Particle filtering for EEG source localization and constrained state spaces
نویسندگان
چکیده
...........................................................................................................................v List of Figures ..................................................................................................................viii List of Tables ...................................................................................................................x Chapter 1: Introduction ....................................................................................................1 1.1 Motivation, Background, and Problem Statement ...............................................1 1.1.1 The EEG Source Localization Problem. .....................................................4 1.1.2 Particle Filtering in Constrained State-Spaces. ...........................................5 1.2 Research Contributions ........................................................................................6 1.3 Organization .........................................................................................................8 Chapter 2: Literature Review ...........................................................................................10 2.1 Problem Statement ...............................................................................................10 2.2 Optimal State Estimation in Linear Models .........................................................13 2.3 Approximate Solutions in Nonlinear Models ......................................................15 2.3.1 The Extended Kalman Filter. ......................................................................15 2.3.2 The Unscented Kalman Filter. ...................................................................17 2.4 The Particle Filter Framework .............................................................................18 2.4.1 Monte Carlo Sampling. ...............................................................................18 2.4.2 Importance Sampling. .................................................................................19 2.4.3 Sequential Importance Sampling. ..............................................................20 2.4.4 The Particle Filter. .....................................................................................24 Chapter 3: The Constrained Particle Filter ......................................................................29 3.
منابع مشابه
Combination of Beamforming and Synchronization Methods for Epileptic Source Localization, using Simulated EEG Signals
Localization of sources in patients with focal seizure has recently attracted many attentions. In the severe cases of focal seizure, there is a possibility of doing neurosurgery operation to remove the defected tissue. The prosperity of this heavy operation completely depends on the accuracy of source localization. To increase this accuracy, this paper presents a new weighted beamforming method...
متن کاملMagnetic source localization via linearly constrained minimum variance spatial filtering
Spatial filtering has a long history of successful application in radar and sonar problems (e.g. [1,2]). The process of spatial filtering is also known as beamforming, since early spatial filters were designed to form pencil beams for either receiving or transmitting signals. More recently, spatial filtering has been applied to EEG and MEG [3–6, 8] to localize intracranial sources of electrical...
متن کاملA Unique Approach of Noise Elimination from Electroencephalography Signals between Normal and Meditation State
In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signa...
متن کاملBeamforming Techniques Applied in EEG Source Analysis
The electrical activity of the human brain causes time-varying potential differences on the head surface. The electroencephalogram (EEG) is a measurement of these potential differences between electrodes on the head. When the electrical brain activity is limited to a small region in the brain (e.g., during epileptic seizures), the source region within the brain can be localised by analysing the...
متن کاملComputationally-efficient algorithms for sparse, dynamic solutions to the EEG source localization problem.
OBJECTIVE Electroencephalography (EEG) and magnetoencephalography (MEG) non-invasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have...
متن کامل