TY  - CONF
EP  - 394
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104844294&doi=10.1109%2fIECBES48179.2021.9398836&partnerID=40&md5=9905af4eb5e09dbe844e9af4a01f87e6
A1  - Khan, D.M.
A1  - Yahya, N.
A1  - Kamel, N.
SN  - 9781728142456
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2021///
KW  - Biomedical engineering; Electroencephalography
KW  -  Akaike information criterion; Auto regressive models; Bayesian information criterion; Conventional methods; Electro-encephalogram (EEG); Enhanced resolutions; Final prediction errors; Multivariate systems
KW  -  Biomedical signal processing
ID  - scholars15123
TI  - Optimum Order Selection Criterion for Autoregressive Models of Bandlimited EEG Signals
SP  - 389
N1  - cited By 5; Conference of 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020 ; Conference Date: 1 March 2021 Through 3 March 2021; Conference Code:168430
N2  - Autoregressive (AR) model is commonly used in many areas of signal processing and is particularly significant in Electroencephalogram (EEG) related studies owing to its enhanced resolution, smoother spectra and its potential to be used on short segments of a signal. When fitting an AR model to an EEG multivariate system, selection of model order (p) is of critical importance. While the lower model orders provide inadequate representation of the signal, higher orders drastically increase noise. Therefore, identification of optimum AR model order is an open challenge. Conventional methods for estimating model orders include Akaike Information Criterion (AIC), Final Prediction Error (FPE), Bayesian Information Criterion (BIC), and Hannan Quinn (HQ). In this paper, we show how these criteria fail to determine optimal order of multivariate EEG signals that undergo mandatory filtration to separate bands and noise. Consequently, we present a novel, yet simple and effective technique to find the AR optimum order to model such systems. Extensive application of proposed method on different EEG dataset indicates that the new method gives better signal estimation with minimum possible order; thereby, improving the accuracy of reconstructed signal at reduced computational cost. © 2021 IEEE.
AV  - none
ER  -