work.
gainin
We begin with an outline of academic and clinical uses of EEG, followed by
in-depth contributions from experts who discuss specific application areas in greater
detail: BCI, focal epilepsy, neonatal and pediatric clinical applications, sleep, and
mobile EEG applications.
Part VI: Multimodal EEG Applications While EEG is a powerful tool in its own
right,
combining it with other measurement or stimulati on modalities can provide
deeper insights into brain function and behavior. This section explains why specific
multimodal combinations are valuable and highlights their contribution to the
broader neuroimaging literature.
Part VII: Writing and Reading Research Papers Reading and writing scientific
ure are essential skills for researchers. After completing a study and analyzing
literat
the data, researchers must communicate their findings clearly and transparently to
the research community. This final section offers guidance on reporting EEG
methods and results, including how to present data using figures, tables, and
statistics. We also discuss how to read EEG literature critically and extract the
information needed to evaluate and interpret published findings.
We since
rely believe that the EEG Handbook: From Principles to Practice will
provide students and researchers with the knowledge and tools to become proficient
EEG practitioners.

Contents
Part I Fundamentals of EEG
1 EEG in Context: Past, Present, and Future . . . . . .............. 3
Tracy Warbrick
2 What Is EEG? . . . . . . . . ................................. 15
Fernando Cross Villasana
3 Basic Anatomy: Central Nervous System . . . . ................ 31
Shivakumar Viswanathan
4 Basic Anatomy: Peripheral Nervous System . . . . . . . . . . . . . . . . . . 41
Cilia Jaeger
5 EEG in the Context of Human Physiology . . . . . . . . ............ 55
Michael Hoppstädter
6 Brain States . . . . . . . .................................... 63
Cilia Jaeger
7 Basic Time Concepts in EEG Practice . . . . . . . . . .............. 75
Shivakumar Viswanathan
Part II Experimental Design and Good Scientific Practice
8 Designing Your EEG Study . . . . . .......................... 89
Fernando Cross Villasana
9 Applying Statistics in Your EEG Research . . . . . . ............. 105
Shivakumar Viswanathan
10 Pilot Testing . . . . . . . . . . . . . . . . . . . . . ..................... 117
Paulo Rodrigo Bazán
xi

xii Contents
11 Study Workflow and Lab Management . . . . . . . . .............. 129
Tracy Warbrick and David Kadlec
Part III EEG Data Acquisition
12 Hardware for Recording EEG and Peripheral Physiology . . ..... 141
Tracy Warbrick and Cilia Jaeger
13 Software for Recording EEG and Peripheral Physiology . . . . . . . . . 155
Tracy Warbrick and David Kadlec
14 Triggers . . . . . . . . . . ................................... 163
Alex Kreilinger and Paulo Rodrigo Bazán
15 Getting Clean Data: Artifacts and How to Prevent Them . . . . . . . . 175
David Kadlec, Shivakumar Viswanathan, and Hannah Kreilinger
16 Practical Aspects of EEG Data Acquisition . . . . . . . ............ 197
Paulo Rodrigo Bazán
Part IV Processing EEG Data
17 EEG Preprocessing and Artifact Handling . . . . . . . . ........... 211
Yin Fen Low and Ramon Martinez-Cancino
18 Introduction to EEG Oscillations and Spectral Analysis . . . . . .... 227
Ramon Martinez-Cancino and Yin Fen Low
19 Event-Related Potentials . . . . . . . . . ........................ 239
Michael Hoppstädter
20 EEG Source Analysis . . . . . . . . . . . ........................ 255
Alejandro Ojeda
21 Online Processing . . . . . . . . . . . . . . . . ...................... 269
Alex Kreilinger and Alejandro Ojeda
22 Quantifying EEG and ERP Data Quality . . . ................. 279
Steven J. Luck
Part V What Is EEG Used For?
23 EEG in Basic Science and Academic Research . . . . . . . ......... 303
Fernando Cross Villasana
24 Clinical Applications . . . . . . . . . . . . ........................ 317
Cilia Jaeger
25 From Brain Signals to Neuroadaptive Technology:
BCIs for Human-Computer Interaction . . . . . . . . ............. 331
Marius
Klug, Diana E. Gherman-Nagy, and Thorsten O. Zander
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