NDLI: Abnormal event detection in EEG imaging - Comparing predictive and model-based approaches

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Abnormal event detection in EEG imaging - Comparing predictive and model-based approaches

Content Provider	IEEE Xplore Digital Library
Author	Dutta, J.K. Banerjee, B. Ilin, R. Kozma, R.
Copyright Year	2014
Description	Author affiliation: Sensors Directorate, Air Force Res. Lab., Wright-Patterson AFB, OH, USA (Ilin, R.) \|\| Dept. Electr. & Comput. Eng., Univ. of Memphis, Memphis, TN, USA (Dutta, J.K.; Banerjee, B.) \|\| Dept. Math. Sci., Univ. of Memphis, Memphis, TN, USA (Kozma, R.)
Abstract	The detection of abnormal/unusual events based on dynamically varying spatial data has been of great interest in many real world applications. It is a challenging task to detect abnormal events as they occur rarely and it is very difficult to predict or reconstruct them. Here we address the issue of the detection of propagating phase gradient in the sequence of brain images obtained by EEG arrays. We compare two alternative methods of abnormal event detection. One is based on prediction using a linear dynamical system, while the other is a model-based algorithm using expectation minimization approach. The comparison identifies the pros and cons of the different methods, moreover it helps to develop an integrated and robust algorithm for monitoring cognitive behaviors, with potential applications including brain-computer interfaces (BCI).
Starting Page	10
Ending Page	15
File Size	1553704
Page Count	6
File Format	PDF
ISBN	9781479945436
DOI	10.1109/CIBCI.2014.7007786
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rabbits Visualization Heuristic algorithms Brain models Prediction algorithms Electroencephalography
Content Type	Text
Resource Type	Article

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