Brain-based thought identifier and classifier

A system, comprising:

• a processor;
• a computer readable memory in circuit communication with the processor;
• and
• a computer readable storage medium in circuit communication with the processor;
• wherein the processor executes program instructions stored on the computer-readable storage medium via the computer readable memory and thereby:
• converts analog electroencephalogram signals to a digital signal bitstream data, wherein the analog electroencephalogram signals are obtained from mental activity of a person;
• identifies a portion of the digital signal bitstream data as a thought chunk that represents discrete thought activity in response to matching, via a first artificial neural network comparison, first metadata of the thought chunk portion of the digital signal bitstream to metadata that is labeled in association with a thought within a thoughts data set, wherein the first artificial neural network is trained on the thoughts data set;
• identifies a user category to which the person generating the mental activity belongs in response to matching, via a second artificial neural network comparison, the first metadata and length and generation time metadata of the thought chunk portion to metadata that is labeled in association with a user category within the thoughts data set, wherein the second artificial neural network is trained on the thoughts data set and is different from the first artificial neural network;
• and
• identifies a specific thought within the thoughts data that has metadata that corresponds to the first metadata, the length and generation time metadata of the thought chunk portion, and to metadata that is labeled in association with the identified user category within the thoughts data set.

A computer program product for a thought classifier, the computer program product comprising:

• a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code comprising instructions for execution by a processor that cause the processor to:
• convert analog electroencephalogram signals to a digital signal bitstream data, wherein the analog electroencephalogram signals are obtained from mental activity of a person;
• identify a portion of the digital signal bitstream data as a thought chunk that represents discrete thought activity in response to matching, via a first artificial neural network comparison, first metadata of the thought chunk portion of the digital signal bitstream to metadata that is labeled in association with a thought within a thoughts data set, wherein the first artificial neural network is trained on the thoughts data set;
• identify a user category to which the person generating the mental activity belongs in response to matching, via a second artificial neural network comparison, the first metadata and length and generation time metadata of the thought chunk portion to metadata that is labeled in association with a user category within the thoughts data set, wherein the second artificial neural network is trained on the thoughts data set and is different from the first artificial neural network;
• and
• identify a specific thought within the thoughts data that has metadata that corresponds to the first metadata, the length and generation time metadata of the thought chunk portion, and to metadata that is labeled in association with the identified user category within the thoughts data set.