Diffusion adaptive filtering for modelling brain responses to motor tasks

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.