Discovering and visualizing patterns in EEG data

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Publication Type pre-print
School or College <blank>
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Creator Anderson, Erik Wesley
Other Author Chong, Catherine; Preston, Gilbert A.; Silva, Claudio T.
Title Discovering and visualizing patterns in EEG data
Date 2013-01-01
Description Brain activity data is often collected through the use of electroen-cephalography (EEG). In this data acquisition modality, the electric fields generated by neurons are measured at the scalp. Although this technology is capable of measuring activity from a group of neurons, recent efforts provide evidence that these small neuronal collections communicate with other, distant assemblies in the brain's cortex. These collaborative neural assemblies are often found by examining the EEG record to find shared activity patterns. In this paper, we present a system that focuses on extracting and visualizing potential neural activity patterns directly from EEG data. Using our system, neuroscientists may investigate the spectral dynamics of signals generated by individual electrodes or groups of sensors. Additionally, users may interactively generate queries which are processed to reveal which areas of the brain may exhibit common activation patterns across time and frequency. The utility of this system is highlighted in a case study in which it is used to analyze EEG data collected during a working memory experiment.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 105
Last Page 112
Language eng
Bibliographic Citation Anderson, E. W., Chong, C., Preston, G. A., & Silva, C. T. (2013). Discovering and visualizing patterns in EEG data. IEEE Pacific Visualization Symposium, 6596134, 105-12.
Rights Management (c) 2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 11,238,401 bytes
Identifier uspace,18355
ARK ark:/87278/s68371zn
Setname ir_uspace
Date Created 2014-02-20
Date Modified 2021-05-06
ID 711314
Reference URL https://collections.lib.utah.edu/ark:/87278/s68371zn
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