Graduate Research and Discovery Symposium (GRADS)

Title

EEGnet: A Web Platform for Collaborative EEG Research

Advisor

Brian C. Dean

Document Type

Poster

Department

Computer Science

Publication Date

Spring 2013

Abstract

EEG research is often impeded due to a lack of large-scale standardized data sets that can be used for training and validating algorithms. To address this issue, we have developed EEGnet, a web-based platform that enables a distributed team of experts to assemble and annotate events in large scalp EEG datasets in a streamlined fashion. EEGnet supports most features of modern digital EEG visualization software, such as multiple montages, digital filtering, and gain adjustment. It allows annotation of segments of EEG signals in single channels or annotation of epochs encompassing all channels. EEGnet supports the visualization of short EEG files and also long EEG files, up to 24 hours in length, but only for data in a 10-20 montage (with one ECG channel). Advanced visualization capabilities are provided for displaying the output of automated interpretation algorithms and comparing these results with annotations from human experts. We hope to make the EEG research community more aware of EEGnet as a means of facilitating large-scale collaborative research initiatives.

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