Mixture of autoregressive modeling orders and its implication on single trial EEG classification.

ABSTRACT: Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR's modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (B

SUBMITTER: Atyabi A 

PROVIDER: S-EPMC5521280 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature