Methods for modeling neurological development and diagnosing a neurological impairment of a patient

1. A method for determining a predicted neurological development outcome of a first user, the method comprising:

for each user of a set of users in a user base:

at each time point of a first set of discrete time points, at a neuroheadset of the user, recording a set of EEG signals;

for each EEG signal of the set of EEG signals, determining a synchronization pattern based on the EEG signal; and

determining a synchronization pattern trend based on a set of the synchronization patterns;

for the set of users:

organizing a set of the synchronization pattern trends into a set of groups;

assigning a tag to each group of the set of groups, the tag associated with a neurological development outcome; and

determining a neurological development model for each group of the set of groups, wherein each neurological development model is configured to determine a predicted neurological development outcome;

for the first user:

recording a second set of EEG signals at a second set of discrete time points, the second set of discrete points occurring after the first set of discrete time points, wherein the second set of EEG signals is recorded at a neuroheadset of the first user;

determining a first user synchronization pattern trend based on the second set of EEG signals;

comparing the first user synchronization pattern trend with a set of the neurological development models;

determining a set of correlations based on comparing the first user synchronization pattern trend with the set of neurological development models; and

determining the predicted neurological development outcome based on the set of correlations;

providing a notification to the first user at an application executing on a user device associated with the first user;

receiving an input from the first user at the application; and

adding the first user synchronization pattern trend to a first group of the set of groups based on the input.

2. The method of claim 1 , wherein the input comprises a neurological status of the first user.

3. The method of claim 2 , further comprising updating a neurological development model associated with the first group based on the first user synchronization pattern trend.

4. The method of claim 1 , wherein the first set of discrete time points are taken on multiple distinct dates.

5. The method of claim 4 , wherein the first set of discrete time points spans a duration of at least a month.

6. The method of claim 1 , wherein the first set of discrete time points spans a first duration of time and the second set of discrete time points spans a second duration of time, wherein the second duration of time is less than the first duration of time.

7. The method of claim 1 , wherein the user base comprises at least 1000 users.

8. The method of claim 1 , further comprising recommending an activity for the first user to perform at the application based on the predicted neurological development outcome.

9. The method of claim 1 , wherein determining the synchronization pattern trend for each user of the set of users comprises: filtering a first EEG signal of the set of EEG signals of the user in a first band and in a second band to generate a first band-filtered EEG signal and a second band-filtered EEG signal, respectively; and extracting a phase locking episode based on the first band-filtered EEG signal and the second band-filtered EEG signal.

10. The method of claim 1 , wherein determining the synchronization pattern trend for each user of the set of users comprises: generating a filtered first EEG signal and a filtered second EEG signal upon filtering each of a first and a second EEG signal of the set of EEG signals of the user; analyzing the filtered first and second EEG signals via spectral analysis; identifying stable phase difference episodes between the filtered first and second EEG signals via statistical identification of phase-locking synchrony, and comparing stable phase difference episodes of the first user and a second user, the first and second EEG signals recorded at distinct time points.

11. The method of claim 1 , wherein determining the synchronization pattern trend for each user of the set of users comprises analyzing the set of EEG signals through multiscale entropy analysis and identifying changes in synchronization entropy over time for the user.

12. A method for determining a predicted neurological development outcome of a first user, the method comprising:

for each user of a set of users in a user base:

at each time point of a first set of discrete time points, at a neuroheadset of the user, recording a set of EEG signals;

for each EEG signal of the set of EEG signals, determining a synchronization pattern based on the EEG signal; and

determining a synchronization pattern trend based on a set of the synchronization patterns;

for the set of users:

organizing a set of the synchronization pattern trends into a set of groups;

assigning a tag to each group of the set of groups, the tag associated with a neurological development outcome; and

determining a neurological development model for each group of the set of groups, wherein each neurological development model is configured to determine a predicted neurological development outcome;

for the first user:

recording a second set of EEG signals at a second set of discrete time points, the second set of discrete points occurring after the first set of discrete time points, wherein the second set of EEG signals is recorded at a neuroheadset of the first user;

determining a first user synchronization pattern trend based on the second set of EEG signals;

comparing the first user synchronization pattern trend with a set of the neurological development models;

determining a set of correlations based on comparing the first user synchronization pattern trend with the set of neurological development models; and

determining the predicted neurological development outcome based on the set of correlations.

13. The method of claim 12 , further comprising, for the first user:

recording a third set of EEG signals at a third set of discrete time points, the third set of discrete points occurring after the second set of discrete time points, wherein the third set of EEG signals is recorded at a neuroheadset of the first user;

updating the first user synchronization pattern trend based on the third set of EEG signals;

comparing the updated first user synchronization pattern trend with the set of neurological development models;

determining a second set of correlations based on comparing the updated first user synchronization pattern trend with the set of neurological development models;

updating the predicted neurological development based on the second set of correlations.

14. The method of claim 12 , wherein the first set of discrete time points are taken on multiple distinct dates.

15. The method of claim 14 , wherein the first set of discrete time points spans a duration of at least a month.

16. The method of claim 15 , wherein the first set of discrete time points spans a duration of at least a year.

17. The method of claim 12 , wherein the first set of discrete time points spans a first duration of time and the second set of discrete time points spans a second duration of time, wherein the second duration of time is less than the first duration of time.

18. The method of claim 12 , wherein the user base comprises at least 1000 users.

19. The method of claim 12 , further comprising recommending an activity for the first user to perform based on the predicted neurological development outcome.

20. The method of claim 12 , wherein determining the synchronization pattern trend for each user of the set of users comprises: filtering a first EEG signal of the set of EEG signals of the user in a first band and in a second band to generate a first band-filtered EEG signal and a second band-filtered EEG signal, respectively; and extracting a phase locking episode based on the first band-filtered EEG signal and the second band-filtered EEG signal.

21. The method of claim 12 , wherein the neurological development model is further determined based on a set of known neurological statuses associated with the set of users in the user base.