EEG/MEG Kalman-like source estimation

The estimation of the instant location and strength of sources takes a considerable importance for many areas of sensor space-array processing, e.g., brain activity in non-invasive electro-medicine. State-space models are a well suited framework for solving that dynamic estimation problem and they are in the core of our studies. Related to brain electrical activity, the state estimation problem can be solved by analyzing spatio-temporal data provided by EEG/MEG measures. Nonlinear Kalman-like filter is proposed for estimating locustemporal data related to electrical activity in the brain. The experimental framework is described. Eje: Procesamiento de Señales Red de Universidades con Carreras en Informática (RedUNCI)