Coupled Hidden Markov Model for Electrocorticographic Signal Classification

This paper investigates the spatial and temporal dynamics in multi-channel electrocorticographic (ECoG) time series signals using Coupled Hidden Markov Model (CHMM). The signals are recorded in a hand motion control task, when the subject uses a joystick to move a cursor appearing on the screen to hit a virtual target. We detect signal onset using two heuristic schemes based on the experiment process. We apply CHMM to capture the spatial and temporal dynamics between two different channels within fixed length of duration, where each channel is modelled by HMM. The interdependence between two channels are modelled by transitions between hidden states of different individual HMM. There are eight possible directions that the target may appear. We learn eight sets of parameters using EM algorithm to characterize the signal patterns for each possible direction of movement. Given the test signals, the set of learned parameters which produces highest probability likelihood decides the class label. The effectiveness of the model is measured by classification accuracy. The results indicate that CHMM outperforms conventional HMM in most of the cases and is significantly better than first order autoregressive model.