WO2023059991A1

WO2023059991A1 - Matching based on inter-brain measurements

Info

Publication number: WO2023059991A1
Application number: PCT/US2022/076892
Authority: WO
Inventors: Michael Moshe YARTSEV
Original assignee: The Regents Of The University Of California
Priority date: 2021-10-04
Filing date: 2022-09-22
Publication date: 2023-04-13

Abstract

An example method includes obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction, constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements, and computing a similarity between the first time series and the second time series.

Description

MATCHING BASED ON INTER-BRAIN MEASUREMENTS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority of United States Provisional Patent Application Serial No. 63/252,138, filed October 4, 2021 , which is herein incorporated by reference in its entirety.

FIELD OF THE INVENTION

[0002] The invention relates generally to assessing compatibility, and relates more particularly to the use of simultaneous neural measurements from two or more individuals to assess interaction features between the two or more individuals.

BACKGROUND

[0003] There are many situations in which it may be beneficial to evaluate whether two or more individuals are compatible, or a “good match.” For instance, employers who are evaluating prospective job candidates often try to determine whether the prospective candidates will be able to work well with the employer’s current employees. Situations may also arise in which the employer may need to form a team amongst a set of current employees to work together on a project or task, and the employer may want to select a group of employees who can collaborate effectively with little to no conflict.

SUMMARY

[0004] In one example, a method performed by a processing system including at least one processor includes obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction, constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements, and computing a similarity between the first time series and the second time series.

[0005] In another example, a non-transitory machine-readable storage medium is encoded with instructions executable by a processing system including at least one processor. When executed, the instructions cause the processing system to perform operations including obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction, constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements, and computing a similarity between the first time series and the second time series.

[0006] In another example, a system includes a processing system including at least one processor and a non-transitory machine-readable storage medium encoded with instructions executable by the processing system. When executed, the instructions cause the processing system to perform operations including obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction, constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements, and computing a similarity between the first time series and the second time series. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 illustrates an example system related to the present disclosure;

[0008] FIG. 2 is a flow diagram illustrating one example of a method for matching based on simultaneously captured inter-brain measurements, according to the present disclosure; and

[0009] FIG. 3 depicts a high-level block diagram of a computing device or processing system specifically programmed to perform the functions described herein.

DETAILED DESCRIPTION

[0010] The present disclosure broadly describes an apparatus, method, and non-transitory computer-readable medium for matching based on inter-brain measurements. As discussed above, there are many situations in which it may be beneficial to evaluate whether two or more individuals are compatible, or a “good match.” For instance, employers who are evaluating prospective job candidates often try to determine whether the prospective candidates will be able to work well with the employer’s current employees. Situations may also arise in which the employer may need to form a team amongst a set of current employees to work together on a project or task, and the employer may want to select a group of employees who can collaborate effectively with little to no conflict.

[0011] These evaluations are typically based on subjective measures, such as the employer’s impressions of individual personalities, strengths, and weaknesses. In the best situations, the employer may have some prior knowledge of the abilities of certain employees to work together (e.g., from work on past projects). However, even this information can be susceptible to subjective biases and/or change and can vary greatly between individuals. In some cases, making the wrong choice can be costly. For instance, a great deal of time and resources may be invested in training a new employee; if that new employee quits after a short period of time or cannot work with their boss or co-workers to effectively perform the job for which they have been hired, the investment may be wasted.

[0012] Examples of the present disclosure evaluate the compatibility of two or more individuals using inter-brain measurements (i.e., simultaneous neural measurements of the two or more individuals). In one example, the brain electrical activity (e.g., electroencephalogram signals or “brain waves”) of the two or more individuals may be measured simultaneously, during an interaction between the two or more individuals. The measured brain electrical activity for the two or more individuals may then be correlated to assess a degree of compatibility between the two or more individuals. The degree of correlation can be used as an unbiased biological measurement (e.g., like a heart rate measurement) of the compatibility between the individuals. These and other aspects of the disclosure are discussed in greater detail below in connection with FIGs. 1-3.

[0013] To aid in understanding the present disclosure, FIG. 1 illustrates an example system 100, related to the present disclosure. As shown in FIG. 1 , the system 100 connects a plurality of user endpoint devices 108, 110, 112, and 114 with one another and with various other devices such as an application server (AS) 104 and a database (DB) 106 via a core network, e.g., a telecommunication network 102, and one or more wireless access networks (e.g., cellular networks) 120 and 122. In one example, the user endpoint devices 108-114 may include any wired or wireless devices that are capable of cellular and/or non-cellular network communication, including desktop computers, laptop computers, tablet computers, mobile phones, gaming consoles, Internet of Things (loT) devices, wearable smart devices (e.g., fitness trackers, smart watches, head mounted displays, etc.), and the like. At least some of the user endpoint devices, e.g., user endpoint devices 108 and 110, may comprise devices that are capable of measuring an individual’s brain electrical activity, such as electroencephalogram (EEG) caps, brain wave sensor headbands such as those used for therapy and meditation, in-ear electrical measurement devices, smart glasses with sensors for measurement of brain activity, intracranial implants, or any other devices that are capable of detecting electrical charges resulting from the activity of an individual’s brain cells. In one example, the user endpoint devices 108 and 110 comprise portable, wearable devices.

[0014] The telecommunication network 102 may comprise a core network, a backbone network, ora transport network, such as an Internet Protocol (I P)/multi- protocol label switching (MPLS) network, where label switched routes (LSRs) can be assigned for routing Transmission Control Protocol (TCP)/IP packets, User Datagram Protocol (UDP)/IP packets, and other types of protocol data units (PDUs), and so forth. It should be noted that an IP network is broadly defined as a network that uses Internet Protocol to exchange data packets. However, it will be appreciated that the present disclosure is equally applicable to other types of data units and transport protocols, such as Frame Relay and Asynchronous Transfer Mode (ATM). In one example, the telecommunication network 102 uses a network function virtualization infrastructure (NFVI), e.g., host devices or servers that are available as host devices to host virtual machines comprising virtual network functions (VNFs). In other words, at least a portion of the telecommunication network 102 may incorporate software-defined network (SDN) components.

[0015] In one example, the AS 104 may comprise a computing system, such as computing system 300 depicted in FIG. 3, and may be configured to provide one or more functions for matching based on inter-brain measurements, reported by the user endpoint devices, in accordance with the present disclosure. For example, AS 104 may be configured to collect data from a plurality of user endpoint devices, e.g., user endpoint devices 108 and 110, where the user endpoint devices are capable of measuring individuals’ brain electrical activity, and correlating the data to determine whether two or more individuals are compatible for a given purpose. The AS 104 may be further configured to share at least some of the raw data collected from the user endpoint devices and/or the correlated data with other user endpoint devices, e.g., user endpoint devices 112 and 114, for review. For instance, users may access an application hosted by the AS 104 for matching based on inter-brain measurements using a network- connected device, such as user endpoint devices 112 and 114.

[0016] It should be noted that as used herein, the terms “configure,” and “reconfigure” may refer to programming or loading a processing system with computer-readable/computer-executable instructions, code, and/or programs, e.g., in a distributed or non-distributed memory, which when executed by a processor, or processors, of the processing system within a same device or within distributed devices, may cause the processing system to perform various functions. Such terms may also encompass providing variables, data values, tables, objects, or other data structures or the like which may cause a processing system executing computer-readable instructions, code, and/or programs to function differently depending upon the values of the variables or other data structures that are provided. As referred to herein a “processing system” may comprise a computing device including one or more processors, or cores (e.g., as illustrated in FIG. 3 and discussed below) or multiple computing devices collectively configured to perform various steps, functions, and/or operations in accordance with the present disclosure.

[0017] The DB 106 may store brain electrical activity measurements reported by the user endpoint devices, e.g., user endpoint devices 108 and 110. In one example, sets of brain electrical activity measurements that are stored in the DB 106 may be indexed or annotated with metadata that indicates information about the measurements, such as an identity (e.g., name, identifying number, or other identifier) of the individual from whom the measurements were taken, a day and time at which the measurements were taken, a location at which the measurements were taken, and other individuals whose brain electrical activity measurements should be correlated with the measurements (e.g., due to the respective individuals being involved in the same interaction when the measurements were taken). The brain electrical activity measurements that are stored in the DB 106 may comprise raw data reported by the user endpoint devices and may be accessed by the AS 104 for further processing, as discussed in greater detail in connection with FIG. 2. In a further example, the DB 106 may store time series or other data structures constructed by the AS 104 using the raw data accessed from the DB 106.

[0018] In one example, wireless access networks 120 and 122 may each comprise a radio access network implementing such technologies as: global system for mobile communication (GSM), e.g., a base station subsystem (BSS), or IS-95, a universal mobile telecommunications system (UMTS) network employing wideband code division multiple access (WCDMA), or a CDMA3000 network, among others. In other words, each wireless access network 120 and 122 may comprise an access network in accordance with any “second generation” (2G), “third generation” (3G), “fourth generation” (4G), “fifth generation” (5G), Long Term Evolution (LTE) or any other existing or yet to be developed future wireless/cellular network technology. In one example, wireless access networks 120 and 122 may be operated by the same ora different service provider that is operating telecommunication network 102.

[0019] It should be noted that the system 100 has been simplified. In other words, the system 100 may be implemented in a different form than that illustrated in FIG. 1. For example, the system 100 may be expanded to include additional networks, and additional network elements (not shown) such as wireless transceivers and/or base stations, border elements, routers (including edge routers), switches, policy servers, security devices, gateways, a network operations center (NOC), a content distribution network (CDN) and the like, without altering the scope of the present disclosure. In addition, system 100 may be altered to omit various elements, substitute elements for devices that perform the same or similar functions and/or combine elements that are illustrated as separate devices.

[0020] FIG. 2 is a flow diagram illustrating one example of a method 200 for matching based on simultaneously captured inter-brain measurements, according to the present disclosure. The method 200 may be performed, for instance, by an application server, such as the AS 104 illustrated in FIG. 1 and described above. Alternatively or in addition, the method 200 may be performed by a computing system, such as the computing system 300 illustrated in FIG. 3 and described in greater detail below. For the sake of example, the method 200 is described as being performed by a processing system.

[0021] The method 200 may begin in step 202. In step 204, the processing system may simultaneously obtain a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction.

[0022] The inter-personal interaction may comprise any sort of interaction in which two or more individuals are communicating with each other. For instance, the inter-personal interaction may comprise a job interview, a casual conversation, playing a game together, or the like. In one example, the interpersonal interactions may take any one or more of a variety of forms, such as an in-person meeting (e.g., face to face), a virtual meeting (e.g., video conference), a telephone call, a text-based chat, or the like. In one example, the individuals engage in the inter-personal interaction in a synchronous manner (i.e., at the same time, such as speaking face to face or participating in a text-based chat at the same time). Whatever the form of the inter-personal interaction, the brain electrical activity measurements of the individuals may comprise real-time measurements of the individuals’ brain electrical activity during the inter-personal interaction. Moreover, the brain electrical activity measurements of the individuals (e.g., the first individual and the second individual) are measured simultaneously, so that the brain electrical activity measurements represent the different perspectives of the individuals during the same time.

[0023] The brain electrical activity measurements may be measured by any device that is capable of measuring an individual’s brain electrical activity, such as an electroencephalogram (EEG) cap, a brain wave sensor headband such as those used for therapy and meditation, in-ear electrical measurement devices, smart glasses with sensors for measurement of brain activity, intracranial implants, or another device that is capable of detecting electrical charges resulting from the activity of an individual’s brain cells. In one example, the processing system may obtain the brain electrical activity measurements directly from the devices that measured the brain electrical activity of the first individual and the second individual. However, in another example, the brain electrical activity measurements may be measured by the devices and stored in a database (e.g., DB 106 of FIG. 1 ), and the processing system may retrieve the brain electrical activity measurements from the database.

[0024] In one example, the brain electrical activity measurements comprise measurements in a broad frequency range (e.g., 1-6000 Hertz) or a narrower frequency range (e.g., 30-150 Hertz). In a further example, the brain electrical activity measurements may comprise discrete measurements that are measured periodically (e.g., every x seconds). In a further example, the brain electrical activity measurements may comprise measurements that are measured continuously. Thus, a set of brain electrical activity measurements may comprise a waveform of an individual’s brain electrical activity over a period of time spanning at least part of the inter-personal interaction. The general waveform may show bursts of energy where the individual is responding to particular stimuli. [0025] Although step 204 describes obtaining brain electrical activity measurements from a first individual and a second individual, it will be appreciated that any number of individuals may participate in the inter-personal interaction, and that brain electrical activity measurements may be obtained from any number of these individuals. In other words, if brain electrical activity measurements are obtained from three or more individuals who participated in the inter-personal interaction, then obtaining brain electrical activity measurements from these three or more individuals would necessarily include obtaining brain electrical activity measurements from a first individual and a second individual (as well as from third and/or further individuals).

[0026] In step 206, the processing system may construct a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements.

[0027] In one example, a time series may be constructed from the set of brain electrical activity measurements by first filtering the set of brain electrical activity measurements in a defined frequency band. That is, the processing system may select a specific frequency band within the frequencies of brain electrical activity that are captured, and may focus any further analysis and processing on the brain electrical activity that falls within this specific frequency band. For instance, as discussed above, the brain electrical activity measurements may comprise measurements in the frequency range of one to six thousand Hertz (Hz). The defined frequency band in this example may comprise a band of forty to sixty Hz. Thus, the defined frequency band may be a narrower band than the band over which the brain electrical activity measurements were recorded.

[0028] In one example, once the brain electrical activity measurements have been filtered to extract the measurements within the defined frequency band, the signal-space projection (SSP) for power within the defined frequency band may be computed over a plurality of consecutive windows of time totaling a defined period of time. For instance, the SSP may be computed for every x-second window over y minutes. The result of the computations is a plurality of points, organized in time over the defined period of time, which constitutes a time series for the corresponding set of brain electrical activity measurements.

[0029] In step 208, the processing system may compute a similarity between the first time series and the second time series. That is, in one example, the processing system may compare the first time series to the second time series and compute a measure of how strongly the first time series resembles the second time series. In one example, the measure may be computed as a ratio of the number of corresponding points in the two time series (e.g., corresponding in time) that share a threshold closeness (e.g., within z Hz of each other) to the number of corresponding points in the two time series that do not share the threshold closeness. Thus, this ratio may give an objective measure of how often the brain electrical activity of the first and second individuals was similar.

[0030] In optional step 210 (illustrated in phantom), the processing system may determine whether the similarity between the first time series and the second time series indicates a compatibility of the first individual and the second individual. In one example, the similarity may indicate a compatibility of the first individual and the second individual if the similarity computed in step 208 at least meets a predefined threshold similarity; conversely, the first individual and the second individual may be considered to be not compatible if the similarity computed in step 208 does not at least meet the predefined threshold similarity. The predefined threshold similarity may be configurable based on the context in which compatibility is being assessed. For instance, the predefined threshold similarity may be higher when assessing compatibility for the purposes of hiring a new employee than when assessing the compatibility of a team of existing employees. A user may determine the predefined threshold compatibility that is desired for a particular context.

[0031] In one example, the predefined threshold similarity may be user configurable. For instance, a user may set the value for the predefined threshold similarity depending upon the context for which the compatibility is being evaluated. For instance, a dating application may require a higher degree of compatibility (and, thus, a higher threshold similarity) than an employer who is selecting employees for a team project.

[0032] The method 200 may end in step 212. [0033] Thus, the method 200 may provide an objective measure of the compatibility of two or more individuals (e.g., whether the two or more individuals are “on the same wavelength”). The method 200 may therefore prove useful in a variety of contexts in which the compatibility of two or more individuals may be crucial. For instance, an employer may utilize the method 200 to determine whether an individual who is interviewing for a job with the employer is likely to be able to work well with the employer’s current employees. In another example, an employer may utilize the method 200 to select a group of existing employees to work together on a team project. In another example, an individual may utilize the method 200 to evaluate potential service providers, such as healthcare or legal professionals, with whom the individual desires a certain degree of personal comfort. In yet another example, the method 200 may be used to evaluate the compatibility of individuals who are subscribed to a dating service or application. [0034] It should be noted that any references to a “first individual,” “second individual,” and the like in the above discussion are not intended to imply that a certain number of individuals are required by the present disclosure. For instance, a reference to a “second individual” in the discussion does not necessarily require that a “first individual” be present. Nor does the absence of a reference to a “third individual,” “fourth individual,” or the like in the discussion necessarily imply that more than two individuals cannot be evaluated for compatibility based on inter-brain measurements in accordance with the method 200. Identifiers such as “first,” “second,” and the like are merely used for the purposes of clarity, to distinguish between separate individuals.

[0035] In addition, although not specifically specified, one or more steps, functions or operations of the method 200 may include a storing, displaying and/or outputting step as required for a particular application. In other words, any data, records, fields, and/or intermediate results discussed in the method 200 can be stored, displayed and/or outputted either on the device executing the method 200, or to another device, as required for a particular application. Furthermore, steps, blocks, functions, or operations in FIG. 2 that recite a determining operation or involve a decision do not necessarily require that both branches of the determining operation be practiced. In other words, one of the branches of the determining operation can be deemed as an optional step. In addition, one or more steps, blocks, functions, or operations of the above described method 200 may comprise optional steps, or can be combined, separated, and/or performed in a different order from that described.

[0036] FIG. 3 depicts a high-level block diagram of a computing device or processing system specifically programmed to perform the functions described herein. As depicted in FIG. 3, the processing system 300 comprises one or more hardware processor elements 302 (e.g., a central processing unit (CPU), a microprocessor, or a multi-core processor), a memory 304 (e.g., random access memory (RAM) and/or read only memory (ROM)), a module 305 for matching based on inter-brain measurements, and various input/output devices 306 (e.g., storage devices, including but not limited to, a tape drive, a floppy drive, a hard disk drive or a compact disk drive, a receiver, a transmitter, a speaker, a display, a speech synthesizer, an output port, an input port and a user input device (such as a keyboard, a keypad, a mouse, a microphone and the like)). In accordance with the present disclosure, input/output devices 306 may also include sensors or electrodes that are configured to monitor brain electrical activity, and so forth. Although only one processor element is shown, it should be noted that the computing device may employ a plurality of processor elements. Furthermore, although only one computing device is shown in the figure, if the method 200 as discussed above is implemented in a distributed or parallel mannerfor a particular illustrative example, i.e., the steps of the above method 200, or the entire method 200 is implemented across multiple or parallel computing devices, e.g., a processing system, then the computing device of this figure is intended to represent each of those multiple computing devices.

[0037] Furthermore, one or more hardware processors can be utilized in supporting a virtualized or shared computing environment. The virtualized computing environment may support one or more virtual machines representing computers, servers, or other computing devices. In such virtualized virtual machines, hardware components such as hardware processors and computer- readable storage devices may be virtualized or logically represented. The hardware processor 302 can also be configured or programmed to cause other devices to perform one or more operations as discussed above. In other words, the hardware processor 302 may serve the function of a central controller directing other devices to perform the one or more operations as discussed above.

[0038] It should be noted that the present disclosure can be implemented in software and/or in a combination of software and hardware, e.g., using application specific integrated circuits (ASIC), a programmable gate array (PGA) including a Field PGA, or a state machine deployed on a hardware device, a computing device or any other hardware equivalents, e.g., computer readable instructions pertaining to the method discussed above can be used to configure a hardware processor to perform the steps, functions and/or operations of the above disclosed method 200. In one example, instructions and data for the present module or process 305 for matching based on inter-brain measurements (e.g., a software program comprising computer-executable instructions) can be loaded into memory 304 and executed by hardware processor element 302 to implement the steps, functions, or operations as discussed above in connection with the illustrative method 200. Furthermore, when a hardware processor executes instructions to perform “operations,” this could include the hardware processor performing the operations directly and/or facilitating, directing, or cooperating with another hardware device or component (e.g., a co-processor and the like) to perform the operations.

[0039] The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module 305 for matching based on inter-brain measurements (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette, and the like. Furthermore, a “tangible” computer- readable storage device or medium comprises a physical device, a hardware device, or a device that is discernible by the touch. More specifically, the computer-readable storage device may comprise any physical devices that provide the ability to store information such as data and/or instructions to be accessed by a processor or a computing device such as a computer or an application server. [0040] While various examples have been described above, it should be understood that they have been presented by way of illustration only, and not a limitation. Thus, the breadth and scope of any aspect of the present disclosure should not be limited by any of the above-described examples, but should be defined only in accordance with the following claims and their equivalents.

Claims

What is claimed is:

1. A method, comprising: obtaining, by a processing system including at least one processor, a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction; constructing, by the processing system, a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements; and computing, the a processing system, a similarity between the first time series and the second time series.

2. The method of claim 1 , wherein the inter-personal interaction comprises an in-person meeting.

3. The method of claim 1 , wherein the inter-personal interaction comprises a virtual meeting.

4. The method of claim 1 , wherein the inter-personal interaction comprises a telephone call.

5. The method of claim 1 , wherein the inter-personal interaction comprises a text-based chat.

6. The method of claim 1 , where the first individual and the second individual engage in the inter-personal interaction in a synchronous manner.

7. The method of claim 1 , wherein the first set of brain electrical activity measurements and the second set of brain electrical activity measurements are measured simultaneously.

8. The method of claim 1 , wherein at least one of: the first set of brain electrical activity measurements or the second set of brain electrical activity measurements is measured using an electroencephalogram cap.

9. The method of claim 1 , wherein at least one of: the first set of brain electrical activity measurements or the second set of brain electrical activity measurements is measured using a brain wave sensor headband.

10. The method of claim 1 , wherein the first set of brain electrical activity measurements and the second set of brain electrical activity measurements comprise measurements in a frequency range of one to six thousand Hertz.

11. The method of claim 1 , wherein the first set of brain electrical activity measurements and the second set of brain electrical activity measurements comprise discrete measurements that are measured periodically.

12. The method of claim 1 , wherein the first set of brain electrical activity measurements and the second set of brain electrical activity measurements comprise measurements that are measured continuously.

13. The method of claim 1 , wherein the constructing comprises: filtering the first set of brain electrical activity measurements and the second set of brain electrical activity measurements in a defined frequency band that is narrower than a band over which the first set of brain electrical activity measurements and the second set of brain electrical activity measurements were recorded; computing a first signal-space projection for power within the defined frequency band for the first set of brain electrical activity measurements, over a plurality of consecutive windows of time totaling a defined period of time; and computing a second signal-space projection for power within the defined frequency band for the second set of brain electrical activity measurements, over the plurality of consecutive windows of time totaling the defined period of time.

14. The method of claim 13, wherein the defined frequency band comprises a band of forty to sixty Hertz.

15. The method of claim 1 , further comprising: determining, by the processing system, whether the similarity between the first time series and the second time series indicates a compatibility of the first individual and the second individual.

16. The method of claim 1 , wherein the similarity between the first time series and the second time series is computed as a ratio of a number of corresponding points in the first time series and the second time series that share a threshold closeness to a number of corresponding points in the first time series and the second time series that do not share the threshold closeness.

17. The method of claim 16, wherein the first individual and the second individual are determined to be compatible when the ratio at least meets a predefined threshold similarity.

18. The method of claim 16, wherein the first individual and the second individual are determined to be incompatible when the ratio fails to at least meet a predefined threshold similarity.

19. A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least on processor, cause the processing system to perform operations, the operations comprising: obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while

17 participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction; constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements; and computing a similarity between the first time series and the second time series.

20. An apparatus, comprising: a processing system including at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising: obtaining a first set of brain electrical activity measurements and a second set of brain electrical activity measurements, where the first set of brain electrical activity measurements represents brain electrical activity of a first individual while participating in an inter-personal interaction with a second individual, and where the second set of brain electrical activity measurements represents brain electrical activity of the second individual while participating in the inter-personal interaction; constructing a first time series from the first set of brain electrical activity measurements and a second time series from the second set of brain electrical activity measurements; and computing a similarity between the first time series and the second time series.

18