Over the past ten years there has been a rapid increase in the number of portable electroencephalographic (EEG) systems available to researchers. However, to date, there has been little work validating these systems for event-related potential (ERP) research. Here we demonstrate that the MUSE portable EEG system can be used to quickly assess and quantify the ERP responses associated with visuospatial attention. Specifically, in the present experiment we had participants complete a standard “oddball” task wherein they saw a series of infrequently (targets) and frequently (control) appearing circles while EEG data was recorded from a MUSE headband. For task performance, participants were instructed to count the number of target circles that they saw. After the experiment, an analysis of the EEG data evoked by the target circles when contrasted with the EEG data evoked by the control circles revealed two ERP components – the N200 and the P300. The N200 is typically associated with stimulus/perceptual processing whereas the P300 is typically associated with a variety of cognitive processes including the allocation of visuospatial attention [1]. It is important to note that the physical manifestation of the N200 and P300 ERP components differed from reports using standard EEG systems; however, we have validated that this is due to the quantification of these ERP components at non-standard electrode locations. Importantly, our results demonstrate that a portable EEG system such as the MUSE can be used to examine the ERP responses associated with the allocation of visuospatial attention.
CITATION STYLE
Krigolson, O. E., Williams, C. C., & Colino, F. L. (2017). Using portable EEG to assess human visual attention. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10284 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I, pp. 56–65). Springer Verlag. https://doi.org/10.1007/978-3-319-58628-1_5
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