Diffusion adaptive filtering for modelling brain responses to motor tasks

Author

Eftaxias, Konstantinos ; Sanei, Saeid

Author_Institution

Dept. of Comput., Univ. of Surrey, Guildford, UK

fYear

2013

fDate

1-3 July 2013

Firstpage

1

Lastpage

5

Abstract

Diffusion adaptation combined with an adaptive way of estimating brain connectivity is used here in order to model specific motor tasks. We use Kalman filtering to fit an adaptive multivariate autoregressive model to our data and compute the connectivity measure which is a time-varying version of directed transfer function (DTF). The resulting method is used to classify the data from movement-related activities. The comparison between the proposed method and the non-diffusion method shows superiority of the former one.

Keywords

adaptive filters; autoregressive moving average processes; brain; estimation theory; transfer functions; DTF; adaptive multivariate autoregressive model; brain connectivity estimation; brain responses; diffusion adaptation; diffusion adaptive filtering; directed transfer function; motor tasks; time-varying version; Adaptation models; Brain modeling; Computational modeling; Electroencephalography; Equations; Kalman filters; Mathematical model; Diffusion adaptation; Kalman filtering; brain connectivity; brain-computer interface; directed transfer function;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing (DSP), 2013 18th International Conference on

Conference_Location

Fira

ISSN

1546-1874

Type

conf

DOI

10.1109/ICDSP.2013.6622799

Filename

6622799