Unsupervised EEG Decoding with Frequency-Trend-Based Information Granule Learning

Electroencephalography (EEG)-driven innovations have gained prominence as pivotal research frontiers in various domains, including neurological disorder diagnostics, sensorimotor rehabilitation, and brain-computer interfaces (BCIs), etc . Traditional EEG decoding methods encounter difficulties in effectively extracting global features from EEG signals while demonstrating constrained adaptability to high-dimensional EEG patterns. Furthermore, the scarcity of labeled EEG significantly restricts the applicability of supervised learning paradigms. To address these challenges, we propose IGEEGc , a novel unsupervised decoding method specifically designed for unlabeled EEG signal analysis. First, we extend the trend-based information granule (TIG) model by incorporating frequency-domain features, thereby developing a frequency-trend-based information granule (FTIG) model that simultaneously captures time-frequency features of EEG signals. Subsequently, an adaptive granule feature weighting mechanism is developed to dynamically optimize the contributions of heterogeneous granule features toward enhancing clustering accuracy. Finally, constraints are integrated into the unified one-step spectral clustering framework to optimize the objective function and enhance clustering performance. Comparative experiments across 30 real-world datasets in multiple scenarios demonstrate IGEEGc ’s consistent superiority over state-of-the-art methods, with its robustness and efficacy being validated through comprehensive sensitivity analyses and ablation studies. The proposed method not only addresses the inherent high-dimensional complexity of EEG signals but also establishes an effective paradigm for processing unlabeled EEG data. The source code can be found at https://github.com/Ljyyds/IGEEGc .