Determining mental states based on biometric data

A system comprising:

• one or more processors;
• and
• a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to:
• receive one or more signal files of data based on voltages detected by one or more electrodes on a set of headphones worn by a user, wherein at least one of the electrodes comprises a first electrode for detecting both an EEG signal and an EMG signal;
• extract features from the received data and feeding the extracted features into one or more machine learning models to generate a determined output that corresponds to at least one of a current mental state of the user and a type of one or more facial gestures performed by the user;
• and
• send the determined output to a computing device to perform an action based on the determined output;
• wherein extract features comprises:
• extract one or more EEG features from the received data based at least on EEG signals detected by the first electrode;
• feed at least one of the extracted EEG features into at least one of a first machine learning model for determining a current level of focus of the user, a second machine learning model for determining a current level of attentiveness of the user and a third machine learning model for determining a current level of cognitive load of the user;
• extract EMG features from the received data based at least on EMG signals detected by the first electrode;
• and
• feed one or more of the extracted EMG features into a facial gesture machine learning model for determining a type of facial gesture from a plurality of types of facial gestures.

A computer program product comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:

• receive one or more signal files of data based on voltages detected by one or more electrodes on a set of headphones worn by a user, wherein at least one of the electrodes comprises a first electrode for detecting both an EEG signal and an EMG signal;
• extract features from the received data and feeding the extracted features into one or more machine learning models to generate a determined output that corresponds to at least one of a current mental state of the user and a type of one or more facial gestures performed by the user;
• and
• send the determined output to a computing device to perform an action based on the determined output;
• wherein extract features comprises:
• extract one or more EEG features from the received data based at least on EEG signals detected by the first electrode;
• feed at least one of the extracted EEG features into at least one of a first machine learning model for determining a current level of focus of the user, a second machine learning model for determining a current level of attentiveness of the user and a third machine learning model for determining a current level of cognitive load of the user;
• extract EMG features from the received data based at least on EMG signals detected by the first electrode;
• and
• feed one or more of the extracted EMG features into a facial gesture machine learning model for determining a type of facial gesture from a plurality of types of facial gestures.