An evaluation of autoregressive spectral estimation model order for brain-computer interface applications.

TitleAn evaluation of autoregressive spectral estimation model order for brain-computer interface applications.
Publication TypeJournal Article
Year of Publication2006
AuthorsKrusienski, DJ, McFarland, DJ, Wolpaw, J
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Volume1
Pagination1323–1326
Date Published09/2006
ISSN1557-170X
KeywordsUser-Computer Interface
Abstract

Autoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies.

URLhttp://www.ncbi.nlm.nih.gov/pubmed/17946038
DOI10.1109/IEMBS.2006.259822

You are here