The human nervous system is one of the most complicated systems in nature. Complex
nonlinear behaviours have been shown from the single neuron level to the system level. For …

This paper addresses the prediction of epileptic seizures from the online analysis of EEG
data. This problem is of paramount importance for the realization of monitoring/control units …

EEG is gaining recognition in the field of real-time applications. However, the EEG inverse
problem leads to poor spatial resolution in brain source localization. This paper presents an …

Diagnosis of Parkinson's disease (PD) in the early stages is very critical for effective
treatments. In this paper, we propose a simple and low-cost biomarker to diagnose PD …

Neural mass model-based tracking of brain states from electroencephalographic signals
holds the promise of simultaneously tracking brain states while inferring underlying …

This paper introduces a novel method for computationally efficient Gaussian estimation of
high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) …

We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that
explicitly includes spatiotemporal constraints, performing a trade-off between space and …

State-space models are widely employed across various research disciplines to study
unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and …

We propose a new Kalman filter algorithm to provide a formal statistical analysis of space–
time data with an autoregressive structure. The Kalman filter technique allows to capture the …

State-space modeling is a promising approach for current source reconstruction from
magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of …