Inducement, verification and optimization of neural entrainment through biofeedback, data analysis and combinations of adaptable stimulus delivery

1. A neural entrainment platform configured to induce and verify neural entrainment of a user, the platform comprising:

a neural entrainment system, wherein the neural entrainment system further comprises:

one or more neural stimuli modules, wherein each neural stimuli module of the one or more neural stimuli modules further comprises one or more stimuli devices and a stimuli control module, and wherein each neural stimuli module of the one or more neural stimuli modules is configured to:

set, by the stimuli control module, one or more operational parameters for the one or more stimuli devices;

control, by the stimuli control module, an operation of the one or more stimuli devices, wherein the operation of the one or more stimuli devices is based at least in part on the one or more operational parameters for the one or more stimuli devices; and

deliver, by the one or more stimuli devices, one or more neural stimuli to the user; and

a verification module, wherein the verification module further comprises:

one or more sensor modules, wherein, at least one of the one or more sensor modules is configured to receive one or more biological signals from the user; and

a data analysis module configured to:

determine, by a cognitive assessment module, one or more efficacy values value of a combination of the one or more neural stimuli, wherein the one or more efficacy values are based at least in part on received user input and results from one or more clinical assessments;

analyze, by a machine learning model generated by a machine learning module, data received from the one or more sensor modules, wherein the data received includes the one or more biological signals, the one or more operational parameters of the one or more stimuli devices and the one or more determined efficacy values, and wherein the machine learning model is trained on one or more training datasets that are retrieved from one or more databases, wherein each training dataset of the one or more training datasets comprises a plurality of training records each comprising one or more historic stimuli profiles of historic neural stimuli, one or more historic biological signals received in response to the historic neural stimuli, historic received user input, historic results from the one or more clinical assessments and one or more historic determined efficacy values;

determine, based on the analysis, one or more adjustments to the one or more operational parameters of the one or more stimuli devices; and

provide the determined one or more adjustments to the stimuli control module, wherein the stimuli control module applies the one or more adjustments to the one or more stimuli devices.

2. The neural entrainment platform of claim 1 , wherein the verification module further comprises a camera module configured to:

capture images of the user when said user wears the neural entrainment system; and

determine, by a configuration module, proper placement of the neural entrainment system, wherein the proper placement determination is based at least in part on the captured images of the user and the analysis of the one or more biological signals received by the one or more sensor modules.

3. The neural entrainment platform of claim 2 , wherein the neural entrainment system is configured to provide the user with adjustment directions to reposition the neural entrainment system to the proper placement.

4. The neural entrainment platform of claim 1 , wherein one of the one or more sensor modules is an electroencephalogram (EEG).

5. The neural entrainment platform of claim 1 , wherein each of the one or more stimuli devices are configured to deliver a neural stimuli selected from:

visible light, audible sound, infrared light, ultraviolet light, infrasound, ultrasound, physical vibrations, transcranial direct and alternating current stimulation (tDCS/tACS), transcranial magnetic stimulation (TMS) or aromas.

6. The neural entrainment platform of claim 5 , wherein each of the one or more stimuli devices are controlled to deliver neural stimuli based on a stimuli profile, wherein the stimuli profile comprises an operational parameter for amplitude, duration, frequency and wave shape.

7. The neural entrainment platform of claim 6 , wherein adjustments are made to the stimuli profile of one or more of the one or more stimuli devices when the determined efficacy value is not within a first window of values.

8. The neural entrainment platform of claim 7 , wherein the adjustments are based at least in part on a relationship between a subset of the one or more neural stimuli and the one or more biological signals received in response to each neural stimuli of the subset.

9. The neural entrainment platform of claim 6 , wherein the data analysis module is further configured to generate one or more predicted efficacy values and one or more predicted biological signals based at least in part on an analysis of the determined one or more adjustments to the one or more operational parameters of the one or more stimuli devices.

10. The neural entrainment platform of claim 9 , wherein one or more new stimuli profiles are generated based at least in part on the one or more predicted efficacy values and the one or more predicted biological signals.

11. A computer implemented method for inducing and verifying neural entrainment of a user, the method comprising:

controlling, by a stimuli control module, one or more stimuli devices, wherein the controlling comprises:

setting one or more operational parameters for the one or more stimuli devices;

delivering, by a first stimuli device of the one or more stimuli devices, a first neural stimuli to the user;

delivering, by a second stimuli device of the one or more stimuli devices, a second neural stimuli to the user;

measuring, by one or more sensors, one or more biological signals from the user;

analyzing, by a machine learning model generated by a machine learning module operating on a data analysis module, the one or more biological signals;

adjusting, based on the analysis of the one or more biological signals, the one or more operational parameters for the first stimuli device and the second stimuli device;

administering one or more clinical assessments to the user;

receiving, from the user, answers to the one or more clinical assessments;

generating a result for each of the one or more clinical assessments based on the received answers;

assessing, by a cognitive assessment module, an assessed efficacy value of a combination of the first neural stimuli and the second neural stimuli, wherein the assessed efficacy value is based on the generated results for the one or more clinical assessments; and

training, by the machine learning module, the machine learning model on one or more training datasets that are retrieved from one or more databases, wherein each training dataset of the one or more training datasets comprises a plurality of training records each comprising one or more historic stimuli profiles of historic neural stimuli, one or more historic biological signals received in response to the historic neural stimuli, historic received user input, historic results from the one or more clinical assessments and one or more historic determined efficacy values.

12. The method of claim 11 , wherein one of the one or more sensors is an electroencephalogram (EEG).

13. The method of claim 12 , wherein the first stimuli device and the second stimuli device are configured to deliver a neural stimuli selected from:

visible light, audible sound, infrared light, ultraviolet light, infrasound, ultrasound, physical vibrations, transcranial direct and alternating current stimulation (tDCS/tACS), transcranial magnetic stimulation (TMS) or aromas.

14. The method of claim 13 , wherein the first stimuli device and the second stimuli device are controlled to deliver neural stimuli based on a stimuli profile, wherein the stimuli profile comprises an operational parameter for amplitude, duration, frequency and wave shape.

15. The method of claim 14 , wherein adjustments are made to the stimuli profile of the first stimuli device and the second stimuli device based at least in part on the assessed efficacy value and a correspondence between the assessed efficacy value and a first window of values.

16. The method of claim 15 , wherein the adjustments are based at least in part on a relationship between the first neural stimuli, the second neural stimuli and biological signals received in response to the first neural stimuli and the second neural stimuli.