Abstract
Electroencephalography (EEG) uses electrodes placed on the scalp to measure signals generated by large populations of neurons in the brain. These electrical signals are very small (in the microvolt range), and the recorded EEG data includes noise and artifacts such as electrical activity from eyeblinks and muscle movements. Unstable or poor electrode connections, drift, and line noise from electrical power lines further contaminate the data. Thus, raw EEG data is messy, and many steps are required to get “clean” data for statistical analysis. The sequence of steps is referred to as a “pipeline” and is commonly performed using commercial software such as BrainVision Analyzer 2. Not only is this software costly, but it also requires a great deal of time and manual work. MNE is a free, open-source Python-based package that can be used to build a processing pipeline for EEG data that is robust, computationally reproducible, and mostly automated. We will demonstrate its use by processing and analyzing EEG data recorded from 30 participants in an oddball experiment designed to investigate the P3a and P3b components of the EEG signal, further increase the field’s understanding of brain activity while experiencing expected and surprise stimuli. With large datasets, code can automate the process for data analysis to combine and interpret multiple dimensions of data in a statistically powerful and timely manner.
Language: English
Published on: Mar 27, 2024
Published by: University of North Georgia
In partnership with: Paradigm Publishing Services
Keywords:
© 2024 Eddie Ramirez, published by University of North Georgia
This work is licensed under the Creative Commons Attribution 4.0 License.