DYNAMICAL FACTOR ANALYSIS OF RHYTHMIC MAGNETOENCEPHALOGRAPHIC ACTIVITY

Dynamical factor analysis (DFA) is a generative dynamical algorithm, with linear mapping from factors to the observations and nonlinear mapping of the factor dynamics. The latter is modeled by a multilayer perceptron. Ensemble learning is used to estimate the DFA model in an unsupervised manner. The performance of the DFA have been tested in a set of artificially generated noisy modulated sinusoids. Furthermore, we have applied it to magnetoencephalographic data containing bursts of oscillatory brain activity. This paper shows that DFA can correctly estimate the underlying factors in both data sets.