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

Abstract

One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

1 . A method for modeling neurological development comprising:
 aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses;   identifying a synchronization pattern trend within the EEG data of the set of users; and   correlating the synchronization pattern trend with neurological development within the set of users.   
     
     
         2 . The method of  claim 1 , wherein identifying the synchronization pattern trend comprises filtering each of a first and a second EEG signal of a user in a first band and in a second band, filtering an amplitude time series of the second band in the first band, computing phase locking between the first-band filtered signal and the first-band filtered second amplitude signal, and comparing phase locking of the first and second EEG signals, the first and second EEG signals recorded on distinct dates. 
     
     
         3 . The method of  claim 1 , wherein identifying the synchronization pattern trend comprises filtering each of a first and a second EEG signal of a user, analyzing the filtered EEG signals via spectral analysis, identifying stable phase difference episodes between the first and second EEG signals via statistical identification of phase-locking synchrony, and comparing stable phase difference episodes of the user and a second user, the first and second EEG signals recorded on distinct dates. 
     
     
         4 . The method of  claim 1 , wherein identifying a synchronization pattern trend comprises analyzing the EEG data through multiscale entropy analysis and identifying changes in synchronization entropy over time for a user. 
     
     
         5 . The method of  claim 1 , further comprising directing a user to record an EEG signal while performing a specified action, and tagging the EEG signal with the specified action. 
     
     
         6 . The method of  claim 1 , further comprising collecting an output of a physiological or environmental sensor proximal a user during recordation of an EEG signal, determining an action of the user based upon the output of the physiological or environmental sensor, and tagging the EEG signal with the determined action. 
     
     
         7 . The method of  claim 1 , wherein aggregating the EEG data comprises tagging each EEG signal with a neurological status of a respective user. 
     
     
         8 . The method of  claim 1 , wherein correlating the synchronization pattern trend with the neurological development comprises isolating synchronization pattern similarities for users with similar neurological statuses and isolating synchronization pattern differences for users with different neurological statuses. 
     
     
         9 . The method of  claim 1 , wherein aggregating the EEG data comprises aggregating EEG signals that each comprise a plurality of EEG subsignals recorded by one or more electrodes of a neuroheadset worn by a user. 
     
     
         10 . The method of  claim 1 , further comprising mapping a first neural connectivity of a user based upon a first EEG signal of the user recorded on a first date, wherein the first EEG signal comprises a plurality of EEG subsignals, each EEG subsignal associated with neuronal electrical activity of the user sensed by one or more electrodes of known arrangement on a neuroheadset worn by the user. 
     
     
         11 . The method of  claim 10 , wherein identifying the synchronization pattern trend comprises identifying changes in neural connectivity of the user by comparing the first neural connectivity associated with the first date with a second neural connectivity of the user based upon EEG subsignals of a second EEG signal recorded on a second date. 
     
     
         12 . The method of  claim 1 , wherein correlating the synchronization pattern trend with neurological development comprises generating a neurological impairment model by comparing synchronization pattern trends of user diagnosed with and not diagnosed with the neurological impairment. 
     
     
         13 . The method of  claim 1 , wherein aggregating EEG data is performed over a distributed network, and wherein identifying the synchronization pattern trend in the EEG data and correlating the synchronization pattern trend with neurological development are performed by a computer system. 
     
     
         14 . A method for diagnosing a neurological impairment of a patient, comprising:
 aggregating a plurality of electroencephalography (EEG) signals of the patient, each EEG signal recorded on a distinct date;   identifying a synchronization pattern trend in the EEG signals of the patient;   comparing the synchronization pattern trend of the patient with a neurological impairment model comprising a correlated neurological impairment; and   diagnosing the patient with the neurological impairment based upon the comparison.   
     
     
         15 . The method of  claim 14 , wherein identifying the synchronization pattern trend comprises filtering an EEG signal of the patient in a first band and in a second band, filtering an amplitude time series of the second band in the first band, and computing phase locking between the first-band filtered signal and the first-band filtered second amplitude signal. 
     
     
         16 . The method of  claim 14 , wherein diagnosing the patient comprises diagnosing the patient with the neurological impairment that is one of autism, epilepsy, Schizophrenia, and Alzheimer's disease. 
     
     
         17 . The method of  claim 14 , wherein comparing the patient synchronization pattern trend comprises comparing the patient synchronization pattern trend with the neurological impairment model extracted from EEG data that comprise a plurality of EEG signals of each of a plurality of users with EEG signals from each user recorded on distinct dates. 
     
     
         18 . The method of  claim 14 , further comprising mapping a first neural connectivity of the patient based upon a first EEG signal of the patient recorded on a first date, wherein the first EEG signal comprises a plurality of EEG subsignals, each EEG subsignal associated with neuronal electrical activity of the patient sensed by one or more electrodes of known arrangement on a neuroheadset worn by the patient. 
     
     
         19 . The method of  claim 18 , further comprising identifying a change in neural connectivity of the patient by comparing the first neural connectivity on the first date with a second neural connectivity of the patient based upon EEG subsignals of a second EEG signal recorded on a second date. 
     
     
         20 . The method of  claim 19 , wherein diagnosing the patient further comprises diagnosing the patient based upon the identified change in neural connectivity between the first date and the second date. 
     
     
         21 . The method of  claim 19 , wherein identifying the change in neural connectivity of the patient comprises tracking neural development trends of the patient based upon an identified change in patient neural connectivity. 
     
     
         22 . The method of  claim 18 , wherein mapping the first neural connectivity of the patient comprises identifying an abnormality of neural connectivity of the patient, wherein diagnosing the patient is further based upon a comparison of the neural connectivity abnormality of the patient with a neural connectivity model extracted from EEG data of a plurality of users of known neurological status. 
     
     
         23 . The method of  claim 14 , further comprising collecting an output of a physiological or environmental sensor proximal a user during recordation of an EEG signal, determining an action of the user based upon the output of the physiological or environmental sensor, and tagging the EEG signal with the determined action. 
     
     
         24 . The method of  claim 14 , wherein identifying the synchronization pattern trend, comparing the synchronization pattern trend with the neurological impairment model and diagnosing the patient with the neurological impairment are performed by a computer system. 
     
     
         25 . A method for tagging electroencephalography (EEG) signals, comprising:
 collecting an EEG signal of a user;   collecting an output of a physiological or environmental sensor proximal the user during recordation of the EEG signal;   determining an action of the user based upon the output of the physiological or environmental sensor; and   tagging the EEG signal with the determined action.   
     
     
         26 . The method of  claim 25 , wherein collecting the output of the physiological or environmental sensor, determining the action of the user, and tagging the EEG signal with the determined action are performed by a computer system.