ECoG Analysis with Affinity Propagation Algorithm

Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtained in the second session. Here we propose a new approach to analyze ECoG trails in the case of session-to-session transfer exists. In our approach, firstly, dimension reduction is performed with independent component analysis (ICA) decomposition. Secondly, ECoG trials are clustered by an unsupervised learning algorithm called affinity propagation. Primary experimental results show that the proposed approach gives the reasonable result than that using the classical K-means clustering algorithm.