NDLI: Improved EEG event classification using differential energy

Content Provider	IEEE Xplore Digital Library
Author	Harati, A. Golmohammadi, M. Lopez, S. Obeid, I. Picone, J.
Copyright Year	2015
Description	Author affiliation: Neural Engineering Data Consortium, Temple University, Philadelphia, Pennsylvania, USA (Harati, A.; Golmohammadi, M.; Lopez, S.; Obeid, I.; Picone, J.)
Abstract	Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.
Starting Page	1
Ending Page	4
File Size	1171707
Page Count	4
File Format	PDF
ISBN	9781509013500
DOI	10.1109/SPMB.2015.7405421
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	Frequency-domain analysis Hidden Markov models Feature extraction Brain modeling Electroencephalography Mel frequency cepstral coefficient
Content Type	Text
Resource Type	Article

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