An analysis of two common reference points for EEGS

Lopez, S., Gross, A., Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, Golmohammadi, M., Obeid, I. and Picone, J. (2016) An analysis of two common reference points for EEGS. In: 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 3 Dec 2016, Philadelphia, PA, USA.

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Abstract

Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data in the TUH EEG Corpus. In this study, we explore the impact this variability has on machine learning performance. We compare the statistical properties of features generated using these two montages, and explore the impact of performance on our standard Hidden Markov Model (HMM) based classification system. We show that a system trained on LE data significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We also demonstrate that performance of a system trained on both data sets is somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data suggests that mean, variance and channel normalization should be considered. However, cepstral mean subtraction failed to produce an improvement in performance, suggesting that the impact of these statistical differences is subtler.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781509067138
Identifier: 10.1109/spmb.2016.7846854
Page Range: pp. 1-5
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Electroencephalography, Hidden Markov models, Brain modeling, Electrodes, Ear, Cepstral analysis, Data models
Subjects: Computing
Related URLs:
Depositing User: Su Yang
Date Deposited: 04 Jun 2021 13:44
Last Modified: 28 Aug 2021 07:15
URI: http://repository.uwl.ac.uk/id/eprint/7930

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