NDLI: Dynamic time warp distances as feedback for EEG feature density

Content Provider	IEEE Xplore Digital Library
Author	Ward, Christian R. Obeid, Iyad
Copyright Year	2015
Description	Author affiliation: Electrical Engineering Department at Temple University Philadelphia, PA 19121 USA (Ward, Christian R.)
Abstract	This work presents a feature detection method built around a dynamic time-warping (DTW) -based confusion matrix. It can be used to discern potential features with minimal data manipulation and minimal prior knowledge. This technique provides a robust distance measurement between sample electroencephalogram (EEG) signals that form the basis of a confusion matrix indexed against events carried out as part of shared data from the PhysioNet Imagined Motion database. DTW matches signals by reconstructing the common time axis to match the amplitudes of signals as closely as possible. The resulting confusion matrices present visual patterns, or motifs, useful for distinguishing artifacts and potential features of interest in each motion trial. The results suggest this technique could be used as a tool to find areas of interest within EEG recordings and then to map them to similar occurrences.
Starting Page	1
Ending Page	5
File Size	5677472
Page Count	5
File Format	PDF
ISBN	9781509013500
DOI	10.1109/SPMB.2015.7405419
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Visualization Brain Image color analysis EEG Feature extraction Electroencephalography Dynamic Time Warping Outlier Detection Indexes Feature Development Confusion Matrix
Content Type	Text
Resource Type	Article

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