An easy‐to‐follow handbook for electroencephalogram data analysis with Python

This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four c...

Full description

Saved in:
Bibliographic Details
Published in:Brain-X Vol. 2; no. 2
Main Authors: Lu, Zitong, Li, Wanru, Nie, Lu, Zhao, Kuangshi
Format: Journal Article
Language:English
Published: Denton John Wiley & Sons, Inc 01.06.2024
Wiley
Subjects:
brain science
Data analysis
Data processing
Datasets
electroencephalogram (EEG)
Electroencephalography
Handbooks
neuroscience
Neurosciences
psychology
Python
Topography
ISSN:2835-3153, 2835-3153
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN). This handbook comprises four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. We provide a standardized procedure for preprocessing EEG data, guide readers through detailed examples on how to read data from multiple subjects, and delve into three popular analysis methodologies.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2835-3153
2835-3153
DOI:10.1002/brx2.64