Real-time analysis of input to machine learning models

A computer-implemented input analysis method for calibrating a diagnostic system, the method executed by one or more processors and comprising:

• obtaining, by the one or more processors, feature sets for a first number of diagnostic trials performed with a patient for diagnostic testing, wherein each feature set comprises 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, by the one or more processors, 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, by the one or more processors and 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;
• and
• selectively ending the diagnostic testing with the patient based on a value of the consistency metric.

A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• obtaining feature sets for a first number of diagnostic trials performed with a patient for diagnostic testing, wherein each feature set comprises 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;
• and
• selectively ending the diagnostic testing with the patient based on a value of the consistency metric.