Temporal Pattern Localization using Mixed Integer Linear Programming

In this paper, we consider the problem of localizing the subsequence in time series which contains the dynamic pattern of interest. This is motivated by brain computer interface application where we need to analyze the dynamic pattern of brain signals in response to external stimulus. We treat the localization as a binary label assignment problem and formalize a mixed integer linear programming (MILP) problem. The optimal solution is obtained by minimizing a cost function associated with label assignment subject to empirical constraints induced by data acquisition process. We first experiment with synthetic data to evaluate the effectiveness of the proposed MILP formulation and achieve 10.8 % improvement on F-1-score. We then experiment with electrocorticographic (ECoG) data for a classification task and achieve 8.8 % improvement on accuracy using subsequences localized by our method compared to other methods.