Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining feature sets for a first number of diagnostic trials performed with a patient for diagnostic testing, wherein each feature set includes one or more features of electroencephalogram (EEG) signals measured from the patient while the patient is presented with trial content known to stimulate one or more desired human brain systems. Iteratively providing different combinations of the feature sets as input data to a diagnostic machine learning model to obtain model outputs, each model output corresponding to a particular one of the combinations. Determining, based on the model outputs, a consistency metric, the consistency metric indicating whether a quantity of feature sets in the combinations is sufficient to produce accurate output from the diagnostic machine learning model. Selectively ending the diagnostic testing with the patient based on a value of the consistency metric.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.