Sequential estimation of mixtures of structured autoregressive models
A class of mixtures of structured autoregressive (AR) models and methods for sequential estimation within this class of models are considered. Such models and methods are motivated by the analysis of electroencephalogram (EEG) signals recorded during a cognitive fatigue experiment. Specifically, an electroencephalogram recorded from a subject who performed continuous mental arithmetic for 180 min is studied. The EEG signal is modeled via mixtures of autoregressive processes with structured prior distributions on the reciprocal roots of the characteristic AR polynomials. The use of structured prior distributions on the AR mixture components allows researchers to include scientifically meaningful information related to various states of mental alertness. On-line posterior estimation of the model parameters and related quantities is achieved using a sequential Monte Carlo algorithm. The performance of such algorithm is illustrated by applying it to simulated data and EEG data. The EEG analyses show that the mixtures of structured AR models successfully identify EEG features that may be associated with states of mental fatigue. Furthermore, one of the key features of the proposed methods is that they can be implemented in real time, allowing for automatic characterization of mental fatigue from EEG recordings.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:58-70. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.