EEG Data Analysis for Intellectual Developmental Disorder

AbstractFor decades, the frontal alpha asymmetry (FAA) - a disproportion in EEG alpha oscillations power between right and left frontal channels - has been one of the most popular measures of depressive disorders (DD) in electrophysiology studies. Patients with DD often manifest a left-sided FAA: relatively higher alpha power in the left versus right frontal lobe. Recently, however, multiple studies failed to confirm this effect, questioning its reproducibility. Our purpose is to thoroughly test the validity of FAA in depression by conducting a multiverse analysis - running many related analyses and testing the sensitivity of the effect to changes in the analytical approach - on data from three independent studies. Only two of the 81 analyses revealed significant results. We conclude the paper by discussing theoretical assumptions underlying the FAA and suggest a list of guidelines for improving and expanding the EEG data analysis in future FAA studies.

Download Full-text

Towards the Real-Life EEG Applications

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Cyber-Physical Systems for Social Applications ◽

10.4018/978-1-5225-7879-6.ch014 ◽

2019 ◽

pp. 305-317

Author(s):

Guangyi Ai

Keyword(s):

Data Analysis ◽

Real Life ◽

The Real ◽

Life Condition ◽

Research Fields ◽

Eeg Data ◽

Artifact Suppression ◽

Electroencephalogram Eeg ◽

Real Life Condition ◽

Brain Monitoring

Electroencephalogram (EEG) is one of the most popular approaches for brain monitoring in many research fields. While the detailed working flows for in-lab neuroscience-targeted EEG experiments conditions have been well established, carrying out EEG experiments under a real-life condition can be quite confusing because of various practical limitations. This chapter gives a brief overview of the practical issues and techniques that help real-life EEG experiments come into being, and the well-known artifact problems for EEG. As a guideline for performing a successful EEG data analysis with the low-electrode-density limitation of portable EEG devices, recently proposed techniques for artifact suppression or removal are briefly surveyed as well.

Download Full-text

Artifacts

MEG-EEG Primer ◽

10.1093/med/9780190497774.003.0008 ◽

2017 ◽

pp. 98-127

Author(s):

Riitta Hari ◽

Aina Puce

Keyword(s):

Data Analysis ◽

Eye Movements ◽

Cardiac Activity ◽

Signal Space Separation ◽

Eeg Data ◽

Eeg Recordings ◽

Power Line Noise ◽

Post Hoc ◽

Space Separation ◽

The Brain

This chapter focuses on different types of biological and nonbiological artifacts in MEG and EEG recordings, and discusses methods for their recognition and removal. Examples are given of various physiological artifacts, including eye movements, eyeblinks, saccades, muscle, and cardiac activity. Nonbiological artifacts, such as power-line noise, are also demonstrated. Some examples are given to illustrate how these unwanted signals can be identified and removed from MEG and EEG signals with methods such as independent component analysis (as applied to EEG data) and temporal signal-space separation (applied to MEG data). However, prevention of artifacts is always preferable to removing or compensating for them post hoc during data analysis. The chapter concludes with a discussion of how to ensure that signals are emanating from the brain and not from other sources.

Download Full-text