Method and system for collecting and processing bioelectrical signals

Abstract

A method and system for detecting bioelectrical signals from a user, including establishing bioelectrical contact between a user and one or more sensors of a biomonitoring neuroheadset; collecting one or more reference signal datasets; collecting, at the one or more sensors, one or more bioelectrical signal datasets referenced to a combined reference signal dataset; and extracting one or more bioparameters from the one or more bioelectrical signal datasets.

We claim: 
     
         1 . A method for detecting bioelectrical signals from a user, comprising:
 establishing bioelectrical contact between a first subregion of a region of the user and a first electroencephalogram (EEG) sensor of a biomonitoring neuroheadset;   establishing bioelectrical contact between a second subregion of the region of the user and a first reference sensor of a noise reduction subsystem of the biomonitoring neuroheadset;   establishing bioelectrical contact between a first auxiliary subregion of an auxiliary region of the user and a second EEG sensor of the biomonitoring neuroheadset;   establishing bioelectrical contact between a second auxiliary subregion of the auxiliary region of the user and a second reference sensor of the noise reduction subsystem the biomonitoring headset;   collecting, at the first reference sensor, a first reference signal dataset during a first time period;   collecting, at the second reference sensor, a second reference signal dataset contemporaneously with the first time period;   generating an averaged reference signal dataset from the first reference signal dataset and the second reference signal dataset;   collecting, at the first EEG sensor, a first EEG signal dataset from the user referenced to the averaged reference signal dataset, contemporaneously with the first time period;   collecting, at the second EEG sensor, a second EEG signal dataset from the user referenced to the averaged reference signal dataset, contemporaneously with the first time period;   generating an aggregated EEG dataset based on the first EEG signal dataset and the second EEG signal dataset; and   extracting a bioparameter from the aggregated EEG dataset.   
     
     
         2 . The method of  claim 1 , wherein generating the aggregated EEG dataset comprises:
 producing a first noise-reduced EEG dataset from the first EEG signal dataset and the first reference signal dataset,   producing a second noise-reduced EEG dataset from the second EEG signal dataset and the second reference signal dataset, and   generating the aggregated EEG dataset from a combination of the first noise-reduced EEG dataset and the second noise-reduced EEG dataset.   
     
     
         3 . The method of  claim 1 , wherein extracting the bioparameter from the aggregated EEG dataset comprises:
 identifying a time-varying oscillation in values of the aggregated EEG dataset;   estimating at least one of a heart rate and a heart rate variability based on the time-varying oscillation in values, wherein the at least one of the heart rate and the heart rate variability corresponds to the first time period.   
     
     
         4 . The method of  claim 3 , wherein identifying the time-varying oscillation in values comprises identifying a set of QRS complex sequences in the values of the aggregated EEG dataset, and wherein estimating the at least one of the heart rate and the heart rate variability is based on the set of QRS complex sequences. 
     
     
         5 . The method of  claim 3 , further comprising generating a cognitive state metric for the user based on the aggregated EEG dataset, and the at least one of the heart rate and the heart rate variability, wherein the cognitive state metric indicates a cognitive state of the user during the first time period. 
     
     
         6 . The method of  claim 1 , wherein the first subregion of the region comprises an ear canal of the user, wherein the first subregion of the auxiliary region comprises a contralateral ear canal of the user, wherein the second subregion of the region is proximal a mastoid process of a temporal bone of the user, and wherein the second subregion of the auxiliary region is proximal a contralateral mastoid process of a contralateral temporal bone of the user. 
     
     
         7 . The method of  claim 1 , wherein generating the aggregated EEG dataset comprises generating a driven right leg signal using a driven right leg module of the noise reduction subsystem, and wherein generating at least one of the first noise-reduced EEG dataset and the second noise-reduced EEG dataset comprises generating the at least one of the first noise-reduced EEG dataset and the second noise-reduced EEG dataset using the driven right leg signal. 
     
     
         8 . The method of  claim 7 , wherein the driven right leg module is characterized by a first feedback reference location at a third subregion of the region, the third subregion proximal the first and the second subregions of the region, the driven right leg module further characterized by a second feedback reference location at a third auxiliary subregion of the auxiliary region, the third auxiliary subregion proximal the first and second auxiliary subregions of the auxiliary region. 
     
     
         9 . The method of  claim 1 , wherein the bioparameter is further extracted from a set of supplemental data, measured by supplemental sensors contemporaneously with the first time period. 
     
     
         10 . The method of  claim 1 , further comprising:
 transmitting a combined dataset to a computing device of the user, the combined dataset comprising the extracted bioparameter and aggregated EEG dataset;   generating, at a software component executing on the computing device, an analysis of the combined dataset;   receiving, at the biomonitoring neuroheadset, operation instructions transmitted by the computing device and generated based on the analysis of the combined dataset; and   operating a third device based on the operation instructions.   
     
     
         11 . The method of  claim 10 , further comprising:
 receiving, at a remote server, an aggregated EEG dataset portion and a bioparametric portion of the combined dataset;   generating a cognitive state metric based on the aggregated EEG dataset portion and the bioparametric portion, wherein the cognitive state metric indicates a cognitive state of the user during the first time period;   determining a stimulus to modify the cognitive state of the user, based on the cognitive state metric; and   providing, at an output of the biomonitoring neuroheadset, the stimulus to the user.   
     
     
         12 . The method of  claim 11 , wherein the output of the biomonitoring neuroheadset comprises a speaker, and wherein the stimulus comprises an audio therapy. 
     
     
         13 . The method of  claim 11 , further comprising:
 in response to providing the stimulus to the user at the output of the biomonitoring neuroheadset:
 generating a second aggregated EEG dataset during a second time period; 
 extracting a second bioparameter from the second aggregated EEG dataset contemporaneously with the second time period; and 
   generating a second combined dataset based on the second aggregated EEG dataset and the second bioparameter.   generating a second cognitive state metric based on the second combined dataset, wherein the second cognitive state metric indicates a cognitive state response to the stimulus during the second time period.   
     
     
         14 . A system for detecting bioelectrical signals from a user, comprising:
 a first EEG sensor positioned proximal an ear canal of the user, the first EEG sensor configured to collect a first EEG signal dataset from the user during a first time period;   a second EEG sensor positioned proximal a contralateral ear canal of the user, the second EEG sensor configured to collect a second EEG signal dataset from the user during the first time period;   a noise reduction subsystem comprising:
 a first reference sensor positioned proximal a mastoid process of a temporal bone proximal the ear canal, 
 a second reference sensor positioned proximal a contralateral mastoid process of a contralateral temporal bone proximal the contralateral ear canal, 
 the noise reduction subsystem configured to generate an averaged reference signal dataset from outputs of the first and second reference sensors contemporaneously with collection of the first and second EEG signal datasets during the first time period, wherein the first and second EEG signal datasets are referenced to the averaged reference signal dataset; 
   a wearable support frame worn at a head region of the user and cooperatively supported at the ear region and the contralateral ear region, the wearable support frame supporting and physically connecting the first and second EEG sensors and the first and second reference sensors; and   an electronics subsystem comprising a processing module, the processing module comprising an averaging circuit configured to produce an aggregated EEG dataset from processing the first and second EEG signal datasets, the electronics subsystem electronically connected to the first EEG sensor, the second EEG sensor, and the noise reduction subsystem.   
     
     
         15 . The system of  claim 14 , wherein the processing module is configured to extract a cardiovascular parameter from the aggregated EEG dataset, wherein the cardiovascular comprises at least one of a heart rate and a heart rate variability. 
     
     
         16 . The system of  claim 15 , wherein the processing module is further configured to:
 identify a time-varying oscillation in values of the aggregated EEG dataset;   estimate the cardiovascular parameter based on the time-varying oscillation in values.   
     
     
         17 . The system of  claim 16 , wherein identifying the time-varying oscillation in values of the aggregated EEG dataset comprises identifying a set of QRS complex sequences from the values of the aggregated EEG dataset. 
     
     
         18 . The system of  claim 16 , wherein the processing module is further configured to generate a cognitive state metric for the user based on the aggregated EEG dataset and the cardiovascular parameter, wherein the cognitive state metric indicates a cognitive state of the user during the first time period. 
     
     
         19 . The system of  claim 14 , wherein the first reference sensor is a first common mode sensor, wherein the second reference sensor is a second common mode sensor, wherein the averaged reference signal dataset is an averaged common mode signal dataset, wherein the noise reduction subsystem further comprises a driven right leg module positioned proximal the first common mode sensor and the mastoid process of the temporal bone, and wherein the processing module is further configured to produce a noise-reduced aggregated EEG dataset from a driven right leg dataset generated by the driven right leg module in combination with the aggregated EEG dataset. 
     
     
         20 . The system of  claim 14 , wherein the processing module comprises:
 a first processing module positioned proximal the first EEG sensor, and   a second processing module positioned proximal the second EEG sensor.