Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

Process capability estimation for leukocyte filtering process in blood service: a comparison study

This article considers a comparison study between different non-normal process capability estimation methods and utilizing themin the leukocyte filtering process in blood service sectors. Since the amount of leukocyte in a unit of the blood is a critical issue inthe blood transfusion process and patient safety, estimating and monitoring the capability of the leukocyte filtering process to meetthe target window is very important for blood service sectors. However, observed data from the leukocyte filtering process showthat the leukocyte levels after filtering demonstrate a right skewed distribution and applying conventional methods with a normalityassumption fails to provide trustful results. Hence, we first conduct a simulation study to compare different methods in estimating theprocess capability index of non-normal processes and then we apply these techniques to obtain the process capability of the leukocytefiltering process. The study reveals that the Box-Cox transformation method provides reliable estimation of the process capability ofthe leukocyte filtering process.