  In this paper, we propose a regularized mixture probabilistic model to
cluster matrix data and apply it to brain signals. The approach is able to
capture the sparsity (low rank, small/zero values) of the original signals by
introducing regularization terms into the likelihood function. Through a
modified EM algorithm, our method achieves the optimal solution with low
computational cost. Theoretical results are also provided to establish the
consistency of the proposed estimators. Simulations show the advantages of the
proposed method over other existing methods. We also apply the approach to two
real datasets from different experiments. Promising results imply that the
proposed method successfully characterizes signals with different patterns
while yielding insightful scientific interpretation.
