EEG/ERP Analysis with Python and MNE: An Introductory Course

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Neura Skills

7:44:41

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1. Introduction to EEG

1. A short history.mp4

07:25

2. The Origins of EEG data.mp4

10:24

3. How to record EEG.mp4

15:35

4. What is a Good EEG.mp4

13:29

2. Frequency and time domain analyses

1. Different types of brain frequencies.mp4

18:34

2. Frequency analysis.mp4

18:29

3. Time-domain analysis and ERP.mp4

21:27

4. Time-frequency analysis.mp4

07:58

3. The essence of artifacting

1. Different types of noises.mp4

15:13

2. A symphony of noises in action.mp4

04:51

3. Filters.mp4

13:37

4. Start working with Python

1. ANACONDA installation.mp4

03:09

2. Basics of coding-Variables.mp4

28:12

3. Basics of coding-Dictionary.mp4

15:44

4. Working with functions.mp4

18:29

5. Control statements.mp4

05:51

6. Plotting.mp4

09:26

7. MNE Installation.mp4

02:40

5. Pre-processing with MNE-Python

1. Importing and reviewing EEG data with MNE.mp4

14:12

2. Filtering the data with MNE.mp4

11:22

3. Saving steps into files.mp4

03:10

4. Removing artifacts considering ICA.mp4

20:27

5. Manual removal of remaining artifacts.mp4

07:50

6. Frequency analysis in Python and MNE

1. Importing EEG in Python.mp4

07:53

2. Frequency analysis in Python with FFT.mp4

07:48

3. Frequency analysis in MNE.mp4

11:34

4. Building custom frequency topographic maps.mp4

10:30

7. Review of important ERPs

1. Time course of stimuli in the brain.mp4

09:17

2. The P300 component.mp4

11:15

3. The N170 component.mp4

09:37

4. The language-related components.mp4

16:56

5. Age and development ERP issues.mp4

04:51

8. ERP and time-frequency analysis in Python and MNE

1. Trial-based EEG data.mp4

13:30

2. Visualize single trials in Python.mp4

10:28

3. Compute mean ERPs in Python.mp4

03:58

4. Structure of EEG data with seperate condition.mp4

03:36

5. Attaching labels to continues EEG data in MNE.mp4

12:48

6. Epoching the events for MNE.mp4

11:04

7. Compute and visualize ERPs in MNE.mp4

14:38

8. Compute and visualze time-frequency in MNE.mp4

09:41

9. Final words.mp4

02:36

9. Extra+Advance with ChatGPT

1. Extra Advance with ChatGPT.mp4

02:39

2. Example.mp4

02:28

3. ChatGPT Prompts.html

10. Course Materials

1.1 basicsofcoding.zip

1.2 edf eeg.zip

1.3 eegfiltered.zip

1.4 erd ers.zip

1.5 erpandtf py mne.zip

1.6 freqpymne.zip

1.7 Labels.txt

1.8 One Ch EEG.txt

1.9 preprocessingmne.zip

1.10 trial-based.zip

1. Course Materials.html

Description

From Brain Signal Basics to Advanced Analysis

What You'll Learn?

Understanding the Basics of Electrophysiology Data
Start Working with Python
Gain Expertise in Frequency Domain Analysis of Electrophysiological Data
Learn to Identify and Analyze ERPs
Acquire the Practical Skills to Conduct Time-Frequency Analysis using Python and the MNE library.

Who is this for?

This course is intended for a diverse range of learners who have an interest in electrophysiology data analysis, regardless of their background or experience.

Beginners in Electrophysiology: Individuals who are new to the field of electrophysiology and want to understand the fundamentals and practical aspects of data analysis will find this course to be an excellent starting point.

Students and Researchers: Undergraduate and graduate students, as well as researchers in fields such as psychology, neuroscience, cognitive science, or related disciplines, who want to incorporate electrophysiological data analysis into their studies or research projects.

Curious Minds: Individuals with a general interest in the brain, cognitive processes, or the applications of data analysis in scientific research will find the course engaging and informative.

What You Need to Know?

No Programming Skill Required. The only software requirement is Anaconda, which is a popular Python distribution that simplifies package management and environment setup. You do not to have it installed in advance, since I will teach how to install and use it during the course. So the only thing you need is a computer and a keyboard!

More details

Description
Whether you're a novice in the field or looking to enhance your skills, this course is your gateway to understanding the basics of EEG data analysis.
A Journey Through EEG History: Join us on a fascinating exploration of the origins of EEG data, from its introduction to the cutting-edge techniques used today.
Recording EEG Data: Learn the essentials of recording high-quality EEG data and what constitutes good EEG data. Learn the basics of artifacting, recognizing different types of noises, and witness noise reduction in action through various filtering techniques.
Frequency and Time Domain Analyses: Demystify the complexities of frequency and time domain analyses. Understand different brain frequencies, conduct frequency analysis, explore time domain analysis and Event-Related Potentials (ERPs), and venture into time-frequency analysis.
Python for EEG Analysis: Familiarize yourself with Python basics, ANACONDA installation, coding fundamentals, and data plotting. Install MNE (MNE-Python) and kickstart your journey into EEG analysis.
MNE-Python Pre-processing: Explore MNE-Python for pre-processing EEG data. Import data, gain an overview, implement filtering, reject bad channels, and perform Independent Component Analysis (ICA) for noise removal.
Frequency Analysis with Python and MNE: Utilize MNE's PSD function for frequency analysis. Create visually stunning frequency band plots and topographic maps to explore the mysteries hidden within EEG data.
Exploring Important ERPs: Review essential Event-Related Potentials (ERPs), such as the P300 and N170 components, along with language-related components. Understand their significance and applications in EEG analysis.
ERP and Time-Frequency Analysis in Python and MNE: Master the art of visualizing ERPs using Python. Leverage MNE for interpreting ERPs and delve into plotting and interpreting time-frequency analyses.
Why Choose This Course:
This course is designed for beginners, providing a seamless transition from the basics to advanced EEG analysis techniques. With hands-on Python coding exercises and practical examples using MNE-Python, you'll gain practical skills that are essential for anyone seeking proficiency in EEG data analysis.
Join us on this educational journey, and let's unravel the mysteries of EEG together! Enroll now to kickstart your EEG analysis adventure.
Who this course is for:
This course is intended for a diverse range of learners who have an interest in electrophysiology data analysis, regardless of their background or experience.
Beginners in Electrophysiology: Individuals who are new to the field of electrophysiology and want to understand the fundamentals and practical aspects of data analysis will find this course to be an excellent starting point.
Students and Researchers: Undergraduate and graduate students, as well as researchers in fields such as psychology, neuroscience, cognitive science, or related disciplines, who want to incorporate electrophysiological data analysis into their studies or research projects.
Curious Minds: Individuals with a general interest in the brain, cognitive processes, or the applications of data analysis in scientific research will find the course engaging and informative.

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Hello everyone, Thank you for checking out the NeuraSkills courses on Udemy. The NeuraSkills is dedicated educator in the fascinating world of cognitive neuroscience, working to better understand how the brain works. The NeuraSkills teaches students about cognitive neuroscience, programming, and designing experiments. NeuraSkills adore neuroscience and is really passionate about helping others understand how our brains function through a series of NeuraSkills Courses. The NeuraSkills aim is to share the things we love in neuroscience and make anything that seems difficult, simple. We think that everyone can learn a lot if they are taught in the right way. Learning should be enjoyable and exciting.

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