Section 1 - Introduction

This section is a WIP!

Ch 1: The Purpose of This Book, Who Should Read it, and How to Use it

This chapter is mainly an introduction for the book, discussing that it serves as an overview of the analysis that comes with EEG (but it can be done with other time-varying signals), but it is not exhaustive. Skipping over most of it here as it is not relevant to this summary

1.1 Cognitive Electrophysiology

The field of study known as Cognitive Electrophysiology can be thought of as a spectrum. One side is Cognitive psychology, which concerns itself with the cognitive components of the brain. The other side is Electrophysiology, which concerns itself with the functional properties of the neural networks that make up the brain.

Ch 2: Advantages and Limitations of Time- and Time-Frequency-Domain Analysis

This chapter discusses the different categories of analysis and their pros/cons,  some discussion of EEG vs MEG, brain network scales, and brain localization

2.1 Why EEG?

EEG has a high temporal resolution (unlike fMRI), which allows it to capture the cognitive events that occur within in 100s of milliseconds, from activity in the 4-8 hz theta-band oscillations all the way to 30-80 hz gamma band. EEGs also directly measure neuronal activity of populations of neurons, which is very prominent when measuring oscillations. EEG is also multidimensional, as the data compromises information across time, space / location, frequency, and power / strength. 

2.2 Why Not EEG?

EEG is not suited when looking for precise locations, and (generally) isn't suited for deep brain structures. IE it is a bad tool for finding " MEG may be better suited if these are you concerns.

EEG is likely to be a suboptimal method for any research questions involving "where in the brain does process X occur or is information Y stored"

EEG / MEG are both unsuited if trials of the experiment last longer than a few seconds or if time is highly variable. This makes it poorly suited for complex cognitive task studies, or those concerning social and emotional responses. The lower temporal resolution of fMRI makes it a better fit instead.

2.3 Interpreting Voltage Values from the EEG Signal

Measurement of EEG signals is typically microvolts, while with MEG it is femtotesla. For EEG, these absolute microvolt values arent helpful, as they can differ in magnitude due to irrelevant factors such as skull thickness or shampoo used or dipole orientation (similar irrelevant factors exist for MEG). Transformations such as decibel normalization may be preferred instead as then there can be comparisons made between trials.

2.4 Advantages of Event-Related-Potentials

Event-Related-Potentials (ERP, no not that kind) are small voltages from brain structures in response to specific events, thus it can be regarded as time locked activity. ERPs are simple, fast to compute, and require few tuning parameters and assumptions. They have high temporal precision and accuracy, are well researched in general. They serve as useful for quick glance data quality checks.

2.5 Limitations of ERPs

ERPs actually reveal rather little data from EEG signal, which makes it difficult to interpret null results (when your experiment finds nothing). ERPs also cannot be averaged with other trials, as it reduces the output to noise. It is also harder to link to physiological mechanisms since the neurophysiological mechanisms that produce ERPs are less understood than those that produce, for example, oscillations.