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. A physician's report with diagnoses, event markers and confidence levels can be generated based on output from the statistical model. Systems and methods for dealing with channel variation or a missing EEG electrode valve are also disclosed. A feature-space boosted maximum mutual information training of discriminative features or an iVectors technique to determine invariant feature components can be implemented for generating a plurality of EEG event labels. An optional GUI allows scrolling by EEG events.
System and Method for Automatic Interpretation of EEG Signals Using a Deep Learning Statistical Model