Why the Kaggle 2016 Seizure Prediction Competition was unrealistically difficult

One of the most difficult parts of living with epilepsy is that seizures are often unpredictable and occur without warning. For decades, the epilepsy research community has dreamed of developing technology that could warn individuals that a seizure was likely so that they could take action to prevent the seizure or mitigate its effects. A … More Why the Kaggle 2016 Seizure Prediction Competition was unrealistically difficult

Linear spatial filters (e.g., ICA, PCA) cannot perfectly extract EEG/MEG sources

Linear spatial filters (LSFs) derived from techniques like independent component analysis (ICA) or principal components analysis (PCA) are extremely popular techniques for analyzing electroencephalographic (EEG) and magnetoencephalographic data. They work by exploiting the fact that different EEG/MEG signal sources (e.g., a cortical patch or the eyes) project across the scalp with different topographies. An LSF … More Linear spatial filters (e.g., ICA, PCA) cannot perfectly extract EEG/MEG sources

Public clinical data science data sets

Looking for a clinical data science project? Here are some free data sets that might be of use (in no particular order): Human Physiological and Neuroimaging Data Cam-CAN (Cambridge Centre for Ageing Neuroscience) dataset: fMRI, MRI, MEG, and behavioral human neurodata on people of various ages (i.e., cross-sectional data) Physionet: A wide array of clinically relevant … More Public clinical data science data sets

Four things you might not (but should know) about false discovery rate control

Massive increases in the amount of data scientists are able to acquire and analyze over the past two decades have driven the development of new statistical tools that can better deal with the challenges of “big data.” One such set of tools is ways of controlling the “false discovery rate” (FDR) in a set of … More Four things you might not (but should know) about false discovery rate control