EEGcMamba: EEG Clustering via State Space Model

Electroencephalography (EEG) is extensively employed for supervised analysis across various scenarios, including disease diagnosis, neurocognition, and brain-computer interfaces. It has heightened the demand for effective EEG labeling. However, the manual labeling process is labor-intensive and time-consuming. Furthermore, there are few studies that attempt to decode information from EEG in an unsupervised manner using deep learning. In this paper, we present a novel end-to-end EEG clustering approach via a state space model (SSM), termed EEGcMamba. EEGcMamba introduces a universal backbone, BrainMamba, for EEG feature learning, and incorporates both a weighted instance-level contrast head and a dual-branch cluster-level contrast head for contrastive learning. Specifically, BrainMamba utilizes a slice-aware scanning mechanism to input segmented EEG slices through multiple sequences, capturing fine-grained contextual connections between slices. To mitigate risks of pushing similar EEG samples apart further in the embedding space, we introduce weight terms from the data space during calculating the instance-level contrastive losses. Furthermore, in the cluster-level contrast head, the assignment-discrimination branch accounts for the clustering distribution consistency, while the semantic-aware branch employs pseudo-labeling semantics to establish group-instance discrimination. Extensive experiments on 11 EEG benchmark datasets demonstrate the superiority of EEGcMamba over existing advanced methods. The code will be available at Github.