Electroencephalogram system with machine learning filtering of redundant signals

An electroencephalogram (EEG) sensor assembly, comprising:

• a plurality of electrodes;
• a platform supporting the plurality of electrodes;
• a sensor processing module supported by the platform, the sensor processing module comprising a processor programmed to:
• evaluate each of a plurality of signals each measured using a corresponding one of the plurality of electrodes using a machine learning algorithm in real-time to determine a quality of each of the plurality of signals;
• select one or more of the signals based on their quality in real-time to provide one or more selected signals;
• and
• output, in real-time, an EEG signal corresponding to the electrical activity based on the selected signals.

One or more non-transitory computer-readable storage mediums comprising instructions stored thereon that are executable by a processing device and upon such execution cause the processing device to perform operations comprising:

• simultaneously measuring a plurality of signals at each of a plurality of electrodes applied to a subject's scalp, the plurality of signals corresponding to electrical activity generated by the subject's brain;
• evaluating each of the plurality of signals using a machine learning algorithm in real-time to determine a quality of each of the plurality of signals;
• selecting one or more of the signals based on their quality in real-time to provide one or more selected signals;
• and
• outputting, in real-time, an EEG signal corresponding to the electrical activity based on the selected signals.