Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.