Resources

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).

De-identified 1,000-patient multicenter dataset (BDSP-linked) and Python code for the prognostic significance of continuous-EEG patterns after cardiac arrest, with exact Clopper-Pearson CIs and temporal-trend analysis.

A de-identified teaching atlas of 224 labeled EEG segments, each pairing raw EEG with its qEEG spectrogram, plus MATLAB code to regenerate the paired figures. Accompanies "A Primer on EEG Spectrograms" (Ng et al., 2022).

De-identified data and reproduction code for a retrospective cEEG study of ictal-interictal continuum patterns and outcomes in 121 patients with toxic-metabolic encephalopathy (MGH, 2012-2017).

MATLAB code and de-identified data for six interpretable quantitative EEG features (amplitude, duration, sharpness, slow wave, asymmetry, field) combined to classify interictal epileptiform discharges (AUC ~0.83).

MATLAB code and de-identified data for the DDESVSFS questionnaire-based screening tool distinguishing epileptic from functional (psychogenic) seizures (208 patients; AUC 0.93).

MATLAB code and de-identified CCEMRC data behind the 2HELPS2B seizure-risk score: seizure probability by ictal-interictal-continuum EEG pattern (5,742 continuous-EEG records).

LASSO models predicting outcome, mortality, and disposition after cardiac arrest, showing EEG reactivity improves multimodal prognostication (373-patient cohort). MATLAB code + de-identified data.

R (mstate) and MATLAB code with de-identified data for a 3-state multistate survival model of time-dependent seizure risk on continuous EEG in critically ill patients (665 consecutive cEEGs).

MATLAB code and de-identified data to forecast the onset and day-by-day trajectory of ICANS after CAR T-cell therapy (combined hidden Markov model + lasso logistic regression; 199-patient cohort, leave-one-patient-out cross-validation).