Resources

Data and code from Kong et al. (Epilepsia 2025): 1,542 expert and non-expert participants annotated 478,834 EEG epochs for seizures and rhythmic/periodic patterns; mixed-effects analyses compare crowd vs expert accuracy.

Data and code to reproduce this paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC9847346/

TEEGLLTEEG

Cyclops dataset

This dataset supports a published prospective cohort study using machine learning to develop the Visual EEG Confusion Assessment Method Severity (VE-CAM-S) scale for quantifying delirium and coma severity.

Data and code to automatically detect periods of suppression in critically ill ICU patients.

De-identified data and a PK-PD + MALTS causal-inference pipeline reproducing the effect of untreated epileptiform-activity burden on poor discharge outcome in critically ill patients (Parikh et al., Lancet Digital Health 2023).

MATLAB code and de-identified data (1,991 patients) relating automatically-quantified ictal-interictal-continuum/seizure burden on continuous EEG to discharge outcome (Zafar et al., Ann Neurol 2021).

MATLAB/Python code and de-identified data for a simulation framework that estimates required sample sizes and effect sizes for randomized trials of anti-seizure treatment in critically ill patients (Parikh et al., Ann Clin Transl Neurol 2024).

De-identified sleep-spindle feature data (BDSP-linked) + cognition data + Python code to find the spindle-detection parameters that best predict cognitive performance (Adra et al., Sleep 2022).