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...
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John Wiley & Sons, Inc
01.06.2024
Wiley |
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| Abstract | 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. |
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| AbstractList | 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 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). Abstract 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 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. |
| Author | Lu, Zitong Nie, Lu Zhao, Kuangshi Li, Wanru |
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| Cites_doi | 10.1016/j.bspc.2023.105125 10.1016/J.NEUBIOREV.2014.12.010 10.1038/sdata.2015.1 10.1177/0956797617699167 10.1162/JOCN_A_00955 10.1016/J.TICS.2006.07.005 10.1088/1741‐2552/AA6401 10.1371/journal.pone.0274847 10.1016/j.isci.2023.108501 10.1016/J.NEUROIMAGE.2020.116531 10.1155/2018/5174815 10.1016/J.JNEUMETH.2003.10.009 10.1101/2021.05.22.445245 10.1101/2023.04.26.538469 10.1523/JNEUROSCI.3577‐09.2009 10.1152/jn.00540.2011 10.1109/ICASSP49357.2023.10096587 10.1007/s00521‐022‐08178‐1 10.3389/FNINS.2013.00267 10.3389/NEURO.06.004.2008 10.1016/J.NEURON.2008.10.043 10.5281/ZENODO.4273674 10.1016/J.NEUROIMAGE.2022.119754 10.1162/JOCN_A_01068 10.1523/JNEUROSCI.2860‐17.2017 10.3389/fncom.2021.754587 10.3389/FNINF.2020.563669 10.1016/J.TICS.2015.02.005 |
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| Copyright | 2024 The Author(s). published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology. 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive... Abstract This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in... |
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| SubjectTerms | brain science Data analysis Data processing Datasets electroencephalogram (EEG) Electroencephalography Handbooks neuroscience Neurosciences psychology Python Topography |
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| Title | An easy‐to‐follow handbook for electroencephalogram data analysis with Python |
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