A new study by Apple researchers presents a method that lets an AI model learn one aspect of the structure of brain electrical activity without any annotated data. Here’s how.
PAirwise Relative Shift
In a new study called “Learning the relative composition of EEG signals using pairwise relative shift pretraining”, Apple introduces PARS, which is short for PAirwise Relative Shift.
Current models rely heavily on human-annotated data for brain activity, indicating which segments correspond to Wake, REM, Non-REM1, Non-REM2, and Non-REM3 sleep stages, as well as the start and end locations of seizure events, and so on.
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