System and method for evaluating pain expected deviation

Abstract

The invention provides a pain expected deviation evaluation system and method, and relates to the technical field of pain management and electrophysiological signal processing. An experiment normal form containing three clue types of'determined high pain ', 'determined low pain' and'uncertainty 'is established. In the aspect of subjective pain expectation deviation evaluation, the AUC of a determined high pain scene and the AUC of an uncertain scene and the AUC of a determined low pain scene and the AUC of an uncertain scene are compared respectively, and the AUC difference between the two scenes is analyzed. In the electrophysiological data analysis, based on the decoding accuracy of the signal detection theory, the decoding accuracy of the situations of'uncertainty ', 'determined high pain' and'determined low pain 'is compared, the difference is quantified and calculated, and an objective pain expected deviation index is obtained. The subjective pain expected deviation is combined with the objective electrophysiological signal for the first time, the limitation that the traditional method only depends on subjective evaluation is overcome, subjective prejudice and individual difference interference are avoided, and the comprehensiveness, reliability and repeatability of pain expected deviation evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of pain management and electrophysiological signal processing, and more particularly, to a system and method for evaluating pain expectancy bias. Background Art

[0002] Pain Expectancy Bias refers to the phenomenon that when an individual faces a possible pain situation, the anticipation of pain occurrence can lead to subjective amplification of their actual pain experience, and even exaggerate the intensity and duration of pain. This psychological phenomenon has attracted much attention in psychology and neuroscience, especially in the research fields related to chronic pain management and fear, because it has a significant impact on an individual's pain perception and behavioral responses.

[0003] In clinical practice, especially for chronic pain patients, pain expectancy bias often makes patients subjectively believe that the pain is more severe or unbearable. This exaggerated perception usually leads to patients' avoidance of treatment and daily activities, forming a vicious cycle. For example, patients may reduce their treatment compliance due to fear of pain.

[0004] This delays the recovery process and may even further exacerbate the pain symptoms. In terms of drug treatment, expectancy bias may also trigger excessive worry in patients, fearing the discomfort or pain exacerbation that the drug may bring, thus reducing the dosage or even completely avoiding treatment. In physical therapy or exposure therapy, pain expectancy bias may also make patients afraid of the appearance of pain, leading to avoidance behavior and thus affecting treatment compliance. Therefore, identifying and understanding patients' pain expectancy bias clinically can provide support for formulating more effective intervention measures, such as using means like Cognitive Behavioral Therapy (CBT) and Mindfulness Therapy, to help patients effectively cope with the anticipated pain situation and thus improve the treatment effect.

[0005] Conversely, for patients with a lower or more positive pain expectancy bias, they may have a lower sensitivity to their own pain and may even be unable to accurately identify potential illness problems. Due to their higher pain tolerance, such patients may not fully convey pain information to medical staff, affecting early diagnosis and treatment. In this case, pain loses its original role as a "danger signal" of the body, causing patients to miss the best treatment opportunity. Therefore, identifying and evaluating the pain expectancy bias of these patients in clinical practice can help doctors conduct detailed diagnoses targeted at avoiding missed diagnoses caused by patients' failure to accurately describe pain.

[0006] The traditional methods for evaluating pain anticipation bias are generally based on patients' subjective reports, such as the direct inquiry method and the scenario description method. In the direct inquiry method, medical staff directly ask patients about their anticipation of upcoming pain. This method is simple and direct, but may be affected by factors such as patients' emotions and cognitive abilities. In the scenario description method, medical staff describe to patients scenarios that may cause pain (such as surgical procedures, wound dressing changes, etc.), and then ask patients to evaluate the degree of pain they may feel in such situations. This method helps to understand patients' pain anticipation bias based on specific scenarios, but there may be a deviation between patients' imagination and the actual situation, and subjective biases and individual differences have a great interference on the evaluation results. Therefore, the traditional methods for evaluating pain anticipation bias cannot accurately evaluate patients' anticipation of pain. Especially in clinical chronic pain management, unreasonable preoperative pain anticipation often affects patients' cooperation and treatment effects. If a method with high accuracy in evaluating patients' anticipation bias can be studied and appropriate interventions can be made based on the evaluation results, it will help to improve the overall treatment effect. Summary of the Invention

[0007] To solve the problem of low accuracy of the current methods for evaluating pain anticipation bias, the present invention proposes an evaluation system and method for pain anticipation bias. First, a new experimental paradigm for pain anticipation evaluation is established. Based on the experimental paradigm, pain anticipation is evaluated, and the electrophysiological signals of the subjects are recorded during the implementation of the experimental paradigm. From the perspective of electrophysiological signal analysis and calculation, the process of pain anticipation evaluation by the experimental paradigm is further coordinated, so as to accurately and objectively evaluate an individual's pain anticipation bias, including the bias of the presence or absence and intensity of pain, providing technical support for individualized pain management and a new research and practice direction.

[0008] To achieve the above technical effects, the technical solution of the present invention is as follows:

[0009] In the first aspect, the present application proposes an evaluation system for pain anticipation bias, including:

[0010] A data acquisition module, which is used to establish an experimental paradigm for evaluating pain anticipation bias considering predictive cue stimulation conditions of "certain high pain", "certain low pain" and "uncertainty", collect the pain anticipation bias evaluation scores of the subjects based on the experimental paradigm for evaluating pain anticipation bias, and at the same time, collect the electrophysiological signal data of the subjects in the experimental paradigm for evaluating pain anticipation bias;

[0011] A subjective anticipation bias evaluation module, which conducts an experimental evaluation of pain anticipation bias based on the experimental paradigm for evaluating pain anticipation bias to obtain the subjective pain anticipation bias that the subjects tend to have in the pain uncertainty scenario;

[0012] The electrophysiological data analysis module decodes and classifies the electrophysiological signal data corresponding to the "definitely high pain" and "uncertain" predictive cue stimulus conditions and the electrophysiological signal data corresponding to the "definitely low pain" and "uncertain" predictive cue stimulus conditions respectively based on the classifier-based neural decoding algorithm, and obtains the objective pain expectation bias of the subject in the pain uncertainty scenario;

[0013] The comprehensive analysis module of subjective and objective pain expectation bias integrates the objective pain expectation bias result and the subjective pain expectation bias result from the perspective of electrophysiological signal data analysis and calculation, and cooperates with the pain expectation bias evaluation experimental paradigm to comprehensively evaluate the pain expectation bias.

[0014] Preferably, in the data acquisition module, the established pain expectation bias evaluation experimental paradigm is as follows: on the test screen, first display a cross fixation image for a duration of 1000 ms, then display a triangle fixation image on the test screen for a duration of 2000 ms. After the duration, the subject's first pain expectation score is obtained, and the scoring range is 0 to 10 points. Then, on the test screen, display a cross fixation image for a duration of 800 ms to 1000 ms. Finally, on the test screen, display an electrical stimulation with a stimulation duration of 50 ms. After the stimulation ends, the subject's second pain expectation score and unpleasantness score are obtained, and the scoring ranges are both 0 to 10 points; among them, the triangle fixation image displayed on the test screen is a predictive cue stimulus, including a "definitely high pain" predictive cue stimulus, a "definitely low pain" predictive cue stimulus, and an "uncertain" predictive cue stimulus; in the scoring ranges of the first pain expectation score and the second pain expectation score, 0 points represents no pain, 10 points represents unbearable pain, and from 0 to 10 points, the score gradually increases, representing a gradual increase in the pain sensation; in the scoring range of the unpleasantness score, 0 points represents no unpleasantness, 10 points represents the maximum unpleasantness, and from 0 to 10 points, the score gradually increases, representing a gradual increase in the unpleasantness;

[0015] Meanwhile, in the data acquisition module, the electrophysiological signal data of the subject collected includes: electroencephalogram signal data, electromyogram signal data, skin conductance signal data, and electrocardiogram signal data.

[0016] According to the above technical solution, the pain expectation bias evaluation scores of the subject are collected based on the pain expectation bias evaluation experimental paradigm, and the effectiveness of pain induction is determined through the second pain expectation score and the unpleasantness score.

[0017] Preferably, when the triangle fixation image displayed on the test screen is a "definitely high pain" predictive cue stimulus or a "definitely low pain" predictive cue stimulus, the process of determining the electrical stimulation intensity is as follows:

[0018] The subject was tested by applying electrical stimuli of ten different intensities. After each electrical stimulus of each intensity, the subject was required to give a pain anticipation score once. The scoring range was from 0 to 10 points. 0 points represented no pain, and 10 points represented unbearable pain. From 0 to 10 points, as the score gradually increased, it represented a gradual increase in the pain sensation;

[0019] The electrical stimulus intensities corresponding to 3 points and 7 points were selected from all the pain anticipation score results respectively, and used as the electrical stimulus intensity of the "determined low pain" predictive cue stimulus and the electrical stimulus intensity of the "determined high pain" predictive cue stimulus;

[0020] Suppose the pain anticipation bias assessment experimental paradigm described above was used to conduct N pain anticipation bias experiments on the subject. During the N pain anticipation bias experiments, the "determined high pain" predictive cue stimulus, the "determined low pain" predictive cue stimulus, and the "uncertain" predictive cue stimulus were used respectively. The triangular fixation images with a duration of 2000 ms were displayed on the test screen N / 3 times each. Among them, when conducting the pain anticipation bias experiments corresponding to N / 3 "uncertain" predictive cue stimuli, 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined high pain" predictive cue stimulus, and the remaining 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined low pain" predictive cue stimulus.

[0021] Preferably, in the subjective anticipation assessment module, the process of obtaining the subjective pain anticipation bias that the subject tends to have in the pain uncertainty scenario is as follows:

[0022] After the duration of each predictive cue stimulus displaying the triangular fixation image on the test screen ended, the first pain anticipation score was taken. The pain scoring scale ranged from 0 to 10 points. The pain anticipation scores under N / 3 "determined high pain", "determined low pain", and "uncertain" predictive cue stimuli were recorded respectively;

[0023] Define different levels in the pain scoring scale as the threshold Threshold i , which is an integer with a value range of 0 to 11. According to the comparison results between the pain anticipation scores under the "determined high pain" predictive cue stimulus and each threshold, and the comparison results between the pain anticipation scores under the "uncertain" predictive cue stimulus and each threshold, the hit rate under the "determined high pain" predictive cue stimulus and the false alarm rate under the "uncertain" predictive cue stimulus were obtained;

[0024] Taking the hit rate under the "determined high pain" predictive cue stimulus corresponding to each threshold as the ordinate and the false alarm rate under the "uncertain" predictive cue stimulus corresponding to each threshold as the abscissa, an ROC curve was plotted,

[0025] The trapezoidal method was used to calculate the area under the ROC curve and used it as AUC1;

[0026] According to the comparison results of the pain anticipation scores under the "certain low pain" predictive cue stimuli with each threshold, obtain the hit rate under the "certain low pain" predictive cue stimuli;

[0027] Taking the hit rate under the "certain low pain" predictive cue stimuli corresponding to each threshold as the ordinate and the false alarm rate under the "uncertain" predictive cue stimuli corresponding to each threshold as the abscissa, plot the ROC curve.

[0028] Use the trapezoidal method to calculate the area under the ROC curve and take it as AUC2;

[0029] Compare the magnitudes of AUC1 and AUC2, and based on the comparison results of AUC1 and AUC2, obtain the subjective pain anticipation bias that the subject tends to have in the pain uncertainty scenario.

[0030] Preferably, when AUC2 > AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain anticipation bias of "certain high pain"; when AUC2 < AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain anticipation bias of "certain low pain"; when AUC2 = AUC1, it indicates that in the pain uncertainty scenario, the subject has no obvious anticipation bias.

[0031] Preferably, in the electrophysiological data analysis module, first perform linear binary classification on the electrophysiological signal data of the subject recorded under different predictive cue stimulus conditions, and divide it into the first type of electrophysiological signal data and the second type of electrophysiological signal. The first type of electrophysiological signal data includes: the electrophysiological signal data corresponding to the "certain high pain" predictive cue stimulus and the electrophysiological signal data corresponding to the "uncertain" predictive cue stimulus condition. The second type of electrophysiological signal data includes: the electrophysiological signal data corresponding to the "certain low pain" predictive cue stimulus and the electrophysiological signal data corresponding to the "uncertain" predictive cue stimulus condition.

[0032] Preferably, in the electrophysiological data analysis module, based on the neural decoding algorithm of the classifier, decode and classify the first type of electrophysiological signal data. After the classifier classifies the electrophysiological signal data as the electrophysiological data under the "certain high pain" predictive cue stimulus condition, record the ratio of the classification label to the true label of the electrophysiological data as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as the electrophysiological data under the "uncertain" predictive cue stimulus condition, record the ratio of the classification label to the true label of the electrophysiological data as the false alarm rate. Taking the hit rate as the ordinate and the false alarm rate as the abscissa, plot the ROC curve, use the trapezoidal method to calculate the area under the ROC curve, and take it as AUC3;

[0033] In the electrophysiological data analysis module, a classifier-based neural decoding algorithm decodes and classifies the second type of electrophysiological signal data. After the classifier classifies the electrophysiological signal data as electrophysiological data under the "definitely low pain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as electrophysiological data under the "uncertain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the false alarm rate. Taking the hit rate as the ordinate and the false alarm rate as the abscissa, an ROC curve is plotted, and the trapezoidal method is used to calculate the area under the ROC curve, which is taken as AUC4.

[0034] According to the above technical means, it is not only possible to evaluate whether the electrophysiological signal contains information for distinguishing predictive cue stimuli, but also to link the pain expectation bias with objective electrophysiological signal indicators.

[0035] Preferably, when AUC4 > AUC3, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectation bias of "definitely high pain"; when AUC4 < AUC3, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectation bias of "definitely low pain"; when AUC4 = AUC3, it indicates that in the pain uncertainty scenario, the subject has no obvious expectation bias.

[0036] According to the above technical means, by using the multivariate patterns in the electrophysiological signal, the neural activity characteristics under different predictive cue stimulation conditions are accurately extracted, the objective indicators of pain expectation bias are quantified, and through the decoding and classification of the signals in the electrophysiological signal, a more intuitive quantitative assessment is provided.

[0037] Preferably, in the comprehensive analysis module of subjective and objective pain expectation bias, a weight index of 50% is assigned to the subjective pain expectation bias result, and a weight index of 50% is assigned to the objective pain expectation bias result;

[0038] First, a weight of 50% is given to the subjective pain expectation bias, and (AUC2 - AUC1) * 50% is calculated; then, the remaining 50% of the weight is evenly distributed to the electrophysiological signal data of each subject for the objective pain expectation bias. The electrophysiological signal data of the subject includes electroencephalogram signal data, electromyogram signal data, skin conductance signal data, and electrocardiogram signal data; finally, all the weighted indicators are integrated to obtain the overall pain expectation bias evaluation result.

[0039] According to the above technical means, it is possible to avoid the interference of subjective bias and individual differences on the evaluation result, greatly improve the reliability and repeatability of the pain expectation bias evaluation, not only improve the understanding of the subject's pain expectation, but also provide a more accurate basis for clinical treatment, and help to formulate personalized intervention measures.

[0040] In a second aspect, the present application also proposes a method for evaluating pain anticipation bias, which is implemented based on the above-mentioned evaluation system for pain anticipation bias, and includes:

[0041] S1: Establish a pain anticipation bias evaluation experimental paradigm that considers the prediction cue stimulation conditions of "definitely high pain", "definitely low pain", and "uncertain";

[0042] S2: Conduct a pain anticipation bias evaluation experiment based on the pain anticipation bias evaluation experimental paradigm to obtain the subjective pain anticipation bias that the subject tends to have in a pain-uncertain scenario;

[0043] S3: Collect the electrophysiological signal data of the subject in the pain anticipation bias evaluation experiment, and respectively decode and classify the electrophysiological signal data corresponding to the "definitely high pain" and "uncertain" prediction cue stimulation conditions and the electrophysiological signal data corresponding to the "definitely low pain" and "uncertain" prediction cue stimulation conditions based on the neural decoding algorithm of the classifier to obtain the objective pain anticipation bias of the subject in a pain-uncertain scenario;

[0044] S4: From the perspective of electrophysiological signal data analysis and calculation, in cooperation with the pain anticipation bias evaluation experimental paradigm, fuse the objective pain anticipation bias result and the subjective pain anticipation bias result to comprehensively evaluate the pain anticipation bias.

[0045] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0046] The present invention proposes an evaluation system and method for pain anticipation bias, innovatively integrating pain prediction cues and pain stimuli, establishing an experimental paradigm including three cue types: "definitely high pain", "definitely low pain", and "uncertain", and collecting subjective pain anticipation bias and objective electrophysiological data of subjects in different scenarios to comprehensively evaluate pain anticipation bias in uncertain scenarios. In terms of evaluating subjective pain anticipation bias, the area under the curve AUC is calculated through the pain anticipation bias score results, and the AUCs in the scenarios of "definitely high pain" vs. "uncertain" and "definitely low pain" vs. "uncertain" are respectively compared, and the difference in AUC between the two is analyzed. The AUC represents the probability that the subject correctly identifies the type of stimulus cue. The higher the value, the more the subject's pain anticipation is biased towards the "definitely high pain" scenario or the "definitely low pain" scenario. In the electrophysiological data analysis, based on the decoding accuracy rate of signal detection theory, the decoding accuracy rates in the "uncertain" scenario are compared with those in the "definitely high pain" scenario and the "definitely low pain" scenario, the difference is quantified and calculated, and an objective pain anticipation bias index is obtained. The present invention combines subjective pain anticipation bias with objective electrophysiological signals for the first time, overcomes the limitations of traditional methods that rely only on subjective evaluation, avoids interference from subjective biases and individual differences, significantly improves the comprehensiveness, reliability, and repeatability of pain anticipation bias evaluation, provides a new tool for clinical diagnosis and intervention, provides technical support for individualized pain management, assists medical staff in accurately predicting and controlling patients' pain anticipation responses, so as to formulate more personalized treatment plans and achieve reasonable allocation of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It shows the overall implementation process diagram of pain anticipation bias evaluation using the evaluation system for pain anticipation bias proposed in the embodiment of the present invention;

[0048] Figure 2 It shows the overall schematic diagram of the pain anticipation bias evaluation experimental paradigm proposed in the embodiment of the present invention;

[0049] Figure 3 It shows the experimental schematic diagram of the subject for pain anticipation bias evaluation proposed in the embodiment of the present invention;

[0050] Figure 4 It shows the probability density curve diagram corresponding to the positive anticipation bias of the subject under the pain anticipation bias evaluation score and the stimulation of the prediction cues of "definitely high pain", "definitely low pain", and "uncertain";

[0051] Figure 5 It shows the probability density curve diagram corresponding to the negative anticipation bias of the subject under the pain anticipation bias evaluation score and the stimulation of the prediction cues of "definitely high pain", "definitely low pain", and "uncertain";

[0052] Figure 6 Schematic diagram of ROC curves showing the positive expectations and negative expectation biases of subjects formed by the "certain low pain" and "uncertain" predictive cue stimuli, and the "certain high pain" and "uncertain" predictive cue stimuli proposed in the embodiments of the present invention;

[0053] Figure 7 Schematic diagram of the implementation process of the pain expectation bias assessment method proposed in the embodiments of the present invention; Detailed implementation manners

[0054] The drawings are only for illustrative purposes and should not be construed as limitations on this patent;

[0055] For better illustration of this embodiment, some parts of the drawings are omitted, enlarged or reduced, and do not represent the actual size;

[0056] For those skilled in the art, it is understandable that some well-known content descriptions in the drawings may be omitted.

[0057] The technical solutions of the present invention will be further described below with reference to the drawings and embodiments.

[0058] The description of the positional relationship in the drawings is only for illustrative purposes and should not be construed as limitations on this patent;

[0059] Embodiment 1

[0060] This embodiment proposes a pain expectation bias assessment system, including: a data acquisition module, a subjective expectation bias assessment module, an electrophysiological data analysis module, and a comprehensive analysis module for subjective and objective pain expectation biases.

[0061] Among them, the data acquisition module is used to establish a pain expectancy bias evaluation experimental paradigm considering predictive cue stimulus conditions of "definitely high pain", "definitely low pain", and "uncertainty", collect the pain expectancy bias evaluation scores of the subjects based on the pain expectancy bias evaluation experimental paradigm, and at the same time, collect the electrophysiological signal data of the subjects in the pain expectancy bias evaluation experiment. The subjective expectancy bias evaluation module conducts a pain expectancy bias evaluation experiment based on the pain expectancy bias evaluation experimental paradigm to obtain the subjective pain expectancy bias that the subjects tend to have in the pain uncertainty scenario. The electrophysiological data analysis module decodes and classifies the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely high pain" and "uncertainty" and the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely low pain" and "uncertainty" respectively based on the neural decoding algorithm of the classifier to obtain the objective pain expectancy bias of the subjects in the pain uncertainty scenario. The comprehensive analysis module of subjective and objective pain expectancy bias fuses the objective pain expectancy bias result and the subjective pain expectancy bias result from the perspective of electrophysiological signal data analysis calculation, in cooperation with the pain expectancy bias evaluation experimental paradigm, to comprehensively evaluate the pain expectancy bias.

[0062] In this embodiment, as Figure 1 shown in the overall implementation process diagram of pain expectancy bias evaluation using the evaluation system of pain expectancy bias, first, the subjects use a pain expectancy bias evaluation experimental paradigm constructed by the data acquisition module of the evaluation system, which considers predictive cue stimulus conditions of "definitely high pain", "definitely low pain", and "uncertainty", to conduct a pain expectancy bias evaluation experiment, collect the behavioral data of the subjects based on the pain expectancy bias evaluation experimental paradigm to obtain the pain expectancy bias evaluation scores, and at the same time, collect the electrophysiological signal data of the subjects in the pain expectancy bias evaluation experiment. In the subjective expectancy evaluation module, the subjective pain expectancy bias that the subjects tend to have in the pain uncertainty scenario is obtained. In the electrophysiological data analysis module, the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely high pain" and "uncertainty" and the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely low pain" and "uncertainty" are decoded and classified respectively based on the neural decoding algorithm of the classifier to obtain the objective pain expectancy bias of the subjects in the pain uncertainty scenario. Finally, through the comprehensive analysis module of subjective and objective pain expectancy bias, from the perspective of electrophysiological signal data analysis calculation, in cooperation with the pain expectancy bias evaluation experimental paradigm, the objective pain expectancy bias result and the subjective pain expectancy bias result are fused to comprehensively evaluate the pain expectancy bias.

[0063] Example 2

[0064] As Figure 2The experimental paradigm display diagram shown below. In this experimental paradigm, a cross fixation image is first displayed on the test screen for a duration of 1000 ms. Then, a triangular fixation image is displayed on the test screen for a duration of 2000 ms. After the expiration of the said duration, the first pain anticipation score of the subject is obtained, with the score range being from 0 to 10. Next, a cross fixation image is displayed on the test screen for a duration of 800 ms to 1000 ms. Finally, an electrical stimulation is displayed on the test screen for a stimulation duration of 50 ms. After the stimulation ends, the second pain anticipation score and the unpleasantness score of the subject are obtained, and the score ranges of both are from 0 to 10. Among them, the triangular fixation image displayed on the test screen is a predictive cue stimulus, including a "certain high pain" predictive cue stimulus, a "certain low pain" predictive cue stimulus, and an "uncertain" predictive cue stimulus. In the score ranges of the first pain anticipation score and the second pain anticipation score, 0 points represent no pain, 10 points represent unbearable pain, and from 0 to 10, the scores gradually increase, representing a gradual increase in the pain sensation. In the score range of the unpleasantness score, 0 points represent no unpleasantness, 10 points represent the maximum unpleasantness, and from 0 to 10, the scores gradually increase, representing a gradual increase in the unpleasantness.

[0065] In Figure 2 it, the cross fixation image with a duration of 1000 ms is the "orienting cross fixation", which is used at the beginning of the experiment as a visual stimulus to attract the attention of the subject, ensuring that the subject's line of sight is focused on a certain point in the center of the test screen. This helps to collect accurate electrophysiological signal data of the subject during the experiment and ensures a baseline level for subsequent electrophysiological analysis. The predictive cue stimulus with a duration of 2000 ms is presented for 2000 milliseconds, which helps to observe the electroencephalogram response related to anticipation when the subject sees the cue. After the expiration of the said duration, the first pain anticipation score of the subject is obtained. The first pain anticipation score is mainly used for the subsequent behavioral analysis of the subject to explore the subjective response of the individual to the predictive cue stimulus. The subsequent cross fixation image displayed, with a duration of 800 ms to 1000 ms in the anticipation stage, prevents the subject from adapting to the time of the stimulus appearance and also serves as a baseline in electroencephalogram analysis before the pain response. The subsequent 50 - ms electrical stimulation is a safe and effective time, and continuing to test the pain score and the unpleasantness score later further helps to understand the pain state of the subject during the entire experiment and better assist in the analysis.

[0066] In the task paradigm proposed in this embodiment, as Figure 2 shown, when the triangular fixation image displayed on the test screen is a "certain high pain" predictive cue stimulus, a "certain high pain" predictive cue, or a "certain low pain" predictive cue stimulus, the process of determining the electrical stimulation intensity is as follows:

[0067] The subjects were tested by applying electrical stimuli of ten different intensities. After each electrical stimulus of a certain intensity, the subjects were asked to give a pain anticipation score once. The score range was from 0 to 10 points. 0 points represented no pain, and 10 points represented unbearable pain. From 0 to 10 points, as the score increased gradually, it represented a gradual increase in the pain sensation. The electrical stimulus intensities corresponding to 3 points (Level 3) and 7 points (Level 7) were selected from all the pain anticipation score results, and were used as the electrical stimulus intensity of the "determined low pain" predictive cue stimulus and the electrical stimulus intensity of the "determined high pain" predictive cue stimulus respectively.

[0068] Suppose the pain anticipation bias assessment experimental paradigm described above was used to conduct N pain anticipation bias experiments on the subjects. During the N pain anticipation bias experiments, the "determined high pain" predictive cue stimulus, the "determined low pain" predictive cue stimulus, and the "uncertain" predictive cue stimulus were used respectively. A triangular fixation image with a duration of 2000 ms was displayed on the test screen N / 3 times for each type of stimulus. Among them, when conducting the pain anticipation bias experiments corresponding to N / 3 times of "uncertain" predictive cue stimuli, 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined high pain" predictive cue stimulus, and the remaining 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined low pain" predictive cue stimulus.

[0069] Specifically, in this embodiment, N was 120. 120 pain anticipation bias experiments were conducted based on the experimental paradigm proposed in this embodiment. During the 120 pain anticipation bias experiments, the "determined high pain" predictive cue stimulus, the "determined low pain" predictive cue stimulus, and the "uncertain" predictive cue stimulus were each conducted 40 times. Among them, when conducting the 40 pain anticipation bias experiments corresponding to the "uncertain" predictive cue stimuli, 50% of the "uncertain" predictive cue stimuli (i.e., 20 times) used the electrical stimulus intensity of the "determined high pain" predictive cue stimulus, and the remaining 50% of the "uncertain" predictive cue stimuli (i.e., 20 times) used the electrical stimulus intensity of the "determined low pain" predictive cue stimulus.

[0070] In this embodiment, as Figure 3 shown in the experimental schematic diagram of the pain anticipation bias assessment of the subjects, the electrophysiological signal data of the subjects were synchronously recorded as the electrophysiological signal data corresponding to the "determined high pain" predictive cue stimulus, the electrophysiological signal data corresponding to the "determined low pain" predictive cue stimulus, and the electrophysiological signal data corresponding to the "uncertain" predictive cue stimulus. The electrophysiological signal data included: electroencephalogram (EEG) signal data, electromyogram (EMG) signal data, galvanic skin response (GSR) signal data, and electrocardiogram (ECG) signal data. Specifically, the EEG signal data of the subjects were recorded by an electroencephalogram device (EEG), and their electrocardiogram (ECG) physiological signal and galvanic skin response (GSR) physiological signal were also recorded.

[0071] In the subjective expectation evaluation module, the process of obtaining the subjective pain expectation bias that the subject tends to have in the pain uncertainty scenario includes:

[0072] First, after the duration of each prediction cue stimulus showing the triangular fixation image on the test screen ends, the first pain expectation score is made. The pain rating scale ranges from 0 to 10 points. Record the pain expectation scores under the "certain high pain", "certain low pain", and "uncertain" prediction cue stimuli for N / 3 times respectively;

[0073] Secondly, define different levels in the pain rating scale as the threshold Threshold i , which takes integer values in the range of 0 to 11. According to the comparison results between the pain expectation scores under the "certain high pain" prediction cue stimulus and each threshold, and the comparison results between the pain expectation scores under the "uncertain" prediction cue stimulus and each threshold, obtain the hit rate under the "certain high pain" prediction cue stimulus and the false alarm rate under the "uncertain" prediction cue stimulus;

[0074] Then, take the hit rate under the "certain high pain" prediction cue stimulus corresponding to each threshold as the ordinate, and take the false alarm rate under the "uncertain" prediction cue stimulus corresponding to each threshold as the abscissa to draw an ROC curve. Use the trapezoidal method to calculate the area under the ROC curve and take it as AUC1;

[0075] Next, according to the comparison results between the pain expectation scores under the "certain low pain" prediction cue stimulus and each threshold, obtain the hit rate under the "certain low pain" prediction cue stimulus and the false alarm rate under the "uncertain" prediction cue stimulus;

[0076] Take the hit rate under the "certain low pain" prediction cue stimulus corresponding to each threshold as the ordinate, and take the false alarm rate under the "uncertain" prediction cue stimulus corresponding to each threshold as the abscissa to draw an ROC curve,

[0077] Use the trapezoidal method to calculate the area under the ROC curve and take it as AUC2;

[0078] Finally, compare the magnitudes of AUC1 and AUC2. According to the comparison results of AUC1 and AUC2, obtain the subjective pain expectation bias that the subject tends to have in the pain uncertainty scenario.

[0079] In this embodiment, the process of obtaining the hit rate under the "certain high pain" prediction cue stimulus or the "certain low pain" prediction cue stimulus is as follows:

[0080] S221: Let Threshold i = 0;

[0081] S222: Compare the pain expectancy scores under the N / 3 times of predictive cue stimuli with Threshold i respectively, and output the number of times the pain expectancy score under the predictive cue stimulus is greater than Threshold i Count(VAS A > Threshold i ), where the predictive cue stimulus is a "definitely high pain" predictive cue stimulus or a "definitely low pain" predictive cue stimulus;

[0082] S223: Increase the value of Threshold i by 1, and determine whether Threshold i is greater than 11. If so, end and execute S224; otherwise, return to S222;

[0083] S224: Calculate the proportion of the number of times Count(VAS A > Threshold i ) obtained from each comparison to the total number of predictive cue stimulus experiments N A to obtain the hit rate HR i under the predictive cue stimulus. The expression is:

[0084]

[0085] where VAS A represents the pain expectancy score under the predictive cue stimulus, and the predictive cue stimulus is a "definitely high pain" predictive cue stimulus or a "definitely low pain" predictive cue stimulus;

[0086] The process of obtaining the false alarm rate under the "uncertain" predictive cue stimulus is as follows:

[0087] S211: Let Threshold i = 0;

[0088] S212: Compare the pain expectancy scores under the N / 3 times of "uncertain" predictive cue stimuli with Threshold i respectively, and output the number of times the pain expectancy score under the "uncertain" predictive cue stimulus is greater than Threshold i Count(VAS B > Threshold i );

[0089] S213: Increase the value of Threshold i by 1, and determine whether Threshold i is greater than 11. If so, end and execute S214; otherwise, return to S212;

[0090] S214: Calculate the number of experiments Count(VAS B >Threshold i ) obtained from each comparison as a proportion of the total number of experiments N A of the predictive cue-stimulus experiments to obtain the false alarm rate FA i under the "uncertain" predictive cue-stimulus, with the expression:

[0091] where VAS B represents the pain expectancy score under the "uncertain" predictive cue-stimulus.

[0092] This embodiment will be described in the process of determining the hit rate under the "certain high pain" predictive cue-stimulus condition. Thresholdi i i.e., includes 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. The number of experiments is 120 times. In the 40 pain expectancy bias evaluation experiments under the "certain high pain" predictive cue-stimulus condition, 40 pain expectancy scores will be obtained. Each pain expectancy score among the 40 pain expectancy scores will be compared with each threshold between 0 and 11 respectively. That is, first compare with threshold 0 to obtain the number of "certain high pain" predictive cue-stimulus times when it is greater than 0, calculate the proportion of the number of "certain high pain" predictive cue-stimulus times obtained from the first comparison to the total number of "certain high pain" predictive cue-stimulus times, then compare with threshold 1 to obtain the number of "certain high pain" predictive cue-stimulus times when it is greater than 1, calculate the proportion of the number of "certain high pain" predictive cue-stimulus times obtained from the second comparison to the total number of "certain high pain" predictive cue-stimulus times, and so on until the comparison with threshold 11 is completed to obtain all the data of pain expectancy score proportions as the hit rate. In this embodiment, with the false alarm rate as the abscissa and the hit rate as the ordinate, the ROC curves formed by the "certain low pain" predictive cue-stimulus and the "uncertain" predictive cue-stimulus, and the ROC curves formed by the "certain high pain" predictive cue-stimulus and the "uncertain" predictive cue-stimulus are plotted, and the trapezoidal method is used to calculate the area under the ROC curve to obtain the AUC value.

[0093] When AUC2 > AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectancy bias of "certain high pain"; when AUC2 < AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectancy bias of "certain low pain"; when AUC2 = AUC1, it indicates that in the pain uncertainty scenario, the subject has no obvious expectancy bias.

[0094] In this embodiment, the pain expectation scores of "definitely high pain", "definitely low pain", and "uncertain" are used as the abscissa, and the probability distributions of the pain expectation scores of "definitely high pain", "definitely low pain", and "uncertain" are used as the ordinate, obtaining the probability density curves corresponding to "definitely high pain", "definitely low pain", and "uncertain" respectively. As shown in Figure 4 If the pain expectation of "uncertain" is horizontally closer to the evaluation score of "definitely low pain" (a < b), that is, AUC1 is larger, it means that when the subject is in "uncertain" pain, it is easier to make a subjective choice of low pain, indicating that he has a relatively positive pain bias expectation (subjectively feels low pain), that is, as shown in the probability density curve under the positive expectation bias in Figure 4 . Conversely, if the pain expectation of "uncertain" is horizontally closer to the evaluation score of "definitely high pain" (a > b), that is, AUC1 is smaller, it means that when the subject is in "uncertain" pain, it is easier to make a subjective choice of high pain, indicating that he has a relatively negative pain bias expectation (subjectively feels very painful), that is, as shown in the probability density curve under the negative expectation bias in Figure 5 .

[0095] The calculation of AUC in this embodiment is a theoretical framework of signal detection theory for analyzing and interpreting decision-making processes under uncertainty and noise conditions, which is widely used in fields such as psychology, medicine, communication, and machine learning. The core of this theory lies in understanding how individuals make judgments when faced with the presence or absence of a signal, and quantifying this process through two key indicators: hit rate and false alarm rate. The hit rate refers to the proportion of correctly identifying a signal when the signal is present, while the false alarm rate refers to the proportion of incorrectly identifying a signal as present when the signal is absent. By calculating the hit rate and false alarm rate at different decision thresholds, we can draw the receiver operating characteristic curve (ROC curve), where the abscissa of this curve represents the false alarm rate and the ordinate represents the hit rate. AUC (area under the curve), as the area under the ROC curve, provides an intuitive indicator to evaluate the overall performance of a classifier, and its value ranges from 0 to 1. The closer it is to 1, the stronger the discrimination ability of the classifier. The threshold independence of AUC makes it applicable to different application scenarios, especially in medical diagnosis to evaluate the effectiveness of tests, in psychological research to analyze perception and decision-making behaviors, and in machine learning as an important tool for evaluating classification models. In short, signal detection theory provides a systematic method for understanding and analyzing the uncertainty in decision-making processes, and AUC, as a key indicator, better quantifies the process.

[0096] In the electrophysiological data analysis module, first, linear binary classification is performed on the electrophysiological signal data of the subjects recorded under different predictive cue stimulation conditions, and it is divided into the first type of electrophysiological signal data and the second type of electrophysiological signal. The first type of electrophysiological signal data includes: the electrophysiological signal data corresponding to the "definitely high pain" predictive cue stimulation and the electrophysiological signal data corresponding to the "uncertain" predictive cue stimulation condition. The second type of electrophysiological signal data includes: the electrophysiological signal data corresponding to the "definitely low pain" predictive cue stimulation and the electrophysiological signal data corresponding to the "uncertain" predictive cue stimulation condition.

[0097] In the electrophysiological data analysis module, based on the neural decoding algorithm of the classifier, decoding classification is performed on the first type of electrophysiological signal data. After the classifier classifies the electrophysiological signal data as the electrophysiological data under the "definitely high pain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as the electrophysiological data under the "uncertain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the false alarm rate. With the hit rate as the ordinate and the false alarm rate as the abscissa, an ROC curve is plotted, and the trapezoidal method is used to calculate the area under the ROC curve, which is taken as AUC3. The specific process is as follows:

[0098] The process is as follows:

[0099] S310: Preprocess all the first type of electrophysiological signal data;

[0100] S311: Divide the preprocessed electrophysiological signal data into a training set and a test set, and mark the true labels for the training set and the test set;

[0101] S312: Use the training set to train the classifier. During the training process, the classifier classifies the electrophysiological signal data into different categories according to the characteristics of different electrophysiological signal data, and uses k-fold cross-validation to evaluate the performance of the classifier;

[0102] S313: Apply the trained classifier to the test set for testing to obtain the classification probabilities of different electrophysiological signal data;

[0103] S314: Sort all the classification probabilities from high to low to generate a probability set, and use each probability in the probability set as each threshold;

[0104] S315: If the classification probability of being classified as "definitely high pain" is greater than each threshold value, the classifier classifies the electrophysiological signal data as electrophysiological data under the "definitely high pain" predictive cue stimulation condition, and records the ratio of the classification label to the true label of the electrophysiological data as the hit rate; otherwise, the classifier classifies the electrophysiological signal data as electrophysiological data under the "uncertain" predictive cue stimulation condition, and records the ratio of the classification label to the true label of the electrophysiological data as the false alarm rate;

[0105] S316: Pair all the hit rates and all the false alarm rates according to the traversed threshold order. Use the hit rate under the "definitely high pain" predictive cue stimulation corresponding to each threshold value as the ordinate, and use the false alarm rate under the "uncertain" predictive cue stimulation corresponding to each threshold value as the abscissa to draw an ROC curve, and calculate the area under the ROC curve using the trapezoidal method, and take it as AUC3.

[0106] In the electrophysiological data analysis module, based on the neural decoding algorithm of the classifier, the second type of electrophysiological signal data is decoded and classified. After the classifier classifies the electrophysiological signal data as electrophysiological data under the "definitely low pain" predictive cue stimulation condition, record the ratio of the classification label to the true label of the electrophysiological data as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as electrophysiological data under the "uncertain" predictive cue stimulation condition, record the ratio of the classification label to the true label of the electrophysiological data as the false alarm rate. Use the hit rate as the ordinate and the false alarm rate as the abscissa to draw an ROC curve, and calculate the area under the ROC curve using the trapezoidal method, and take it as AUC4. The specific process is as follows:

[0107] S320: Preprocess all the second type of electrophysiological signal data;

[0108] S321: Divide the preprocessed electrophysiological signal data into a training set and a test set, and mark the true labels of the training set and the test set;

[0109] S322: Use the training set to train the classifier. During the training process, the classifier classifies the electrophysiological signal data into different categories according to the characteristics of different electrophysiological signal data, and uses k-fold cross-validation to evaluate the performance of the classifier;

[0110] S323: Apply the trained classifier to the test set for testing to obtain the classification probabilities of different electrophysiological signal data;

[0111] S324: Sort all the classification probabilities from high to low to generate a probability set, and use each probability in the probability set as each threshold value;

[0112] S325: If the classification probability of being classified as "definitely low pain" is greater than each threshold value, the classifier classifies the electrophysiological signal data as electrophysiological data under the "definitely low pain" prediction cue stimulus condition, and records the ratio of the classification label to the true label of the electrophysiological data as the hit rate; otherwise, the classifier classifies the electrophysiological signal data as electrophysiological data under the "uncertain" prediction cue stimulus condition, and records the ratio of the classification label to the true label of the electrophysiological data as the false alarm rate.

[0113] S326: Pair all the hit rates and all the false alarm rates according to the traversed threshold order. Take the hit rate under the "definitely low pain" prediction cue corresponding to each threshold value as the ordinate, and take the false alarm rate under the "uncertain" prediction cue corresponding to each threshold value as the abscissa to draw an ROC curve, and use the trapezoidal method to calculate the area under the ROC curve, and take it as AUC4.

[0114] The neural decoding algorithm mentioned in this embodiment is based on neural decoding of multi-voxel pattern analysis (MVPA), which is a method of using multi-variable activity patterns in brain imaging data (such as fMRI, EEG or MEG) to identify or predict specific conditions, stimuli or cognitive states. Compared with traditional univariate analysis, MVPA can more sensitively detect subtle differences between conditions by capturing the co-activity between multiple voxels or channels. In this study, we used MVPA to analyze the neural decoding performance of different experimental conditions (such as "uncertain low pain" and "definitely low pain", "uncertain high pain" and "definitely high pain"), and calculated the classification performance (AUC) of each pair of conditions. Specifically, first, preprocess the electrophysiological signal data, including noise reduction, normalization and segmentation, to ensure the quality and consistency of the data. Subsequently, extract multi-variable patterns from the data as features, and correspond them to the target conditions A and B (such as "uncertain" and "definitely high pain" conditions, or "uncertain" and "definitely low pain" conditions). To prevent overfitting, we divide the data into a training set and a test set, and use cross-validation (such as k-fold cross-validation) to enhance the reliability of the results. Then, the classifier (such as support vector machine or logistic regression) learns the specific pattern differences between conditions A and B on the training set, and applies the trained classifier to the test set to generate the classification probability or decision value of each sample. By calculating the matching degree between the predicted probability and the true label, a receiver operating characteristic curve (ROC curve) can be drawn, and the classification performance is quantified by the area under the curve (AUC).

[0115] The AUC value ranges from 0.5 (completely indistinguishable) to 1.0 (perfectly distinguishable), reflecting the discrimination between Condition A and Condition B in the neural activity pattern. By using the multivariate patterns in the electrophysiological signals, the neural activity characteristics under different predictive cue stimuli are accurately extracted, quantifying the objective index of pain expectancy bias. Through the decoding and classification of the signals in the electrophysiological signals, a more intuitive quantitative assessment is provided.

[0116] When AUC2 > AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectancy bias of "definitely high pain"; when AUC2 < AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectancy bias of "definitely low pain"; when AUC2 = AUC1, it indicates that in the pain uncertainty scenario, the subject has no obvious expectancy bias.

[0117] In this embodiment, the ROC curves formed by the "definitely low pain" predictive cue stimulus and the "uncertain" predictive cue stimulus, as well as the ROC curves formed by the "definitely high pain" predictive cue stimulus and the "uncertain" predictive cue stimulus are plotted as Figure 6 shown. In Figure 6 it, the subject is more likely to make a subjective choice of low pain under uncertainty, indicating that the subject has a relatively positive expected pain bias. The subject is more likely to make a subjective choice of high pain under uncertainty, indicating that the subject has a relatively negative expected pain bias.

[0118] In this embodiment, the process of comprehensively evaluating the pain expectancy bias by fusing the objective pain expectancy bias result and the subjective pain expectancy bias result is as follows: Assign a weight index of 50% to the subjective pain expectancy bias result and a weight index of 50% to the objective pain expectancy bias result. The electrophysiological signal data of the subject includes electroencephalogram signal data, electromyogram signal data, skin conductance signal data, and electrocardiogram signal data.

[0119] First, assign 50% weight to the subjective pain expectancy bias and calculate (AUC2 - AUC1) * 50%; then, evenly distribute the remaining 50% weight to each electrophysiological signal data of the objective pain expectancy bias of the subject; finally, integrate all the weighted indicators to obtain the overall pain expectancy bias evaluation result.

[0120] Embodiment 3

[0121] As Figure 7 shown, this embodiment proposes an evaluation method for pain expectancy bias. This method is implemented based on the system described in the foregoing embodiment. The implementation process of this method is shown in Figure 7 and includes the following steps:

[0122] S1: Establish an experimental paradigm for evaluating pain expectancy bias that considers predictive cue stimulus conditions of "definitely high pain", "definitely low pain", and "uncertainty".

[0123] S2: Conduct a pain expectancy bias evaluation experiment based on the experimental paradigm for evaluating pain expectancy bias to obtain the subjective pain expectancy bias that the subject tends to have in a pain uncertainty scenario.

[0124] S3: Collect the electrophysiological signal data of the subject in the pain expectancy bias evaluation experiment, and use a neural decoding algorithm based on a classifier to decode and classify the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely high pain", "uncertainty", and the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely low pain", "uncertainty" respectively, to obtain the objective pain expectancy bias of the subject in a pain uncertainty scenario.

[0125] S4: From the perspective of electrophysiological signal data analysis and calculation, in cooperation with the experimental paradigm for evaluating pain expectancy bias, fuse the objective pain expectancy bias result and the subjective pain expectancy bias result to comprehensively evaluate the pain expectancy bias.

[0126] In the method proposed in this embodiment, first establish an experimental paradigm for pain expectancy evaluation that considers predictive cues of "definitely high pain", "definitely low pain", and "uncertainty". Conduct a pain expectancy evaluation experiment based on this experimental paradigm to obtain the subjective pain expectancy bias that the subject tends to have in a pain uncertainty scenario. In the pain expectancy evaluation experiment, simultaneously collect the electrophysiological signal data of the subject, and then use a neural decoding algorithm based on a classifier to decode and classify the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely high pain", "uncertainty" and the electrophysiological signal data corresponding to the predictive cue stimulus conditions of "definitely low pain", "uncertainty" respectively, to obtain the objective pain expectancy bias of the subject. Further cooperate with the experimental paradigm to evaluate the pain expectancy from the perspective of electrophysiological signal analysis and calculation, combine the objective pain expectancy bias result and the subjective pain expectancy bias result, and comprehensively evaluate the pain expectancy bias. The present invention breaks through the limitation of the traditional single reliance on subjective reports to evaluate expectancy bias, and for the first time combines the subjective bias of the subject with peripheral and central electrophysiological signals, providing a more comprehensive and objective method for evaluating expectancy bias, which can avoid the interference of subjective biases and individual differences on the evaluation results, and greatly improve the reliability and repeatability of the evaluation. It provides a new tool for clinical diagnosis and intervention, provides technical support for individualized pain management, assists medical staff in accurately predicting and controlling the pain expectancy response of patients, so as to formulate more personalized treatment plans and achieve the rational allocation of medical resources.

[0127] The embodiments are merely examples given to clearly illustrate the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or alterations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. A pain anticipation bias assessment system, characterized in that: include: The data collection module is used to establish a pain anticipation bias assessment experimental paradigm that takes into account "certain high pain", "certain low pain" and "uncertain" predictive cue stimulation conditions, collect the subjects' pain anticipation bias assessment scores based on the pain anticipation bias assessment experimental paradigm, and collect the subjects' electrophysiological signal data in the pain anticipation bias assessment experiment; The subjective expectation bias assessment module conducts a pain expectation bias assessment experiment based on the pain expectation bias assessment experimental paradigm to obtain the subjective pain expectation bias that the subjects tend to have in the pain uncertainty scenario; The electrophysiological data analysis module uses a neural decoding algorithm based on a classifier to decode and classify the electrophysiological signal data corresponding to the "certain high pain" and "uncertain" prediction clue stimulation conditions and the electrophysiological signal data corresponding to the "certain low pain" and "uncertain" prediction clue stimulation conditions, respectively, to obtain the subject's objective pain expectation bias in the pain uncertainty scenario; The comprehensive analysis module of subjective and objective pain anticipation bias, from the perspective of electrophysiological signal data analysis and calculation, cooperates with the pain anticipation bias evaluation experimental paradigm, integrates the objective pain anticipation bias results with the subjective pain anticipation bias results, and conducts a comprehensive evaluation of the pain anticipation bias.

2. The pain anticipation bias assessment system according to claim 1, characterized in that: In the data acquisition module, the pain anticipation bias assessment experimental paradigm established is: on the test screen, first a cross fixation image is displayed for 1000ms, then a triangle fixation image is displayed for 2000ms, after the duration, the subject's first pain anticipation score is made, the score range is 0 to 10 points, then a cross fixation image is displayed for 800ms to 1000ms, finally, electrical stimulation is displayed for 50ms, after the stimulation, the subject's second pain anticipation score and unpleasantness score are made. The score ranges from 0 to 10 points; wherein the triangular fixation image displayed on the test screen is a prediction cue stimulus, including a "confirmed high pain" prediction cue stimulus, a "confirmed low pain" prediction cue stimulus, and an "uncertain" prediction cue stimulus; in the scoring range of the first pain expectation score and the second pain expectation score, 0 points represent no pain, 10 points represent unbearable pain, and from 0 to 10 points, the score gradually increases, representing a gradually increasing pain sensation; in the scoring range of the unpleasantness score, 0 points represent no unpleasantness, 10 points represent the maximum unpleasantness, and from 0 to 10 points, the score gradually increases, representing a gradually increasing unpleasantness; At the same time, in the data acquisition module, the collected electrophysiological signal data of the subject include: electroencephalogram signal data, electromyography signal data, skin electricity signal data and electrocardiogram signal data.

3. The pain anticipation bias assessment system according to claim 2, characterized in that: When the triangular fixation image displayed on the test screen is a "high pain confirmed" prediction cue stimulus or a "low pain confirmed" prediction cue stimulus, the process of determining the intensity of the electrical stimulation is as follows: The subject was tested by applying electrical stimuli of ten different intensities. After each electrical stimulus of a certain intensity, the subject was asked to give a pain anticipation score once. The score range was from 0 to 10 points. 0 points represented no pain, and 10 points represented unbearable pain. From 0 to 10 points, as the score increased, it represented an increasing degree of pain. The electrical stimulus intensities corresponding to 3 points and 7 points were selected from all the pain anticipation score results, and were used as the electrical stimulus intensities of the "determined low pain" predictive cue stimulus and the "determined high pain" predictive cue stimulus respectively. Suppose the subject was given N pain anticipation bias experiments using the pain anticipation bias assessment experimental paradigm described above. During the N pain anticipation bias experiments, the "determined high pain" predictive cue stimulus, the "determined low pain" predictive cue stimulus, and the "uncertain" predictive cue stimulus were used respectively. The triangular fixation images with a duration of 2000 ms were displayed on the test screen N / 3 times for each of them. Among them, during the N / 3 pain anticipation bias experiments corresponding to the "uncertain" predictive cue stimulus, 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined high pain" predictive cue stimulus, and the remaining 50% of the "uncertain" predictive cue stimuli used the electrical stimulus intensity of the "determined low pain" predictive cue stimulus.

4. The pain anticipation bias assessment system according to claim 3, characterized in that: In the subjective anticipation assessment module, the process of obtaining the subjective pain anticipation bias that the subject tends to have in the pain uncertainty scenario is as follows: After the duration of the triangular fixation image displayed on the test screen for each predictive cue stimulus ended, the first pain anticipation score was made. The pain score scale range was from 0 to 10 points. The pain anticipation scores under the N / 3 "determined high pain", "determined low pain", and "uncertain" predictive cue stimuli were recorded respectively. Different levels in the pain rating scale are defined as thresholds i , with a value range of 0 to 11, based on the comparison results of the pain expectation score under the "definitely high pain" prediction cue stimulation and each threshold, and the comparison results of the pain expectation score under the "uncertain" prediction cue stimulation and each threshold, the hit rate under the "definitely high pain" prediction cue stimulation and the false alarm rate under the "uncertain" prediction cue stimulation are obtained; Taking the hit rate under the "determined high pain" predictive cue stimulus corresponding to each threshold as the ordinate, and the false alarm rate under the "uncertain" predictive cue stimulus corresponding to each threshold as the abscissa, an ROC curve was plotted, and the area under the ROC curve was calculated using the trapezoidal method, which was taken as AUC1. According to the comparison results of the pain anticipation scores under the "determined low pain" predictive cue stimulus and each threshold, the hit rate under the "determined low pain" predictive cue stimulus was obtained. Taking the hit rate under the "determined low pain" predictive cue stimulus corresponding to each threshold as the ordinate, and the false alarm rate under the "uncertain" predictive cue stimulus corresponding to each threshold as the abscissa, an ROC curve was plotted, and the area under the ROC curve was calculated using the trapezoidal method, which was taken as AUC2. Compare the magnitudes of AUC1 and AUC2. According to the comparison results of AUC1 and AUC2, the subjective pain anticipation bias that the subject tends to have in the pain uncertainty scenario was obtained.

5. The pain anticipation bias assessment system according to claim 4, characterized in that: When AUC2 > AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain anticipation bias of "determined high pain"; when AUC2 < AUC1, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain anticipation bias of "determined low pain"; when AUC2 = AUC1, it indicates that in the pain uncertainty scenario, the subject has no obvious anticipation bias.

6. The pain anticipation bias assessment system according to claim 1, characterized in that: In the electrophysiological data analysis module, first, linear binary classification is performed on the electrophysiological signal data of the subjects recorded under different predictive cue stimulation conditions, and it is divided into the first type of electrophysiological signal data and the second type of electrophysiological signal. The first type of electrophysiological signal data includes: electrophysiological signal data corresponding to the "definitely high pain" predictive cue stimulation and electrophysiological signal data corresponding to the "uncertain" predictive cue stimulation condition. The second type of electrophysiological signal data includes: electrophysiological signal data corresponding to the "definitely low pain" predictive cue stimulation and electrophysiological signal data corresponding to the "uncertain" predictive cue stimulation condition.

7. The pain anticipation bias assessment system according to claim 6, characterized in that: In the electrophysiological data analysis module, decoding classification is performed on the first type of electrophysiological signal data based on the neural decoding algorithm of the classifier. After the classifier classifies the electrophysiological signal data as the electrophysiological data under the "definitely high pain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as the electrophysiological data under the "uncertain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the false alarm rate. With the hit rate as the ordinate and the false alarm rate as the abscissa, an ROC curve is plotted, and the trapezoidal method is used to calculate the area under the ROC curve, which is taken as AUC3. In the electrophysiological data analysis module, decoding classification is performed on the second type of electrophysiological signal data based on the neural decoding algorithm of the classifier. After the classifier classifies the electrophysiological signal data as the electrophysiological data under the "definitely low pain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the hit rate; otherwise, after the classifier classifies the electrophysiological signal data as the electrophysiological data under the "uncertain" predictive cue stimulation condition, the ratio of the classification label to the true label of the electrophysiological data is recorded as the false alarm rate. With the hit rate as the ordinate and the false alarm rate as the abscissa, an ROC curve is plotted, and the trapezoidal method is used to calculate the area under the ROC curve, which is taken as AUC4.

8. The pain anticipation bias assessment system according to claim 7, characterized in that: When AUC4 > AUC3, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectation bias of "definitely high pain"; when AUC4 < AUC3, it indicates that in the pain uncertainty scenario, the subject tends to have a subjective pain expectation bias of "definitely low pain"; when AUC4 = AUC3, it indicates that in the pain uncertainty scenario, the subject has no obvious expectation bias.

9. The pain anticipation bias assessment system according to claim 1, characterized in that: In the comprehensive analysis module of subjective and objective pain expectation bias, a 50% weight index is assigned to the result of subjective pain expectation bias, and a 50% weight index is assigned to the result of objective pain expectation bias. First, assign 50% weight to the subjective pain expectation bias and calculate (AUC2 - AUC1) * 50%; then, evenly distribute the remaining 50% weight to the electrophysiological signal data of each subject for objective pain expectation bias. The electrophysiological signal data of the subject includes electroencephalogram signal data, electromyogram signal data, skin conductance signal data, and electrocardiogram signal data; finally, integrate all the weighted indicators to obtain the overall pain expectation bias evaluation result.

10. A method for evaluating pain anticipation bias, characterized in that: The method is implemented based on the pain anticipation bias evaluation system of claim 1. The following steps are involved: S1: Establish a pain anticipation bias assessment experimental paradigm that considers "certain high pain", "certain low pain" and "uncertain" predictive cue stimulation conditions; S2: Conduct a pain anticipation bias assessment experiment based on the pain anticipation bias assessment experimental paradigm to obtain the subjective pain anticipation bias that the subjects tend to have in pain uncertainty situations; S3: In the pain anticipation bias assessment experiment, the subjects' electrophysiological signal data were collected. The classifier-based neural decoding algorithm decoded and classified the electrophysiological signal data corresponding to the "certain high pain" and "uncertain" prediction cue stimulation conditions and the electrophysiological signal data corresponding to the "certain low pain" and "uncertain" prediction cue stimulation conditions, respectively, to obtain the subjects' objective pain anticipation bias in the pain uncertainty scenario; S4: From the perspective of electrophysiological signal data analysis and calculation, combined with the pain anticipation bias assessment experimental paradigm, the objective pain anticipation bias results are integrated with the subjective pain anticipation bias results to conduct a comprehensive assessment of the pain anticipation bias.

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