01404nas a2200181   4500008004100000022001400041245011100055210006900166260001200235300001600247490000600263520080700269653002801076100002301104700002601127700002101153856004801174       2006                        eng d  a1557-170X00aAn evaluation of autoregressive spectral estimation model order for brain-computer interface applications.0 aevaluation of autoregressive spectral estimation model order for  c09/2006  a1323–13260 v13 aAutoregressive (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.10aUser-Computer Interface1 aKrusienski, D., J.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/17946038