SYSTEM AND METHOD FOR AUTOMATIC INTERPRETATION OF EEG SIGNALS USING A DEEP LEARNING STATISTICAL MODEL

摘要

A system and method for automatically interpreting EEG signals is described. In certain aspects, the system and method use a statistical model trained to automatically interpret EEGs using a three-level decision-making process in which event labels are converted into epoch labels. In the first level, the signal is converted to EEG events using a hidden Markov model based system that models the temporal evolution of the signal. In the second level, three stacked denoising autoencoders (SDAs) are implemented with different window sizes to map event labels onto a single composite epoch label vector. In the third level, a probabilistic grammar is applied that combines left and right context with the current label vector to produce a final decision for an epoch.