Electroencephalogram Clustering with Multiple Regularization Constrained Pseudo Label Propagation Optimization

As the non-invasive analyzing media, electroencephalogram (EEG) signals are widely applied in brain-computer interfaces, dysfunctional disorder diagnosis and rehabilitation. However, the techniques used in such applications are supervised and completely require EEG labels, like classification. Meanwhile, with the ever-increasing of unlabeled EEG emerged in these applications, traditional supervised techniques are becoming inapplicable, which probably degrades the development of this new-type unlabeled EEG in the emerging potential fields. To deal with the issue of unsupervised analysis for unlabeled EEG signals, we propose a multiple regularization constrained pseudo label propagation optimization model, which integrates the pseudo label propagation learning, EEG similarity adjacency matrix approximation, and label classifier learning. Subsequently, to pursuit the goal of EEG clustering with the proposed model, we transform the model to a multi-objective optimization function and propose a gradient descent-based algorithm named EEGapc (electroencephalogram clustering with pseudo label propagation) to solve it. EEGapc not only can make best use of messages passing through pairwise EEG signals in EEG-constructed graph, but can also quickly converge to its local optima. Experimental results by comparing EEGapc with 8 different types of state-of-the-art clustering algorithms on 14 real-world EEG data sets clearly demonstrate the superiority of EEGapc, and its performances with respect to average NMI (normalized mutual information), ARI (adjusted rand index), F-score and kappa are at least improved by 86.88%, 58.01%, 6.29%, 61.17%, respectively.