MLSPred-Bench will create 12 different benchmarks based on different values of the seizure prediction horizon (SPH) and the seizure occurrence period (SOP). The benchmarks are for patient-independent epileptic seizure prediction using only raw electroencephalography (EEG) data and are machine learning (ML)-ready.
For each benchmark, MLSPred-Bench draws preictal segments of length from the SPH duration. We assume there is a gap equal to the SOP in minutes before the start of a seizure where the SPH ends. The datasets are class-balanced where an equal amount of interictal samples are drawn from sessions of the same subject where there were no seizures.
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Data collection and curation for this study is complete.