Module 9 Overview:
This module is designed to introduce data preprocessing topics such as: re-referencing, filtering, bad channels, baseline correction, epoching, and artifact correction and rejection.
Units:
Filtering
Overview: This unit is designed to introduce filtering.
Learning Goals:
- Describe the general preprocessing steps and explain their purpose.
- Explain why we filter EEG data
- Discuss different types of frequency filters and select an appropriate filter for particular types of noise
- Explain a time domain filter
Materials Included: Slides, In-class Lab, Take-home Lab
Pre-processing Lab: Filtering EEG/ERP Data: The purpose of this lab is to understand the effects of filtering in processing electrophysiological data. Students will learn to open a file and filter the data.
Bad Channels through Baseline Correction
Overview: This unit introduces epoching and baseline correction and why we do them.
Learning Goals:
- Detect a bad channel
- Explain the purpose of re-referencing and select an appropriate reference
- Describe epoching of the data
- Explain baseline correction and why we do it
Materials Included: Slides, In-class Lab, Take-home Lab
Pre-processing Lab: Epoching: The purpose of this lab is to learn to identify bad channels in their data and process the data through the epoching stage of analysis. This lab includes learning to identify bad channels, re-referencing the data, and epoching.
Artifact Rejection
Overview: This unit introduces the different kinds of artifacts seen in EEG/ERP data and the two major methods to detect and address them, rejection and correction.
Learning Goals:
- Identify what artifacts are and explain how to spot them
- Describe artifact detection and rejection
- Explain artifact correction
Materials Included: Slides, In-class Lab, Take-home Lab
Pre-processing Lab: Artifact Rejection: The purpose of this lab is to learn to do artifact rejection by hand which will also give you a better sense of what artifacts look like. You will also learn the impact of artifact rejection choices.
Independent Components Analysis
Overview: This unit covers using Independent Components Analysis (ICA) to correct artifacts.
Learning Goals:
- Understand why you would use ICA over artifact rejection techniques
- Explain basic idea of how ICA corrects EEG data (i.e. corrects for artifacts)
- Recognize classic ICA patterns for EEG neural signals and common artifacts
- Learn to classify components as artifacts or neural signals
Materials Included: Slides, In-class Lab, Take-home Lab
Pre-processing Lab: Independent Components Analysis: The purpose of this lab is to learn artifact correction using independent components analysis (ICA) in conjunction with scrolling eeg data corresponding to each ICA component. This will also give you a better sense of what artifact and neural components look like. You will also learn the impact of artifact correction choices.