Optimized Hidden Markov Model for Classification of Motor Imagery EEG Signals

A motor imagery related electroencephalogram (EEG) classification technique through the Hidden Markov Model (HMM) is presented for brain computer interaction (BCI) applications. We describe a method for classification of EEG signals using optimized HMM and the proposed method was focus on the optimization process based on Harmony Search algorithm. By using the raw EEG signals, EEG features obtained as the wavelet coefficients feature vectors between the optimal channels by using discrete wavelet transform approach. In order to optimize the classifier, firstly, Baum-Welch algorithm is applied to parameter learning of HMM. In this case, harmony search algorithm (HSA) is sufficiently adaptable to allow incorporation of other technique, such as Baum-Welch algorithm. In order to prove the performance of the proposed technique, three class motor imagery (left hand, right hand, foot) EEG signals were used as inputs of the optimized HMM classifier. The experimental results confirmed that the proposed method has potential in classifying the motor imagery EEG signals.