Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations

As an important research branch of artificial intelligence, decoding motor imagery electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing noninvasive brain-computer interfaces (BCIs). Clustering becomes a crucial manner in decoding MI-EEG due to lack of effective labels. However, recently clustering methods for EEG rely on modeling time-series characteristics with high dimensions, as well as classical clustering frameworks, which requires a large iterative consumption. To address these challenges, we proposed a novel Efficient one-step EEG spectral clustering (EosEEGsc) method for multi-trial scenarios. Firstly, two forms of covariance-base representations are constructed for the multi-trial MI-EEG samples using unsupervised manner. Subsequently, the similarity graphs are constructed according to such representation, and a weighting strategy between similarity graphs and spectral embedding is progressively iterated using a one-step spectral clustering manner. Comparative experiments were conducted on ten MI-EEG datasets from BCI Competitions. The EosEEGsc achieved better clustering performance with lower time complexity quickly converged to local optima during the one-step framework. Ablation studies have demonstrated the necessity of two key components of EosEEGsc, and parameter sensitivities have validated the robustness. Our method offers a novel option for online MI-BCIs. When labels for MI tasks cannot be quickly annotated, employing the EosEEGsc method enables rapid cluster acquisition, thereby guiding precise control instructions for MI-BCIs output.