This invention, which focuses on personality and aptitude matching by psychophysiologic response to stimuli, is referred to as Brain Matching. In general terms, this invention starts by selecting highly specialized skill sets and top performer group for each skill set. The various groups are analyzed though psychophysiologic stimuli testing by using basically the same testing consisting of large numbers stimuli tests in a consistent testing environment. Stimuli tests can range from hundreds to thousands of images each producing a brainwave response. Neural Networks, Artificial Intelligence, Deep Learning computers look at the test results, highly specialized group by other highly specialized group to reduce the groups signature/response commonality into a template. Test subjects are then tested using the same stimuli. The subject's test results are analyzed for correlation with the various specialized expert groups.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: presenting, by sensory presentation device in communication with a first computing device, a first sequence of stimuli to: (i) a first set of subjects associated with a first selection criteria and (ii) a second set of subjects associated with a second selection criteria; detecting, by an electrode in communication with the first computing device, a first set of voltage fluctuation sequences, the first set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the first set of subjects; detecting, by the electrode, a second set of voltage fluctuation sequences, the second set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the second set of subjects; determining, by a neural network, a pattern of voltage fluctuations characteristic of the first set of voltage fluctuation sequences and not characteristic of the second set voltage fluctuation sequences; and providing, by the first computing device, a recommendation for a selection of subjects from among a third set of subjects based on the pattern of voltage fluctuations. 2. The method of claim 1, further comprising: presenting, by the sensory presentation device, the first sequence of stimuli to the third set of subjects; and selecting, by the first computing device; a fourth set of subjects from among the third set of subjects based on the pattern of voltage fluctuations, wherein the provision of the recommendation is based on the fourth set of subjects 3. The method of claim 1, further comprising: presenting, by the sensory presentation device, a set of sequences of stimuli to the first set of subjects; determining, by the neural network, a respective correlation value for each of the set of sequences of stimuli; and selecting, by the first computing device, the first sequence of stimuli from among the set of sequences of stimuli based on the respective correlation value for the first sequence of stimuli. 4. The method of claim 1, further comprising: presenting, by the sensory presentation device, a set of sequences of stimuli to the first set of subjects; determining, by the neural network, a respective correlation value for each of the set of sequences of stimuli; and selecting, by the first computing device, the first sequence of stimuli from among the set of sequences of stimuli based on the respective correlation value for the first sequence of stimuli, wherein the respective correlation value for the first sequence of stimuli comprises a measure of interdependence among components of the first sequence of stimuli and components of a sequence of voltage fluctuations collected from each of the first set of subjects during the presentation of the first sequence of stimuli. 5. The method of claim 1, further comprising: selecting, by the first computing device, the first set of subjects based on the first criteria and the second set of subjects based on the second criteria. 6. The method of claim 1, wherein the sensory presentation device comprises at least one selected from the group consisting of: a video display, a speaker, a scent-generating device, a tactile generator, and a taste simulation device. 7. The method of claim 1, wherein the presentation of the first sequence of stimuli comprises rapid serial visual presentation. 8. The method of claim 1, wherein the first sequence of stimuli is unrelated to the first selection criteria. 9. The method of claim 1, wherein the first selection criteria comprises at least one selected from the group consisting of: a test score exceeding a threshold score, a membership in an organization, a career, membership in a profession, an accreditation, an academic degree, a certification, a professional title, an amount of experience, an age, a gender, a rate of career progression, and a rate of academic progression. 10. The method of claim 1, wherein the second set of subjects are not associated with the first criteria. 11. The method of claim 1, wherein the second criteria comprises being substantially randomly selected from a population of subjects comprising the first set of subjects and the second set of subjects. 12. The method of claim 1, wherein the first set of voltage fluctuation sequences and the second set of voltage fluctuation sequences are collected in accordance with electroencephalographic techniques. 13. The method of claim 1, wherein the electrode is in contact with a scalp of a subject of the first set of subjects. 14. The method of claim 1, wherein the first set of voltage fluctuation sequences comprise evoked potentials and/or event related potentials. 15. The method of claim 1, wherein the neural network comprises at least one selected from the group consisting of: a deep neural network, convolutional neural network, long short-term memory neural network, and a convolutional, long short-term memory, fully connected deep neural network. 16. The method of claim 1, wherein the neural network is executing on one or more second computing devices remote from the first computing device. 17. The method of claim 1, wherein the neural network is executing on the first computing device. 18. The method of claim 1, wherein the pattern of voltage fluctuations characteristic of the first set of voltage fluctuation sequences comprises a pattern of voltage fluctuations more common in the first set of voltage fluctuation sequences than in the second set of voltage fluctuation sequences. 19. A non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising: presenting a first sequence of stimuli to: (i) a first set of subjects associated with a first selection criteria and (ii) a second set of subjects associated with a second selection criteria; detecting a first set of voltage fluctuation sequences, the first set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the first set of subjects; detecting a second set of voltage fluctuation sequences, the second set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the second set of subjects; determining, by a neural network, a pattern of voltage fluctuations characteristic of the first set of voltage fluctuation sequences and not characteristic of the second set voltage fluctuation sequences; and providing a recommendation for a selection of subjects from among a third set of subjects based on the pattern of voltage fluctuations. 20. A system comprising: an electrode; a computing device comprising a processor in communication with the electrode; and a non-transitory, computer-readable medium in communication with the processor and storing instructions that, when executed by the processor, cause the processor to perform operations comprising: presenting a first sequence of stimuli to: (i) a first set of subjects associated with a first selection criteria and (ii) a second set of subjects associated with a second selection criteria; detecting a first set of voltage fluctuation sequences, the first set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the first set of subjects; detecting a second set of voltage fluctuation sequences, the second set of voltage fluctuation sequences comprising a sequence of voltage fluctuations from each of the second set of subjects; determining, by a neural network, a pattern of voltage fluctuations characteristic of the first set of voltage fluctuation sequences and not characteristic of the second set voltage fluctuation sequences; and providing a recommendation for a selection of subjects from among a third set of subjects based on the pattern of voltage fluctuations.