Systems and methods for collecting, analyzing, and sharing bio-signal and non-bio-signal data

Abstract

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

1 . A brainwave monitoring system comprising:
 at least one client computing device storing or accessing an application;   at least one bio-signal sensor including at least one electroencephalography (EEG) bio-signal sensor and in communication with the at least one client computing device;   at least one user effector to provide a real-time biofeedback output for the application; and   the at least one client computing device configured to:
 receive from at least one computer server a client pipeline instance generated or selected on the at least one computer server, the client pipeline instance based on a prediction model based on aggregated data, the client pipeline instance for predicting brain states; 
 receive time-coded EEG bio-signal data of a user from the at least one EEG bio-signal sensor; 
 acquire time-coded feature event data; 
 extract feature events from the time coded feature event data at feature event time codes; 
 label segments in the time-coded EEG bio-signal data using the feature event time codes using the client pipeline instance; 
 determine biofeedback output based on the labelled segments of the time-coded EEG bio-signal data, the biofeedback output based in part on a brain state of the user at the EEG bio-signal time codes; 
 output the biofeedback output using the user effector; and 
 transmit a user response to the biofeedback output to the at least one computer server to update the aggregated data and the prediction model. 
   
     
     
         2 . The system of  claim 1 , further comprising:
 at least one additional client computing device storing or accessing an additional application;   at least one additional bio-signal sensor including at least one additional electroencephalography (EEG) bio-signal sensor and in communication with the at least one additional client computing device;   at least one additional user effector to provide an additional real-time biofeedback output for the additional application; and   the at least one additional client computing device configured to:
 receive from the at least one computer server an additional client pipeline instance generated or selected on the computer server, the additional client pipeline instance based on the updated prediction model based on the updated aggregated data; 
 receive additional time-coded EEG bio-signal data of an additional user from the additional at least one EEG bio-signal sensor; 
 acquire additional time-coded feature event data; 
 extract additional feature events from the additional time coded feature event data at additional feature event time codes; 
 label additional segments in the additional time-coded EEG bio-signal data using the additional feature event time codes using the additional client pipeline instance; 
 determine additional biofeedback output based on the additional labelled segments of the additional time-coded EEG bio-signal data, the additional biofeedback output based in part on an additional brain state of the additional user at the additional EEG bio-signal time codes; and 
 output the additional biofeedback output using the additional user effector. 
   
     
     
         3 . The system of  claim 1 , wherein the at least one computer server is configured to generate or select the client pipeline instance based on a bio-signal interaction profile corresponding to the user. 
     
     
         4 . The system of  claim 1 , wherein user information is transmitted to the at least one computer server, and the at least one computer server is configured to generate or select the client pipeline instance based on the user information. 
     
     
         5 . The system of  claim 1 , wherein the client pipeline instance is generated or selected based on the computational resources available at the client computing device. 
     
     
         6 . The system of  claim 1 , wherein the client pipeline instance is generated or selected based on a criteria regarding a user's current state. 
     
     
         7 . The system of  claim 1 , wherein the biofeedback output comprises a user-response classification and the user response to the biofeedback output comprises confirmation of accuracy of the user-response classification. 
     
     
         8 . The system of  claim 1 , wherein the feature events are extracted using the client pipeline instance. 
     
     
         9 . The system of  claim 1 , wherein the at least one client computing device is further configured to tune the client pipeline instance based on the user response to the biofeedback output. 
     
     
         10 . The system of  claim 1 , wherein the aggregated data comprises data from other users using other applications, wherein the application and the other applications are different. 
     
     
         11 . A computer readable medium storing machine executable instructions to configure a processor to execute a brainwave monitoring process comprising:
 receiving a client pipeline instance generated or selected on at least one computer server, the client pipeline instance based on a prediction model based on aggregated data, the client pipeline instance for predicting brain states;   receiving time-coded EEG bio-signal data of a user;   acquiring time-coded feature event data;   extracting feature events from the time coded feature event data at feature event time codes;   labelling segments in the time-coded EEG bio-signal data using the feature event time codes using the client pipeline instance;   determining biofeedback output based on the labelled segments of the time-coded EEG bio-signal data, the biofeedback output based in part on a brain state of the user at the EEG bio-signal time codes;   outputting the biofeedback output using a user effector; and   transmitting a user response to the biofeedback output to the at least one computer server to update the aggregated data and the prediction model.   
     
     
         12 . The computer readable medium of  claim 11 , the brainwave monitoring process further comprising:
 receiving from the at least one computer server an additional client pipeline instance generated or selected on the computer server, the additional client pipeline instance based on the updated prediction model based on the updated aggregated data;   receiving additional time-coded EEG bio-signal data of an additional user;   acquiring additional time-coded feature event data;   extracting additional feature events from the additional time coded feature event data at additional feature event time codes;   labelling additional segments in the additional time-coded EEG bio-signal data using the additional feature event time codes using the additional client pipeline instance;   determining additional biofeedback output based on the additional labelled segments of the additional time-coded EEG bio-signal data, the additional biofeedback output based in part on an additional brain state of the additional user at the additional EEG bio-signal time codes; and   outputting the additional biofeedback output using an additional user effector.   
     
     
         13 . The computer readable medium of  claim 11 , wherein the at least one computer server is configured to generate or select the client pipeline instance based on a bio-signal interaction profile corresponding to the user. 
     
     
         14 . The computer readable medium of  claim 11 , wherein the at least one computer server is configured to generate or select the client pipeline instance based on user information. 
     
     
         15 . The computer readable medium of  claim 11 , wherein the client pipeline instance is generated or selected based on the computational resources available at a client computing device. 
     
     
         16 . The computer readable medium of  claim 11 , wherein the client pipeline instance is generated or selected based on a criteria regarding a user's current state. 
     
     
         17 . The computer readable medium of  claim 11 , wherein the biofeedback output comprises a user-response classification and the user response to the biofeedback output comprises confirmation of accuracy of the user-response classification. 
     
     
         18 . The computer readable medium of  claim 11 , wherein the feature events are extracted using the client pipeline instance. 
     
     
         19 . The computer readable medium of  claim 11 , the brainwave monitoring process further comprising tuning the client pipeline instance based on the user response to the biofeedback output. 
     
     
         20 . The computer readable medium of  claim 11 , wherein the aggregated data comprises data from other users using other applications different from an application used by the user.