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.

Requirements

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!

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 dаta: 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.

Overview

Section 1: Introduction to EEG

Lecture 1 A short history

Lecture 2 The Origins of EEG data

Lecture 3 How to record EEG

Lecture 4 What is a Good EEG

Section 2: Frequency and time domain analyses

Lecture 5 Different types of brain frequencies

Lecture 6 Frequency analysis

Lecture 7 Time-domain analysis and ERP

Lecture 8 Time-frequency analysis

Section 3: The essence of artifacting

Lecture 9 Different types of noises

Lecture 10 A symphony of noises in action

Lecture 11 Filters

Section 4: Start working with Python

Lecture 12 ANACONDA installation

Lecture 13 Basics of coding-Variables

Lecture 14 Basics of coding-Dictionary

Lecture 15 Working with functions

Lecture 16 Control statements

Lecture 17 Plotting

Lecture 18 MNE Installation

Section 5: Pre-processing with MNE-Python

Lecture 19 Importing and reviewing EEG data with MNE

Lecture 20 Filtering the data with MNE

Lecture 21 Saving steps into files

Lecture 22 Removing artifacts considering ICA

Lecture 23 Manual removal of remaining artifacts

Section 6: Frequency analysis in Python and MNE

Lecture 24 Importing EEG in Python

Lecture 25 Frequency analysis in Python with FFT

Lecture 26 Frequency analysis in MNE

Lecture 27 Building custom frequency topographic maps

Section 7: Review of important ERPs

Lecture 28 Time course of stimuli in the brain

Lecture 29 The P300 component

Lecture 30 The N170 component

Lecture 31 The language-related components

Lecture 32 Age and development ERP issues

Section 8: ERP and time-frequency analysis in Python and MNE

Lecture 33 Trial-based EEG data

Lecture 34 Visualize single trials in Python

Lecture 35 Compute mean ERPs in Python

Lecture 36 Structure of EEG data with seperate condition

Lecture 37 Attaching labels to continues EEG data in MNE

Lecture 38 Epoching the events for MNE

Lecture 39 Compute and visualize ERPs in MNE

Lecture 40 Compute and visualze time-frequency in MNE

Lecture 41 Final words

Section 9: Extra+Advance with ChatGPT

Lecture 42 Extra Advance with ChatGPT

Lecture 43 Example

Lecture 44 ChatGPT Prompts

Section 10: Course Materials

Lecture 45 Course Materials

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.