NDLI: A computationally efficient order statistics based outlier detection technique for EEG signals

Content Provider	IEEE Xplore Digital Library
Author	Giri, B.K. Sarkar, S. Mazumder, S. Das, K.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., IIEST, Shibpur, India (Sarkar, S.) \|\| Dept. of Phys. Sci., IISER Kolkata, Kolkata, India (Giri, B.K.) \|\| Dept. of Math. & Stat., IISER Kolkata, Kolkata, India (Mazumder, S.; Das, K.)
Abstract	Detecting artifacts in EEG data produced by muscle activity, eye blinks and electrical noise is a common and important problem in EEG applications. We present a novel outlier detection method based on order statistics. We propose a 2 step procedure comprising of detecting noisy EEG channels followed by detection of noisy epochs in the outlier channels. The performance of our method is tested systematically using simulated and real EEG data. Our technique produces significant improvement in detecting EEG artifacts over state-of-the-art outlier detection technique used in EEG applications. The proposed method can serve as a general outlier detection tool for different types of noisy signals.
Starting Page	4765
Ending Page	4768
File Size	1656109
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319459
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Noise measurement Muscles Dispersion Robustness Independent component analysis Electric potential
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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