ShVEEGc: EEG Clustering With Improved Cosine Similarity-Transformed Shapley Value

Abstract

As the number of unlabeled or mislabeled electroencephalogram (EEG) increases dramatically in such applications as cerebral disease diagnosis, rehabilitation, and brain-computer interfaces, the supervised approaches that require labels or markers become inapplicable. Unfortunately, there are few reports on unsupervised studies for unlabeled EEG data, especially for unlabeled EEG clustering. To address the challenging task, we propose an effective approach named ShVEEGc for EEG clustering inspired by an improved Shapley value in cooperative game theory. The idea of ShVEEGc is first utilizing an improved cosine similarity to measure the correlations of EEG data and then calculating the improved Shapley value based on the inherent connection between unlabeled EEG data, which considers both global connections and local relationships potentially hidden in EEG data. Thus, ShVEEGc not only has good anti-interference ability but also can mine potential relationships among unlabeled EEG data. The comparison experiments with fourteen state-of-the-art EEG time series clustering algorithms on eleven real-world EEG datasets with four standard evaluation criteria demonstrate the efficacy and superiority of ShVEEGc for EEG clustering. Besides, the discussion on the impact of several different similarity measures on ShVEEGc also illustrates that the improved cosine similarity proposed in this paper is more suitable for EEG data.