Abstract

Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices–using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication.

Highlights

  • Electroencephalography (EEG) is a suitable non-invasive measure for investigating the temporal dynamics of mental processing because of its high temporal resolution and cost-effectiveness

  • We introduce EasyEEG toolbox, in which several multivariate analyses are included for processing EEG sensor data and testing cognitive hypotheses

  • We demonstrate scripts for applying four analysis methods and their outcomes as follows (the entire script was running in a Jupyter notebook, see: https://github.com/ray306/EasyEEG/ blob/master/tests/(Demo)%20Face%20perception.ipynb)

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Summary

Introduction

Electroencephalography (EEG) is a suitable non-invasive measure for investigating the temporal dynamics of mental processing because of its high temporal resolution and cost-effectiveness. ERP analyses are mostly based on responses in individual sensors or an average of a group of selected sensors. This “selecting sensors” analysis method is not optimal, because it faces various challenges (Tian and Huber, 2008; Tian et al, 2011). Only relying on data in a few sensors cannot differentiate between changes in the distribution of neural sources vs changes in the magnitude of neural sources. Selecting sensors may introduce subjective bias during the selection processes, and sometimes data in different

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