WO2025235566A1 - Systems and methods for detecting a response bias in an individual - Google Patents

Abstract

Provided herein are systems and methods of linking biological activity data to an assessment tool (e.g., a series of questions), enabling a clinician to evaluate an individual's biological activity data profile with particular assessment results. The biological activity may be brainwave data obtained via quantitative (digital) EEG.

Description

SYSTEMS AND METHODS FOR DETECTING A RESPONSE BIAS IN AN

INDIVIDUAL

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present patent application claims priority under 35 U.S.C. 119(e) to the U.S provisional patent application identified as U.S. Serial No. 63/643,567, filed on May 7, 2024, which is hereby incorporated by reference herein in its entirety.

BACKGROUND

[0002] To date, no tools exist that allow for the analysis of EEG on an item-by-item level on psychometric assessments. There are no technologies available to date that allow for the recording of EEG with marking notation function imposed to the EEG feed when the participant completes an item on an assessment/questionnaire. Furthermore, there is a dearth of methods which enable the simultaneous recording/analysis of human EEG activity across the span of an assessment.

BRIEF DESCRIPTION OF THE DRAWINGS

[0003] Several embodiments of the present disclosure are hereby illustrated in the appended drawings. It is to be noted however, that the appended drawings only illustrate several typical embodiments and are therefore not intended to be considered limiting of the scope of the inventive concepts disclosed herein. The figures are not necessarily to scale and certain features and certain views of the figures may be shown as exaggerated in scale or in schematic in the interest of clarity and conciseness.

[0004] FIG. 1 provides a visual example of the differences between the clinician and client screen. The images depicted on the computer screens are normal for the industry and do not reflect the screens described in the description below.

[0005] FIG. 2 provides exemplary EEG activity data. Orange line indicates the addition of EEG feed demarcation that is automatically applied when the subject completes the question. DETAILED DESCRIPTION

[0006] Provided herein is a method of linking biological activity data obtained via one or more sensor to an assessment tool (e.g., a series of questions), enabling a clinician to evaluate an individual’s biological activity in conjunction with particular assessment results.

[0007] Before further describing various embodiments of the compositions and methods of the present disclosure in more detail by way of exemplary description, examples, and results, it is to be understood that the embodiments of the present disclosure are not limited in application to the details of methods and compositions as set forth in the following description. The embodiments of the compositions and methods of the present disclosure are capable of being practiced or carried out in various ways not explicitly described herein. As such, the language used herein is intended to be given the broadest possible scope and meaning; and the embodiments are meant to be exemplary, not exhaustive. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting unless otherwise indicated as so. Moreover, in the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to a person having ordinary skill in the art that the embodiments of the present disclosure may be practiced without these specific details. In other instances, features which are well known to persons of ordinary skill in the art have not been described in detail to avoid unnecessary complication of the description. While the methods of the present disclosure have been described in terms of particular embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the spirit, and scope of the inventive concepts as described herein. All such similar substitutes and modifications apparent to those having ordinary skill in the art are deemed to be within the spirit and scope of the inventive concepts as disclosed herein.

[0008] All patents, published patent applications, and non-patent publications referenced or mentioned in any portion of the present specification are indicative of the level of skill of those skilled in the art to which the present disclosure pertains, and are hereby expressly incorporated by reference in their entirety to the same extent as if the contents of each individual patent or publication was specifically and individually incorporated herein.

[0009] Unless otherwise defined herein, scientific and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those having ordinary skill in the art. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[00010] As utilized in accordance with the methods and compositions of the present disclosure, the following terms, unless otherwise indicated, shall be understood to have the following meanings:

[00011] The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or when the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” The use of the term “at least one” will be understood to include one as well as any quantity more than one, including but not limited to, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 100, or any integer inclusive therein. The term “at least one” may extend up to 100 or 1000 or more, depending on the term to which it is attached; in addition, the quantities of 100/1000 are not to be considered limiting, as higher limits may also produce satisfactory results. In addition, the use of the term “at least one of X, Y and Z” will be understood to include X alone, Y alone, and Z alone, as well as any combination of X, Y and Z.

[00012] As used in this specification and claims, the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[00013] The term “or combinations thereof’ as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof’ is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AAB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context. [00014] Throughout this application, the terms “about” or “approximately” are used to indicate that a value includes the inherent variation of error for the composition, the method used to administer the composition, or the variation that exists among the objects, or study subjects. As used herein the qualifiers “about” or “approximately” are intended to include not only the exact value, amount, degree, orientation, or other qualified characteristic or value, but are intended to include some slight variations due to measuring error, manufacturing tolerances, stress exerted on various parts or components, observer error, wear and tear, and combinations thereof, for example. The term “about” or “approximately”, where used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass, for example, variations of ± 20% or ± 10%, or ± 5%, or ± 1%, or ± 0.1% from the specified value, as such variations are appropriate to perform the disclosed methods and as understood by persons having ordinary skill in the art. As used herein, the term “substantially” means that the subsequently described event or circumstance completely occurs or that the subsequently described event or circumstance occurs to a great extent or degree. For example, the term “substantially” means that the subsequently described event or circumstance occurs at least 90% of the time, or at least 95% of the time, or at least 98% of the time.

[00015] As used herein any reference to "one embodiment" or "an embodiment" means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

[00016] As used herein, all numerical values or ranges include fractions of the values and integers within such ranges and fractions of the integers within such ranges unless the context clearly indicates otherwise. Thus, to illustrate, reference to a numerical range, such as 1-10 includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc., and so forth. Reference to a range of 1-50 therefore includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc., up to and including 50, as well as 1.1, 1.2, 1.3, 1.4, 1.5, etc., 2.1, 2.2, 2.3, 2.4, 2.5, etc., and so forth. Reference to a series of ranges includes ranges which combine the values of the boundaries of different ranges within the series. Thus, to illustrate reference to a series of ranges, for example, a range of 1-1,000 includes, for example, 1-10, 10-20, 20- 30, 30-40, 40-50, 50-60, 60-75, 75-100, 100-150, 150-200, 200-250, 250-300, 300-400, 400- 500, 500-750, 750-1,000, and includes ranges of 1-20, 10-50, 50-100, 100-500, and 500-1,000. A range of 1 to 20 includes, for example, the numerals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20, and fractions between each integer, such as indicated above. A range of 6 to 24 includes, for example, the numerals 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, and 26, and fractions between each integer, such as indicated above.

[00017] The term “clinician” is used to reflect the trained individual that operates the biological activity recording and/or analysis process. This process has applications beyond the clinical realm and as such, their title based on their specific role. Additional examples of titles could include evaluator, interviewer, interrogator, researcher, technician, among others.

[00018] The term “subject” is used to reflect the person who is creating the biological activity data that is being recorded. Additional examples of titles could include client, participant, volunteer, applicant, interviewee, among others.

[00019] The term “biological activity” reflects any data that is recorded using sensors. Industry-standard practices currently include measuring EEG, heart rate, respirations, galvanic skin response (sweat gland activity), skin temperature, pupil dilation, among others.

[00020] A particular goal of the present disclosure is to provide a method of linking brainwave data obtained via quantitative (digital) EEG to an assessment tool (e.g., a series of questions), enabling a clinician to evaluate an individual’s EEG profile with particular assessment results. Artificial intelligence/machine learning systems are useful for analyzing information, and may assist human experts in decision making. As such, the evaluation method can be performed not only by a person, but also by artificial intelligence, specifically, by machine learning. In other words, particular embodiments can use advanced machine learning and/or artificial intelligence (Al) algorithms to assist with analysis of the human EEG data to detect the presence of biases. Artificial intelligence may be combined with the data collection using the EEG assessment tool to realize aided bias detection based on the artificial intelligence.

[00021] [0025] Broadly speaking, the present disclosure contemplates a system and method for predicting the presence of biases based on analyzing an individual’s EEG profile with particular assessment results using machine learning techniques. In one embodiment, a machine learning model for predicting the presence of biases is trained. In a broad form, a method in accordance with one embodiment trains a machine learning model to predict presence of biases using several data sets. For example, a first data set defining EEG profiles with known biases encodes the first data as a first input. A decoy data input matched to a set of data and encoding the decoy data input as a negative input. The neural network is trained using the first and decoy data inputs. The trained machine learning model may be operated by inputting one or more EEG profiles into the trained machine learning model, and outputting a classification of said input EEG profiles. In some embodiments, the classification is expressed as a probability of bias. In some embodiments, a second data defining EEG profiles with unknown biases and encoding the second data is also used as an input to train the machine learning model. [00022] In one implementation, the machine learning model is a neural network. Neural networks are a form of machine learning typically modeled to operate like a biological neural circuit by having a plurality of interconnected neurons or nodes. These nodes can be implemented using a computer system. The nodes can optionally be arranged in a plurality of layers such as input layer, output layer, and one or more hidden layers. Each node is connected to one or more other nodes in the neural network. Each node is configured to receive an input, implement a function and provide an output in accordance with the function. Additionally, each node is associated with a respective weight. Neural networks are trained with one or more data sets to minimize a cost function, which is a measure of the neural network’s performance. Typically the training algorithm uses node weights and/or bias to minimize a cost function. There are many algorithms that find the minimum of the cost function and can be used to for training a neural network. There are many types of neural networks, convolutional neural network (CNN) and artificial neural networks (ANNs) being common. Neural networks can operate as function approximation, e.g. prediction and modeling, classification, and data processing.

[00023] In one embodiment, a plurality of EEG sensors can be used to record measurements in various locations on the scalp of a subject.

[00024] The present technology, in one embodiment, employs EEG amplifiers to record human EEG data at the rate of at least 1024 samples per second and have the capability of recording at least 19 channels of active EEG. The application of the present approach allows for all amplifiers that are currently recognized to meet industry standards among the field of neurofeedback.

[00025] This technology presents a novel approach to explore the role of human EEG in detecting response bias among assessments allowing for an in-depth analysis of human EEG on both itemized and wholistic analysis of assessments. This approach to bias detection can inform practices within the fields of, for example, criminal justice, marketing, mental health professions, and aid researchers that are involved in the development of surveys or assessments that could be influenced by human biases. For example, the technology can be used by mental health professionals that are working with a client population that might be motivated to deceive psychometric assessments for perceived benefits (e.g., increased social security disability funding, child custody hearings that question parental involvement, individuals appeasing family members that have concerns about substance use, etc.).

[00026] This technology could also be beneficial to police departments that utilize polygraph assessments in criminal justice proceedings. Currently polygraphs rely on the analysis of biological sensors (skin temperature, galvanic skin response, respiration belts, etc.), which can be flawed if the person practices specific relaxation techniques. However, the sampling rate of the EEG acquisition process (e.g., 1024 per second or greater) that measures electrical communication that occurs between/within nerves, makes it much more difficult to fabricate responses.

[00027] Additional applications could be of use in athletics settings for the analysis of athletes that are suspected of acquiring a traumatic brain injury. If a player is motivated to deceive an on-field assessment in order to return to play, the present technology could be of benefit to explore the potential of deceit/bias in their perception of their injury. Additionally, the feedback acquired from this brain-based analysis of items on assessments will assist psychometricians in the development of more sound items that reduce the susceptibility of assessments to produce potentially biased results.

[00028] The present technology utilizes a programmable client screen that interfaces with a quantitative EEG (qEEG) acquisition screen (such as, but not limited to, BrainMaster’s 19-channel or 20-channel encephalograph device). The client screen should be bare in design so that clients are not distracted when taking the psychometric assessment. Each client screen will allow the clinician to pre-program a series of question-and-answer choice stems before setting up the client with the q-cap electrode cap. The number of questions will vary from assessment to assessment as will the number of answer choices.

[00029] After the client is set up with the q-cap, they will be able to start the computer- based testing that utilizes the pre -programed client assessment screen. Once the client reads the question, selects the answer that they wish, and presses the next button at the bottom of the screen, an indicium (e.g., a red line) will appear across all (e.g., 19 or 20) channels on the clinician qEEG acquisition screen. The assessment may be comprised of a series of questions, which may include from 2 to 100 questions, or more. The series of red marks on the qEEG acquisition screen will be used by the clinician/researcher to analyze brain activity that occurred within the given time period for each question on the psychometric assessment.

[00030] In a non-limiting embodiment, to assist with this analysis, the line created in the qEEG acquisition screen could be reflected as a period (.) across all EEG channels on the exported CSV/text file. It is important that the indicia (e.g., line or mark) on the exported file and qEEG acquisition screen must appear at the exact moment when the client clicks the “next” button. Furthermore, optionally, the clinician/researchers can also go back and view each of the questions and the answer choices selected by the client/participant.

[00031] The physical steps to create biological activity markings on recording feed may be performed as follows:

1) Clinician writes questions into computer software designed to present a question and possible response options to the subject via a subject screen. a. Potential questions: i) Using the below answer choices, please select the option that corresponds with your chronological age in years.

A) 0-20 B) 21^10 C) 41-60 D) 61-80 E) 81-100 ii) My biological sex is:

A) Male B) Female

Note: Options A-E become selectable answer choices for the subject.

2) The clinician applies sensors to the subject that measure the subject’s biological activity. Sensors transmit activity to an amplifier, which in turn presents the activity via computer screen. The clinician will then open a separate subject screen. See FIG. 1 for an image for illustrative purposes.

3) Clinician opens software that presents the subject with the pre-programmed set of questions (from step 1). Only one question is presented to the subject at a time.

4) Subject selects their answer choice (e.g., “A”) and then selects button that allows them to advance to the next question (next button) or indicate completion of the question set (finished button). a. When the subject selects the “next” or “finished” button that allows them to advance, a marker is instantaneously added to the biological activity recording. When exploring the data, the marker is notated (see Table 1) in the dataset by a row of nonsense syllables, nonsense characters, or otherwise determined method that differentiates the row from biological activity. The example provided in Table 1 uses the symbol of to demarcate that the subject has completed a question. This marker is seen visually on the clinician screen (see FIG. 2). i) Whenever the subject is finished with their questionnaire, the clinician will stop the recording, save the recorded data in accordance with the practices of the industry, close the subject screen, and remove all sensors from the subject. All data can be reviewed and analyzed later by the clinician or their designee.

[00032] The images provided FIG. 2 reflect quantitative EEG activity. However, this process can be applied to software that measures any form of biological activity. Table 1. Example data output table.

Added “Trigger column” as well as clear demarcation that corresponds with subject advancing to next question across all EEG recording sites.

Claims

Claims:

1. A method of detecting a response bias in an individual, the method comprising:

(a) recording a biological activity of the individual using one or more sensor;

(b) concurrently subjecting the individual to a series of questions in an assessment tool on a computer; wherein the biological activity data are automatically marked with an indicium corresponding to the time period taken to answer each question in the assessment.

2. The method of claim 1, wherein the biological activity is EEG, heart rate, respirations, galvanic skin response (sweat gland activity), skin temperature, and/or pupil dilation.

3. The method of claim 1, wherein the biological activity is brainwave data obtained via quantitative (digital) EEG.

4. The method of claim 3, wherein the quantitative EEG is performed at a rate of at least 1024 samples per second and has the capability of recording at least 19 channels of active quantitative EEG.

5. A system comprising a first console for measuring a biological activity of an individual and a second console for obtaining the individual’s answers to a series of questions in an assessment tool, wherein the system automatically marks the biological activity data with an indicium corresponding to the time period taken to answer each question in the assessment.

6. The system of claim 5, wherein the biological activity is EEG, heart rate, respirations, galvanic skin response (sweat gland activity), skin temperature, and/or pupil dilation.

7. The system of claim 5, wherein the biological activity is brainwave data obtained via quantitative (digital) EEG.

8. The system of claim 7, wherein the quantitative EEG is performed at a rate of at least 1024 samples per second and has the capability of recording at least 19 channels of active quantitative EEG.