Eeg/Erp Analysis With Python And Mne: An Introductory Course

Eeg/Erp Analysis With Python And Mne: An Introductory Course
Published 2/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

From Brain Signal Basics to Advanced Analysis

What you’ll learn

Understanding the Basics of Electrophysiology Data

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

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

Lecture 9 Different types of noises

Lecture 10 A symphony of noises in action
Lecture 11 Filters

Lecture 12 ANACONDA installation

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

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