Onset Detection of Pulse-Shaped Bioelectrical Signals Using Linear State Space Models

Blind synchronization and detection of Nyquist pulse shaped QAM signals

Detection Limits for Linear Non-Gaussian State-Space Models

The performance of nonlinear fault detection schemes is hard to decide objectively, so Monte Carlo simulations are often used to get a subjective measure and relative performance for comparing different algorithms. There is a strong need for a constructive way of computing an analytical performance bound, similar to the Cramér-Rao lower bound for estimation. This paper provides such a result fo...

Normalizing Empirically Underidentified Linear State-Space Models

Normalizing latent-variable models in empirical work has sometimes more influence on statistical inference than commonly appreciated. In this paper, I show how relating non-identification to an invariance property of the likelihood function under certain groups of parameter transformations helps understanding the influence of normalization on inference and can guide the choice of identifying re...

Mean Shift Detection for State Space Models

In this paper we develop and validate a procedure for testing against a shift in mean in the observa-tions and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the true state of a sys-tem may be corrupted by measurement noise (usually, a Gaussian noise process...

Robot control system using SMR signals detection

One of the important issues in designing a brain-computer interface system is to select the type of mental activity to be imagined. In some of these systems, mental activity varies with user intent and action that must be controlled by the brain-computer system, and in a number of other signals, the received signals contain the same activity-related mental activity that should be performed by t...