Objective. The non-stationary nature of EEG poses a major challenge to robust operation of brain–computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification.Approach. We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are ...

The measurement of synonymous and non-synonymous substitution rates (dS and dN) is useful for assessing selection operating on protein sequences or for investigating mutational processes affecting genomes. In particular, the ratio dNdS is expected to be a good proxy for ω, the ratio of fixation robabilities of non-synonymous mutations relative to that of neutral mutations. Standard methods for ...

This study tests for the stationarity and sustainability of current account deficits for ten transition economies. For this purpose, a new test is employed that allows one to test for unit roots in heterogeneous panel datasets. While the benefits from creating a panel to overcome low test power are well known, this test also offers key advantages over existing alternative panel data unit root t...

A formal test for weak stationarity of spatial and spatio-temporal random fields is proposed. We consider the cases where the spatial domain is planar or spherical and we do not require distributional assumptions for the random fields. The method can be applied to univariate or to multivariate random fields. Our test is based on the asymptotic normality of certain statistics that are functions ...

We consider a zero mean discrete time series, and define its discrete Fourier transform at the canonical frequencies. It is well known that the discrete Fourier transform is asymptotically uncorrelated at the canonical frequencies if and if only the time series is second order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing stationarity...

In this paper we define a spatio-temporal model with location dependent parameters to describe temporal variation and spatial nonstationarity. We consider the prediction of observations at unknown locations using known neighbouring observations. Further we propose a local least squares based method to estimate the parameters at unobserved locations. The sampling properties of these estimators a...

In this paper, we propose a robust voice activity detection method based on long-term stationarity (LTS) of the speech signal. The approach is motivated by the fact that noise, in timedomain, is relatively more stationary as compared to speech. We describe the use of Linear dynamic models (LDMs) as a measure of calculating the long-term stationarity of the signal and propose a voice activity de...

Time series models are often constructed by combining nonstationary effects such as trends with stochastic processes that are known (or believed) to be stationary. However, there are numerous time series models for which the stationarity of the underlying process is conjectured but not yet proven. We give an approachable introduction to the use of drift criteria (also known as Lyapunov function...