# Self-Supervised Distillation of Legacy Rule-Based Methods for Enhanced EEG-Based Decision-Making Source: [Self-Supervised Distillation of Legacy Rule-Based Methods for Enhanced EEG-Based Decision-Making]() Collection index: [llms.txt]() Data updated: 2026-01-02T10:45:24.663Z Authors: Yipeng Zhang, Yuanyi Ding, Chenda Duan, Atsuro Daida, Hiroki Nariai, Vwani Roychowdhury Published: 2025-07-19T09:01:13.000Z ## Abstract High-frequency oscillations (HFOs) in intracranial Electroencephalography (iEEG) are critical biomarkers for localizing the epileptogenic zone in epilepsy treatment. However, traditional rule-based detectors for HFOs suffer from unsatisfactory precision, producing false positives that require time-consuming manual review. Supervised machine learning approaches have been used to classify the detection results, yet they typically depend on labeled datasets, which are difficult to acquire due to the need for specialized expertise. Moreover, accurate labeling of HFOs is challenging due to low inter-rater reliability and inconsistent annotation practices across institutions. The lack of a clear consensus on what constitutes a pathological HFO further challenges supervised refinement approaches. To address this, we leverage the insight that legacy detectors reliably capture clinically relevant signals despite their relatively high false positive rates. We thus propose the Self-Supervised to Label Discovery (SS2LD) framework to refine the large set of candidate events generated by legacy detectors into a precise set of pathological HFOs. SS2LD employs a variational autoencoder (VAE) for morphological pre-training to learn meaningful latent representation of the detected events. These representations are clustered to derive weak supervision for pathological events. A classifier then uses this supervision to refine detection boundaries, trained on real and VAE-augmented data. Evaluated on large multi-institutional interictal iEEG datasets, SS2LD outperforms state-of-the-art methods. SS2LD offers a scalable, label-efficient, and clinically effective strategy to identify pathological HFOs using legacy detectors. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Classification Specificity on Open iEEG](): Classification Specificity; Open iEEG; Specificity: 74.9. - [Classification Specificity on Zurich iEEG](): Classification Specificity; Zurich iEEG; Specificity: 90.9. - [Surgical Outcome Prediction on Open iEEG (test)](): Surgical Outcome Prediction; Open iEEG (test); Accuracy: 61.2. - [Surgical Outcome Prediction on Zurich iEEG](): Surgical Outcome Prediction; Zurich iEEG; ACC: 64. ## Original sources and implementation [Original paper]() [PDF]()