Designing EEG Experiments for Studying the Brain

Design Code and Example Datasets

Aamir Saeed Malik, Hafeez Ullah Amin

• Publisher: Academic Press
• Year: 2017
• Print ISBN: 9780128111406
• eBook ISBN: 9780128111413
• Topic: Psychology
• Subtopic: Addiction in Psychology
• Index: Psychology

Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets details the design of various brain experiments using electroencephalogram (EEG). Providing guidelines for designing an EEG experiment, it is primarily for researchers who want to venture into this field by designing their own experiments as well as those who are excited about neuroscience and want to explore various applications related to the brain. The first chapter describes how to design an EEG experiment and details the various parameters that should be considered for success, while remaining chapters provide experiment design for a number of neurological applications, both clinical and behavioral. As each chapter is accompanied with experiment design codes and example datasets, those interested can quickly design their own experiments or use the current design for their own purposes. Helpful appendices provide various forms for one's experiment including recruitment forms, feedback forms, ethics forms, and recommendations for related hardware equipment and software for data acquisition, processing, and analysis.- Written to assist neuroscientists in experiment designs using EEG- Presents a step-by-step approach to designing both clinical and behavioral EEG experiments- Includes experiment design codes and example datasets- Provides inclusion and exclusion criteria to help correctly identify experiment subjects and the minimum number of samples- Includes appendices that provide recruitment forms, ethics forms, and various subjective tests associated with each of the chapters

Chapter 1

Designing an EEG Experiment

Abstract

This chapter describes the basics of electroencephalography (EEG) research and experiment design. At the beginning the fundamental EEG waves are briefly described. EEG experiments and related factors that can highly influence the experimental results, such as clear definition of study objectives, ethical issues, and proper sample size calculation are discussed. In addition, an example of experiment design is explained in detail. The well-known EEG data acquisition systems and stimulus presentation software are listed. Finally, general guidelines for EEG data collection are provided including general data acquisition setup, proper experiment design, preparation of participant, and EEG system check-up.

Keywords

EEG research; experiment design; ethical issues; ethical approval; sample size calculation; guidelines for EEG data acquisition

Contents

1.1 Introduction 2

1.2 Fundamental of EEG Waves 2

1.2.1 Delta Waves (Up to 4 Hz) 2

1.2.2 Theta Waves (4–8 Hz) 3

1.2.3 Alpha Waves (8–13 Hz) 3

1.2.4 Beta Waves (13–25 Hz) 4

1.2.5 Gamma Waves (above 25 Hz) 4

1.3 Importance of Experiment Design 4

1.4 EEG Experimentation: Ethical Issues and Guidelines 6

1.4.1 Ethical Issues 6

1.4.2 Ethics Approval Guidelines 7

1.5 Sample Size Computation 9

1.5.1 Objective and Hypothesis of the Study 9

1.5.2 Target Population 9

1.5.3 Statistical Attributes of Sample Size 10

1.5.4 Types and Numbers of Dependent and Independent Variables 11

1.5.5 Groups, Conditions, and Statistical Tests 11

1.5.6 Available Software 12

1.6 Example of Experiment Design 13

1.6.1 Objective 13

1.6.2 Instructions to Participant 14

1.6.3 Stimulus and Time 14

1.6.4 Trials, Blocks, and Conditions 15

1.6.5 Participants’ Response and Feedback 17

1.6.6 Events Synchronization with EEG 19

1.6.7 End of Task 19

1.7 EEG Equipment and Software 19

1.7.1 EEG Equipment and Data Acquisition Software 19

1.7.2 Presentation Software 22

1.8 Guidelines for EEG Data Acquisition 25

1.8.1 General Data Acquisition Setup 25

1.8.2 Experiment Design 28

1.8.3 Preparation of Participant 28

1.8.4 EEG System Check-Up 29

1.9 Summary 29

References 29

1.1 Introduction

Electroencephalography (EEG) is a reliable and widely used measurement tool for studying brain functions, abnormalities, and neurophysiological dynamics due to its low cost, noninvasiveness, portability, and high temporal resolution in the millisecond range.¹ In the field of neural signal processing, EEG is commonly used as a noninvasive brain imaging technique for diagnosis of brain disorders and normal EEG for understanding of brain functions in research studies. It enables the researchers and clinicians to study brain functions such as memory, vision, intelligence, motor imagery, emotion, perception, and recognition, as well as detect abnormalities such as epilepsy, stroke, dementia, sleep disorders, depression, and trauma. EEG signals reflect the electrical neuronal activity of the brain, which contains useful information about the brain state. This chapter will discuss the fundamentals of EEG, EEG experiments, ethical approval guidelines, sample size computation, experiment design, EEG equipment and presentation software, and EEG data acquisition.

1.2 Fundamental of EEG Waves

Since the beginnings of EEG, the study of different brain oscillations and their relationship with different brain functions has attracted the attention of researchers. Hans Berger discovered the presence of alpha and beta waves in EEG. The brain oscillations are categorized in frequency bands and related with different brain states or functions. In this section, a brief description of EEG frequency bands is provided. A typical example of EEG waves can be seen in Fig. 1.1.

Figure 1.1 EEG signal and corresponding bands.

1.2.1 Delta Waves (Up to 4 Hz)

EEG delta waves are high-amplitude brain waves and are associated with deep sleep stages. The delta waves are also associated with different brain functions other than deep sleep, e.g., high frontal delta waves in awake subjects are associated with cortical plasticity. Delta bands are reported as prominent brain waves in cognitive processing especially in event-related studies.² EEG low-frequency components, especially delta bands, are the primary contributor to the P300 peak of event-related potentials (ERPs). P300 is a widely studied and well-known indicator of cognitive processing.

1.2.2 Theta Waves (4–8 Hz)

Theta waves are observed in the drowsy state and more common in children than adults. In the awake adult, without doing any attention/cognitive activity, high theta activity is considered abnormal and associated with different brain disorders, e.g., high frontal theta is linked with nonresponse to antidepressant treatment in depression patients. However, high theta activity plays a significant role in attentional processing and working memory, see for review.³ Changes in theta activity are also reported in brain disorders such as depression in adults and dyslexia in children. Pizzagalli et al.⁴ reported that better responders to treatment in major depressive disorder (MDD) showed high theta activity in the rostral interior cingulate (BA 24/32). Klimesch et al.⁵ reported reduced theta activity in dyslexic children.

1.2.3 Alpha Waves (8–13 Hz)

Alpha waves can be observed spontaneously in normal adults during wakefulness and in relaxed state, especially when there is no mental activity. During the eyes-closed condition, alpha waves are prominent at parietal locations. Attentional processing or cognitive tasks attenuate the alpha waves. Alpha waves are subdivided into lower alpha and upper alpha. It has been observed that alpha activity changes with load during retention of working memory.⁶ In addition, individual alpha peak frequency is an indicator of general intelligence factor (also known as g factor).⁷

1.2.4 Beta Waves (13–25 Hz)

Beta waves have lower amplitudes than alpha, delta, and theta waves. Traditionally, beta waves are subdivided into low beta and high beta. The frontal and central regions of the brain are locations where enhanced beta waves can be observed during activeness, anxious thinking, problem solving, and deep concentration. Gola et al.⁸ reported that beta band power is increased over occipital sites during spatial discrimination tasks and visual attention in high-performing participants both young and aged. A detailed review on beta band activity is provided by Engel and Fries⁹ where evidence is given about beta activity involvement in cognitive processing and the motor system.

1.2.5 Gamma Waves (above 25 Hz)

Gamma waves are fast oscillations and are usually found during conscious perception. Due to small amplitude and high contamination by muscle artifacts, gamma waves are underestimated and not widely studied as compared to other slow brain waves. High gamma activity at temporal locations is associated with memory processes. Research studies reported that gamma activity is involved in attention, working memory, and long-term memory processes (see Ref. ¹⁰ for review). Gamma activity is also involved in psychiatric disorders such as schizophrenia, hallucination, Alzheimer’s disease, and epilepsy (see Herrmann and Demiralp review¹¹).

1.3 Importance of Experiment Design

Every scientific research study starts with a question and ends with a possible solution. In experimental research, especially in brain research, the question may be as general as “what brain regions are associated with the state of depression and stress?” or “what is the role of alpha band in stress and anxiety?” The experimental question pushes the researchers to derive a research hypothesis, which is a description about how a given manipulation can change certain measurements. The research hypothesis may be general or specific, like a research question. An example of research hypothesis in EEG research is the statement, “EEG alpha activity at frontal corte...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of Figures
• List of Tables
• Preface
• Chapter 1. Designing an EEG Experiment
• Chapter 2. Mental Stress
• Chapter 3. Major Depressive Disorder
• Chapter 4. Epileptic Seizures
• Chapter 5. Alcohol Addiction
• Chapter 6. Passive Polarized and Active Shutter 3D TVs
• Chapter 7. 2D and 3D Educational Contents
• Chapter 8. Visual and Cognitive Fatigue During Learning
• Chapter 9. 3D Video Games
• Chapter 10. Visually Induced Motion Sickness
• Chapter 11. Mobile Phone Calls
• Chapter 12. Drivers’ Cognitive Distraction
• Chapter 13. Drivers’ Drowsiness
• Chapter 14. Working Memory and Attention
• Appendices
• Glossary
• Index