CN117322891A - Measurement method for human perception sensitivity and decision responsiveness - Google Patents
Measurement method for human perception sensitivity and decision responsiveness Download PDFInfo
- Publication number
- CN117322891A CN117322891A CN202311305724.2A CN202311305724A CN117322891A CN 117322891 A CN117322891 A CN 117322891A CN 202311305724 A CN202311305724 A CN 202311305724A CN 117322891 A CN117322891 A CN 117322891A
- Authority
- CN
- China
- Prior art keywords
- data
- decision
- responsiveness
- electroencephalogram
- sensitivity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000004043 responsiveness Effects 0.000 title claims abstract description 52
- 230000035945 sensitivity Effects 0.000 title claims abstract description 40
- 230000008447 perception Effects 0.000 title claims abstract description 37
- 238000000691 measurement method Methods 0.000 title description 2
- 238000004519 manufacturing process Methods 0.000 claims abstract description 55
- 238000000034 method Methods 0.000 claims abstract description 41
- 230000002159 abnormal effect Effects 0.000 claims abstract description 32
- 210000004556 brain Anatomy 0.000 claims abstract description 28
- 238000001914 filtration Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 14
- 230000005611 electricity Effects 0.000 claims abstract description 9
- 238000004070 electrodeposition Methods 0.000 claims description 29
- 230000008859 change Effects 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 16
- 230000008825 perceptual sensitivity Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000003321 amplification Effects 0.000 claims description 4
- 238000012880 independent component analysis Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000006872 improvement Effects 0.000 abstract description 5
- 238000005259 measurement Methods 0.000 description 15
- 238000010586 diagram Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 239000000306 component Substances 0.000 description 3
- 230000006998 cognitive state Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 241000282412 Homo Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003930 cognitive ability Effects 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/6803—Head-worn items, e.g. helmets, masks, headphones or goggles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/20—Workers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Pathology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Psychology (AREA)
- Mathematical Physics (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses a method for measuring human perception sensitivity and decision responsiveness, which is characterized in that an electroencephalogram electrode is arranged on a safety working cap, and the safety working cap is worn on the head of a production operator to be measured; acquiring brain electricity data of operators in front of a screen; filtering and segmenting the electroencephalogram data and performing anti-fake analysis; then, carrying out connectivity analysis on the electrode to obtain a connectivity index, and finally, respectively setting judgment standards of perception sensitivity and decision responsiveness according to the obtained connectivity index, wherein if the judgment standards are met, the perception sensitivity and the decision responsiveness are determined to be good, otherwise, the perception sensitivity and the decision responsiveness are determined to be not good; the method has the advantages that the perception sensitivity and decision responsiveness level of production operators to abnormal data in the production process can be objectively evaluated, and the method has the advantages of non-invasiveness, real-time performance, objectivity, digital reference basis, decision responsiveness improvement and the like, and brings important innovation and application values to the fields of machine cooperation and intelligent manufacturing.
Description
Technical Field
The invention relates to the technical field of brain electrical nerve function measurement, in particular to a method for measuring human body perception sensitivity and decision responsiveness, which is used for measuring and judging the perception sensitivity and decision responsiveness of a measured person facing abnormal data.
Background
Digital economics are becoming increasingly important for intelligent manufacturing as one of the major economic modalities today. Intelligent manufacturing is a core component of digital economy and relies on digital technology support to achieve efficient, accurate and flexible production. However, in current man-machine hybrid manufacturing systems, digitization of machine equipment is relatively mature, and digitization of critical states of humans, including perceptual sensitivity and decision responsiveness, remains a challenge.
In smart manufacturing production systems, the perceived sensitivity of workers to abnormal data and conditions and absolute response are critical. In the intelligent construction of the man-machine hybrid manufacturing system at the present stage, related data of objects and people are needed, namely, except that the manufacturing equipment needs to realize digitization, the digitization perception of cognitive states (such as perception sensitivity and decision responsiveness) of production operators in the production process also becomes a core requirement, but the perception sensitivity and decision responsiveness of the production operators in the face of abnormal production data cannot be accurately measured at present, namely, the digitization of cognitive data of the people cannot be realized in the intelligent manufacturing. The lack of accurate measurement and assessment of human perceptual sensitivity and decision responsiveness limits further optimization of the system. Current assessment often relies on subjective experience and intuition, with subjective and uncertain problems.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for measuring human perception sensitivity and decision responsiveness, which can objectively evaluate the perception sensitivity and decision responsiveness level of staff to abnormal data in the production process and provide scientific basis for optimizing and improving a human-machine hybrid manufacturing system.
The technical scheme adopted for solving the technical problems is as follows: a method for measuring human perception sensitivity and decision responsiveness comprises the following specific steps:
(1) Selecting 32 electrode positions according to the electroencephalogram electrode layout of the international 10-10 standard system, installing electroencephalogram electrodes at the corresponding positions of the safety working cap, and wearing the safety working cap on the head of a production operator to be tested;
(2) Monitoring production operation data by a production operator to be tested in front of a master control screen;
(3) The brain electrical electrode collects brain electrical signals of the production operator to be tested;
(4) The data processing system receives the brain electricity data measured by the brain electricity electrode in real time in a wireless mode;
(5) When the production operation data in the master control screen is abnormal, an event trigger is generated at the same time and transmitted to the data processing system, and the event trigger is used as an event mark (mark) signal to be recorded in the electroencephalogram data;
(6) Every 30 minutes, the data processing system automatically processes the brain electrical data once: the method comprises the steps of signal amplification, signal noise reduction, artifact removal, high-pass filtering, low-pass filtering and analysis section interception, so as to obtain electroencephalogram data in two states of abnormal data change and unchanged data by taking an event mark as a reference;
(7) Performing short-time Fourier transform on the processed electroencephalogram data, calculating a wavelet function of the electroencephalogram data for time-frequency analysis, and convolving the wavelet function with a Fourier transform result to obtain time-frequency representation, so that time-frequency energy data of the electroencephalogram data is calculated;
(8) Connectivity analysis is carried out among the 32 selected electrodes, and the connectivity index between any two electrodes is calculated by using an HERMES software system and based on time-frequency energy data: pearson correlation coefficients;
(9) And respectively setting judgment standards of the perception sensitivity and the decision responsiveness according to the obtained Pearson correlation coefficient, if the judgment standards are met, the perception sensitivity and the decision responsiveness are determined to be good, and otherwise, the perception sensitivity and the decision responsiveness are determined to be not good.
Further, in the step (1), when the sensitivity is measured, the 32 electrode positions are selected to be F1, FZ, F2, F4, F6, F8, FC1, FCZ, FC2, FC4, FC6, FT8, C1, CZ, C2, C4, C6, CP1, CPZ, CP2, CP4, CP6, P1, PZ, P2, P4, P6, PO3, POZ, PO4, PO6, PO8;
when measuring decision responsiveness, the 32 electrode positions selected are F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, TP7, CP5, CP3, P7, P5, P3.
Further, in the step (6), the filtering and analyzing section intercepting method for the electroencephalogram data comprises the following steps: processing by adopting an Event Related Potential (ERP) mode, and performing high-pass filtering and low-pass filtering on the electroencephalogram data at 0.1hz and 40 hz; then, taking an event mark as a reference, intercepting an electroencephalogram signal of-200 ms to 1000ms as electroencephalogram data in a data abnormal change state; and intercepting the brain electrical signal of-1400 ms to-200 ms by taking the same event mark as a reference, and taking the brain electrical signal as brain electrical data under the state that the data is unchanged if other events mark are not present in the time period.
In the step (6), the artifact removal of the electroencephalogram data adopts independent component analysis to remove the data of interference and artifact components.
Further, in the step (9), the judgment criterion of the perception sensitivity is:
condition one: between any two electrodes at the selected 32 electrode positions, the Pearson correlation coefficient of the abnormal change state of the data in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition I is larger than 15;
condition II: meanwhile, the Pearson correlation coefficient of the unchanged state of the total control screen data between any two electrodes at the selected 32 electrode positions is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the abnormal change state of the total control screen data, and the number of electrode pairs meeting the second condition is smaller than 3;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, the perception sensitivity is judged to be good, otherwise, the perception sensitivity is judged to be not good.
Further, in the step (9), the decision responsiveness criterion is: dividing F7, F5, F3, FT7, FC5, FC3, T7, C5, C3, TP7, CP5, CP3, P7, P5, P3 of the 32 selected electrode positions into a first region, dividing F3, F1, FZ, F2, F4, F6, F8, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, C3, C1, CZ, C2, C4, C6 of the 32 selected electrode positions into a second region,
condition one: between any two electrodes in the first area, the Pearson correlation coefficient of the data unchanged state in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the data abnormal changed state of the total control screen, and the number of the electrode pairs meeting the condition I is larger than 5; meanwhile, the number of the electrode pairs meeting the first condition in the second area is smaller than 2;
condition II: between any two electrodes in the first area, the Pearson correlation coefficient in the abnormal change state of the data of the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient in the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition II is smaller than 2; meanwhile, the number of the electrode pairs meeting the second condition in the second area is more than 10;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, determining that the decision response is good, otherwise, determining that the decision response is not good.
Further, in the step (8), the calculation relation of the Pearson correlation coefficient r is:
，
wherein: x and Y represent observations of two electrodes over a period of time, respectively, and X ̄ and Ȳ represent the average of observations of the corresponding electrodes over the same period of time, respectively.
Further, when the decision responsiveness of the production operator to be tested is determined to be not good, an alarm prompt is sent when the index of the production operation data in the master control screen is close to 95% of the abnormal threshold value, so that the decision responsiveness of the production operator is improved.
Further, the alarm reminding mode comprises the following steps: displaying a picture of the occurrence of the potential fault corresponding to the index in an additional display screen, and displaying four word patterns of potential; simultaneously, a fault sound corresponding to the index is emitted.
Compared with the prior art, the invention has the advantages that:
(1) Non-invasive measurement: the method adopts a non-invasive mode to measure, does not need to make any body contact or wear special equipment on the measured personnel, so that the measurement process is more convenient and comfortable, and the interference and pressure on the measured personnel are reduced.
(2) Real-time measurement: the method can measure the perception sensitivity and decision responsiveness of the tested personnel in real time, provides instant feedback and evaluation, is very valuable for timely adjustment and intervention in the production process, and is beneficial to improving the production efficiency and quality.
(3) Objectivity measurement: the method adopts objective indexes and algorithms for measurement, does not depend on subjective evaluation or personal advocacy, eliminates the influence of subjective bias and individual difference on measurement results, and provides reliable and consistent measurement data.
(4) Digital reference basis: the method provides a digital reference basis for the cognitive state of production operators. By accurately measuring the perception sensitivity and the decision responsiveness, important data support can be provided for the intelligent construction of the man-machine hybrid manufacturing system, and the improvement of the cognitive ability and the improvement of the working efficiency of operators are promoted.
(5) And (3) improving decision responsiveness: when the decision responsiveness of the production operator to be tested is determined to be not good, the method provides a neural synchronous induction mode, and the decision responsiveness is improved by prompting an alarm through sound and images. The method can timely draw attention and prompt operators to make quicker and more accurate decisions, and improves the working efficiency and the safety of production critical occasions.
In conclusion, the method has the advantages of non-invasiveness, real-time performance, objectivity, digital reference basis, decision responsiveness improvement and the like, and brings important innovation and application value to the fields of machine cooperation and intelligent manufacturing.
Drawings
FIG. 1 is a diagram showing a selected electroencephalogram position distribution diagram according to an embodiment of the present invention;
FIG. 2 is a diagram showing a distribution diagram of electroencephalogram electrode positions according to a second embodiment of the present invention;
FIG. 3 is a graph showing the result of determining the first condition after the human body sensing sensitivity measurement is performed on the person under test;
FIG. 4 is a graph showing the result of determining the second condition after the human body sensing sensitivity measurement is performed on the person under test;
FIG. 5 is a graph showing the result of determining the first condition after the decision-responsive measurement of the person under test;
fig. 6 shows a result of determining the second condition after the decision responsiveness measurement is performed on the person under test.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
Embodiment one: as shown in the figure, the method for measuring the human perception sensitivity comprises the following specific steps:
(1) The method comprises the steps of selecting 32 electrode positions according to the electroencephalogram electrode layout of an international 10-10 standard system, installing electroencephalogram electrodes at corresponding positions of a safety working cap, and wearing the safety working cap on the head of a production worker to be tested, wherein the selected 32 electrode positions are F1, FZ, F2, F4, F6, F8, FC1, FCZ, FC2, FC4, FC6, FT8, C1, CZ, C2, C4, C6, CP1, CPZ, CP2, CP4, CP6, P1, PZ, P2, P4, P6, PO3, POZ, PO4, PO6 and PO8;
(2) Monitoring production operation data by a production operator to be tested in front of a master control screen;
(3) The brain electrical electrode collects brain electrical signals of the production operator to be tested;
(4) The data processing system receives the brain electricity data measured by the brain electricity electrode in real time in a wireless mode such as Bluetooth, WIFI and the like;
(5) When the production operation data in the master control screen is abnormal (namely, the index exceeds the threshold), an event trigger is generated at the same time and transmitted to the data processing system to be used as an event mark signal to be recorded in the electroencephalogram data;
(6) Every 30 minutes, the data processing system automatically processes the brain electrical data once: the method comprises the steps of signal amplification, signal noise reduction, artifact removal, high-pass filtering, low-pass filtering and analysis section interception, so as to obtain electroencephalogram data in two states of abnormal data change and unchanged data by taking an event mark as a reference; wherein:
the specific method for filtering, analyzing segment interception and artifact removal of the electroencephalogram data comprises the following steps: processing by adopting an Event Related Potential (ERP) mode, and performing high-pass filtering and low-pass filtering on the electroencephalogram data at 0.1hz and 40 hz; then, taking an event mark as a reference, intercepting an electroencephalogram signal of-200 ms to 1000ms as electroencephalogram data in a data abnormal change state; then taking the same event mark as a reference, intercepting an electroencephalogram signal of-1400 ms to-200 ms, and taking the electroencephalogram signal as electroencephalogram data in a data unchanged state when other events mark are absent in the time period; finally, independent component analysis is carried out on the electroencephalogram data so as to remove data of interference and artifact components;
(7) Performing short-time Fourier transform on the processed electroencephalogram data, calculating a wavelet function of the electroencephalogram data for time-frequency analysis, and convolving the wavelet function with a Fourier transform result to obtain time-frequency representation, so that time-frequency energy data of the electroencephalogram data is calculated;
(8) Connectivity analysis is carried out among the 32 selected electrodes, and the connectivity index between any two electrodes is calculated by using an HERMES software system and based on time-frequency energy data: the Pearson correlation coefficient r is calculated by the following relation:
，
wherein: x and Y respectively represent the observed values of two electrodes in a period of time, and X ̄ and Ȳ respectively represent the average value of the observed values of the corresponding electrodes in the same period of time;
(9) Setting a judgment standard of perception sensitivity according to the obtained Pearson correlation coefficient, wherein the judgment standard specifically comprises the following steps:
condition one: between any two electrodes at the selected 32 electrode positions, the Pearson correlation coefficient of the abnormal change state of the data in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition I is larger than 15;
condition II: meanwhile, the Pearson correlation coefficient of the unchanged state of the total control screen data between any two electrodes at the selected 32 electrode positions is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the abnormal change state of the total control screen data, and the number of electrode pairs meeting the second condition is smaller than 3;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, the perception sensitivity is judged to be good, otherwise, the perception sensitivity is judged to be not good.
Embodiment two: as shown in the figure, the method for measuring the human decision responsiveness comprises the following specific steps:
(1) The method comprises the steps of selecting 32 electrode positions according to the electroencephalogram electrode layout of an international 10-10 standard system, installing electroencephalogram electrodes at corresponding positions of a safety working cap, and wearing the safety working cap on the head of a production worker to be tested, wherein the selected 32 electrode positions are F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, TP7, CP5, CP3, P7, P5 and P3;
(2) Monitoring production operation data by a production operator to be tested in front of a master control screen;
(3) The brain electrical electrode collects brain electrical signals of the production operator to be tested;
(4) The data processing system receives the brain electricity data measured by the brain electricity electrode in real time in a wireless mode such as Bluetooth, WIFI and the like;
(5) When the production operation data in the master control screen is abnormal (namely, the index exceeds the threshold), an event trigger is generated at the same time and transmitted to the data processing system to be used as an event mark signal to be recorded in the electroencephalogram data;
(6) Every 30 minutes, the data processing system automatically processes the brain electrical data once: the method comprises the steps of signal amplification, signal noise reduction, artifact removal, high-pass filtering, low-pass filtering and analysis section interception, so as to obtain electroencephalogram data in two states of abnormal data change and unchanged data by taking an event mark as a reference; wherein:
the specific method for filtering, analyzing segment interception and artifact removal of the electroencephalogram data comprises the following steps: processing by adopting an Event Related Potential (ERP) mode, and performing high-pass filtering and low-pass filtering on the electroencephalogram data at 0.1hz and 40 hz; then, taking an event mark as a reference, intercepting an electroencephalogram signal of-200 ms to 1000ms as electroencephalogram data in a data abnormal change state; then taking the same event mark as a reference, intercepting an electroencephalogram signal of-1400 ms to-200 ms, and taking the electroencephalogram signal as electroencephalogram data in a data unchanged state when other events mark are absent in the time period; finally, independent component analysis is carried out on the electroencephalogram data so as to remove data of interference and artifact components;
(7) Performing short-time Fourier transform on the processed electroencephalogram data, calculating a wavelet function of the electroencephalogram data for time-frequency analysis, and convolving the wavelet function with a Fourier transform result to obtain time-frequency representation, so that time-frequency energy data of the electroencephalogram data is calculated;
(8) Connectivity analysis is carried out among the 32 selected electrodes, and the connectivity index between any two electrodes is calculated by using an HERMES software system and based on time-frequency energy data: the Pearson correlation coefficient r is calculated by the following relation:
，
wherein: x and Y respectively represent the observed values of two electrodes in a period of time, and X ̄ and Ȳ respectively represent the average value of the observed values of the corresponding electrodes in the same period of time;
(9) Setting decision response judgment criteria according to the obtained Pearson correlation coefficient, specifically:
dividing F7, F5, F3, FT7, FC5, FC3, T7, C5, C3, TP7, CP5, CP3, P7, P5, P3 of the 32 selected electrode positions into a first region (region circled in the figure), dividing F3, F1, FZ, F2, F4, F6, F8, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, C3, C1, CZ, C2, C4, C6 of the 32 selected electrode positions into a second region (region circled in the figure);
condition one: between any two electrodes in the first area, the Pearson correlation coefficient of the data unchanged state in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the data abnormal changed state of the total control screen, and the number of the electrode pairs meeting the condition I is larger than 5; meanwhile, the number of the electrode pairs meeting the first condition in the second area is smaller than 2;
condition II: between any two electrodes in the first area, the Pearson correlation coefficient in the abnormal change state of the data of the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient in the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition II is smaller than 2; meanwhile, the number of the electrode pairs meeting the second condition in the second area is more than 10;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, determining that the decision response is good, otherwise, determining that the decision response is not good.
When the decision responsiveness of the production operator to be tested is determined to be not good, an alarm prompt is sent when the index of the production operation data in the master control screen is close to 95% of the abnormal threshold value, so that the decision responsiveness of the production operator is improved, and specific alarm prompt modes include, but are not limited to: displaying a picture of the occurrence of the potential fault corresponding to the index in an additional display screen, and displaying four word patterns of potential; meanwhile, the fault sound corresponding to the index is sent out according to a certain sound volume (for example, 30 dB), and the sound volume is increased by 5 dB again when the index rises by 1% of the threshold value.
Actual measurement case: by adopting the measuring method to measure the human body perception sensitivity and the decision responsiveness of a workshop operator, the measurement results are shown in figures 3-6, and the human body perception sensitivity and the decision responsiveness are good according to the judging standard in the method.
The protection scope of the present invention includes, but is not limited to, the above embodiments, the protection scope of which is subject to the claims, and any substitutions, modifications, and improvements made by those skilled in the art are within the protection scope of the present invention.
Claims (9)
1. The method for measuring the human perception sensitivity and the decision responsiveness is characterized by comprising the following specific steps of:
(1) Selecting 32 electrode positions according to the electroencephalogram electrode layout of the international 10-10 standard system, installing electroencephalogram electrodes at the corresponding positions of the safety working cap, and wearing the safety working cap on the head of a production operator to be tested;
(2) Monitoring production operation data by a production operator to be tested in front of a master control screen;
(3) The brain electrical electrode collects brain electrical signals of the production operator to be tested;
(4) The data processing system receives the brain electricity data measured by the brain electricity electrode in real time in a wireless mode;
(5) When the production operation data in the master control screen is abnormal, an event trigger is generated at the same time and transmitted to the data processing system to be used as an event mark signal to be recorded in the electroencephalogram data;
(6) Every 30 minutes, the data processing system automatically processes the brain electrical data once: the method comprises the steps of signal amplification, signal noise reduction, artifact removal, high-pass filtering, low-pass filtering and analysis section interception, so as to obtain electroencephalogram data in two states of abnormal data change and unchanged data by taking an event mark as a reference;
(7) Performing short-time Fourier transform on the processed electroencephalogram data, calculating a wavelet function of the electroencephalogram data for time-frequency analysis, and convolving the wavelet function with a Fourier transform result to obtain time-frequency representation, so that time-frequency energy data of the electroencephalogram data is calculated;
(8) Connectivity analysis is carried out among the 32 selected electrodes, and the connectivity index between any two electrodes is calculated by using an HERMES software system and based on time-frequency energy data: pearson correlation coefficients;
(9) And respectively setting judgment standards of the perception sensitivity and the decision responsiveness according to the obtained Pearson correlation coefficient, if the judgment standards are met, the perception sensitivity and the decision responsiveness are determined to be good, and otherwise, the perception sensitivity and the decision responsiveness are determined to be not good.
2. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (1), when the perception sensitivity is measured, the 32 electrode positions selected are F1, FZ, F2, F4, F6, F8, FC1, FCZ, FC2, FC4, FC6, FT8, C1, CZ, C2, C4, C6, CP1, CPZ, CP2, CP4, CP6, P1, PZ, P2, P4, P6, PO3, POZ, PO4, PO6, PO8;
when measuring decision responsiveness, the 32 electrode positions selected are F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, TP7, CP5, CP3, P7, P5, P3.
3. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (6), the filtering and analyzing section intercepting method for the electroencephalogram data comprises the following steps: processing by adopting an event-related potential mode, and performing high-pass filtering and low-pass filtering on the electroencephalogram data at 0.1hz and 40 hz; then, taking an event mark as a reference, intercepting an electroencephalogram signal of-200 ms to 1000ms as electroencephalogram data in a data abnormal change state; and intercepting the brain electrical signal of-1400 ms to-200 ms by taking the same event mark as a reference, and taking the brain electrical signal as brain electrical data under the state that the data is unchanged if other events mark are not present in the time period.
4. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (6), the artifact removal of the electroencephalogram data adopts independent component analysis to remove the data of interference and artifact components.
5. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (9), the judgment standard of the perception sensitivity is as follows:
condition one: between any two electrodes at the selected 32 electrode positions, the Pearson correlation coefficient of the abnormal change state of the data in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition I is larger than 15;
condition II: meanwhile, the Pearson correlation coefficient of the unchanged state of the total control screen data between any two electrodes at the selected 32 electrode positions is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the abnormal change state of the total control screen data, and the number of electrode pairs meeting the second condition is smaller than 3;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, the perception sensitivity is judged to be good, otherwise, the perception sensitivity is judged to be not good.
6. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (9), the decision responsiveness is judged by the following criteria: dividing F7, F5, F3, FT7, FC5, FC3, T7, C5, C3, TP7, CP5, CP3, P7, P5, P3 of the 32 selected electrode positions into a first region, dividing F3, F1, FZ, F2, F4, F6, F8, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, C3, C1, CZ, C2, C4, C6 of the 32 selected electrode positions into a second region,
condition one: between any two electrodes in the first area, the Pearson correlation coefficient of the data unchanged state in the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient of the data abnormal changed state of the total control screen, and the number of the electrode pairs meeting the condition I is larger than 5; meanwhile, the number of the electrode pairs meeting the first condition in the second area is smaller than 2;
condition II: between any two electrodes in the first area, the Pearson correlation coefficient in the abnormal change state of the data of the total control screen is obviously (the statistical judgment standard is p < 0.05) larger than the Pearson correlation coefficient in the unchanged state of the data of the total control screen, and the number of electrode pairs meeting the condition II is smaller than 2; meanwhile, the number of the electrode pairs meeting the second condition in the second area is more than 10;
and when the communication condition between any two electrodes at the selected 32 electrode positions meets the two conditions, determining that the decision response is good, otherwise, determining that the decision response is not good.
7. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: in the step (8), the calculation relation of the Pearson correlation coefficient r is as follows:
，
wherein: x and Y represent observations of two electrodes over a period of time, respectively, and X ̄ and Ȳ represent the average of observations of the corresponding electrodes over the same period of time, respectively.
8. A method for measuring human perceptual sensitivity and decision responsiveness as defined in claim 1, wherein: when the decision responsiveness of the production operator to be tested is determined to be not good, an alarm prompt is sent out when the index of the production operation data in the master control screen is close to 95% of the abnormal threshold value, so that the decision responsiveness of the production operator is improved.
9. A method of measuring human perceptual sensitivity and decision responsiveness as defined in claim 8, wherein: the alarm reminding mode comprises the following steps: displaying a picture of the occurrence of the potential fault corresponding to the index in an additional display screen, and displaying four word patterns of potential; simultaneously, a fault sound corresponding to the index is emitted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311305724.2A CN117322891B (en) | 2023-10-09 | 2023-10-09 | Measurement method for human perception sensitivity and decision responsiveness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311305724.2A CN117322891B (en) | 2023-10-09 | 2023-10-09 | Measurement method for human perception sensitivity and decision responsiveness |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117322891A true CN117322891A (en) | 2024-01-02 |
| CN117322891B CN117322891B (en) | 2024-07-02 |
Family
ID=89276816
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311305724.2A Active CN117322891B (en) | 2023-10-09 | 2023-10-09 | Measurement method for human perception sensitivity and decision responsiveness |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117322891B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100026426A (en) * | 2008-08-29 | 2010-03-10 | 연세대학교 산학협력단 | An eeg-based real-time functional cortical connectivity monitoring system |
| US20200345287A1 (en) * | 2019-04-30 | 2020-11-05 | WITFIT, Ltd. | Mental fitness assessment systems and methods using physiologic signal inputs |
| CN112932505A (en) * | 2021-01-16 | 2021-06-11 | 北京工业大学 | Symbol transfer entropy and brain network characteristic calculation method based on time-frequency energy |
| CN113855046A (en) * | 2021-10-12 | 2021-12-31 | 华中科技大学 | Intelligent safety helmet for monitoring human body physical signs and brain electrical information and monitoring method thereof |
| CN116636859A (en) * | 2023-06-08 | 2023-08-25 | 中国人民解放军国防科技大学 | Evaluation method and system for visual cognition capability based on electroencephalogram signals |
-
2023
- 2023-10-09 CN CN202311305724.2A patent/CN117322891B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100026426A (en) * | 2008-08-29 | 2010-03-10 | 연세대학교 산학협력단 | An eeg-based real-time functional cortical connectivity monitoring system |
| US20200345287A1 (en) * | 2019-04-30 | 2020-11-05 | WITFIT, Ltd. | Mental fitness assessment systems and methods using physiologic signal inputs |
| CN112932505A (en) * | 2021-01-16 | 2021-06-11 | 北京工业大学 | Symbol transfer entropy and brain network characteristic calculation method based on time-frequency energy |
| CN113855046A (en) * | 2021-10-12 | 2021-12-31 | 华中科技大学 | Intelligent safety helmet for monitoring human body physical signs and brain electrical information and monitoring method thereof |
| CN116636859A (en) * | 2023-06-08 | 2023-08-25 | 中国人民解放军国防科技大学 | Evaluation method and system for visual cognition capability based on electroencephalogram signals |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117322891B (en) | 2024-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102973266B (en) | For method, equipment and computer program that automatic spasm is monitored | |
| CN111951637B (en) | Task-context-associated unmanned aerial vehicle pilot visual attention distribution mode extraction method | |
| CN114063507A (en) | Remote equipment control system based on intelligent agriculture and control method thereof | |
| CN113842118B (en) | Epileptic seizure real-time detection monitoring system for epileptic video electroencephalogram examination | |
| CN104720796A (en) | Automatic detecting system and method for epileptic attack time period | |
| CN106580349A (en) | Controller fatigue detection method and device and controller fatigue responding method and device | |
| CN110367975A (en) | A kind of fatigue driving detection method for early warning based on brain-computer interface | |
| CN115165079A (en) | Equipment alarm method based on multiple sound characteristic analysis | |
| CN113806969B (en) | Compressor unit health prediction method based on time domain data correlation modeling | |
| CN111938673A (en) | Anxiety state detection and feedback system based on EEG signal | |
| CN117594253B (en) | Personnel health early warning method and system based on intelligent monitoring equipment | |
| CN111067513B (en) | Sleep quality detection key brain area judgment method based on characteristic weight self-learning | |
| CN117322891B (en) | Measurement method for human perception sensitivity and decision responsiveness | |
| CN116661381A (en) | Intelligent production equipment remote monitoring system | |
| CN106388780A (en) | Sleep state detection method and system based on fusion of two classifiers and detector | |
| CN112545460B (en) | Mental load detection method based on double visual-audio tasks | |
| CN110934600A (en) | Anti-fatigue driving early warning device based on brain wave monitoring and monitoring method | |
| CN118315071A (en) | Monitoring method and system for intelligent finger ring based on PPG physiological index monitoring | |
| CN117475482A (en) | Tongue picture feature extraction method and device | |
| CN116211309A (en) | System and method for judging situation awareness state of operator based on eye movement and brain electrical characteristics | |
| CN114742090A (en) | Cockpit man-machine interaction system based on mental fatigue monitoring | |
| CN109334669B (en) | Sign safety monitoring method and data processing system for driver in driving state | |
| CN114343661A (en) | Method, device and equipment for estimating reaction time of high-speed rail driver and readable storage medium | |
| CN117322892B (en) | Perception sensitivity discrimination method based on auditory and visual two-dimensional gradient measurement | |
| CN113191191A (en) | Community epidemic situation management method and system based on user habit analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |