Brain wave intervention method based on artificial intelligence

Abstract

The invention provides a brain wave intervention method based on artificial intelligence. The method comprises the following steps: acquiring memory emotion data and brain wave data of a user in real time; the scene memory is awakened by using the VR equipment; determining a memory consolidation window period according to the memory emotion data; playing the intervention memory scene content by using the VR equipment in the memory consolidation window period, and calculating the concentration degree of the user according to the brain wave data; and when the concentration degree of the user is not within the set threshold range, performing brain wave intervention on the user. According to the method, the memory is intervened and rewritten in the memory consolidation window period, and the brain wave intervention is performed on the brain, so that the user can intervene and rewrite the memory more intensively, and the memory rewriting is deeper and more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of memory intervention, in particular to a brain wave intervention method based on artificial intelligence. Background Art

[0002] At present, the increasingly serious drug problem has become a global disaster. The spread of drugs directly endangers people's physical and mental health, and poses a huge threat to economic development and social progress. According to statistics from the United Nations, the annual drug trade in the world is more than 500 billion US dollars. The scope of drug spread has expanded to more than 200 countries and regions on five continents, and the number of people who take various drugs in the world has reached more than 200 million, of which 78% are young and middle-aged people aged 17 to 35. Memory is formed through consolidation, but memory is not stable. Studies have found that when people recall the memory again, the memory is activated again, and the memory reconsolidation process is started. At this time, the memory is in an unstable state. Interference in the time window of memory reconsolidation can play a role in rewriting, clearing or strengthening the original memory. Emotional memory refers to the memory formed by the emotions, feelings or feelings that have been experienced. The memory formed after an individual has suffered a disaster, psychological trauma or drug abuse is called pathological memory. Pathological memory may cause mental illness in people, such as anxiety, depression, traumatic stress disorder or addiction. Therefore, intervention in the pathological memory reconsolidation process can eliminate fear memory, addiction memory, etc., and can be used in the treatment of phobia, post-traumatic stress disorder, anxiety disorder, substance addiction, etc. Similarly, pleasant memories can also be strengthened for the treatment of depression, etc. Traditional extinction training is used in the memory reconsolidation time window, and a "memory evoked-extinction behavior intervention model" is proposed. The principle is that traditional extinction training can change the fear valence of the conditioned stimulus and place it in the reconsolidation memory time window, thereby forming a new non-fear valence of the conditioned stimulus, thus rewriting the initial fear memory, weakening memory traces and channels, and blocking the fear response. The following are some of the commonly used drug rehabilitation methods: 1) Natural withdrawal method: that is, drug addicts are not provided with special drugs for drug rehabilitation, and they are forced to stop taking drugs, allowing withdrawal symptoms to develop and subside naturally. The application of natural withdrawal method has a long history, but it has been applied all over the world for decades, and there has been no effect of learning lessons and not relapsing. Instead, drug addicts are afraid of drug rehabilitation institutions, try to avoid them, and lose the enthusiasm for seeking treatment. The completion of drug and addiction is not all good. The prolonged withdrawal symptoms will not fade away after half a year or even a year. Drug addicts feel tortured at any time, and the craving for drugs will lead to relapse sooner or later.

[0003] 2) Opioid substitution tapering therapy: This method has been used for the longest time in my country. It is obviously effective in detoxifying opioid dependence. It is safe for clinical use and has few side effects. It is suitable for opioid or mild heroin addicts. However, the dosage of opioid substitution tapering therapy must be controlled appropriately. A large dosage is not easy to taper and withdraw, and a small dosage cannot achieve the purpose of alleviating withdrawal symptoms. It must follow the principle of individual dosage according to different people. It is not easy to master and can easily lead to drug addiction. Drug addiction, especially opioid addiction, is one of the current narcotic abuse problems in the world and a major social hazard.

[0004] 3) Traditional Chinese medicine detoxification method: mainly based on traditional Chinese medicine adjustment, improve the immunity of drug addicts, enhance physical quality, and carry out auxiliary detoxification treatment. This method has a wide range of effects, and the traditional Chinese medicine compound has effects on multiple points in the human body at the same time, but it takes a long time to take effect and has weak drug strength. It can be used for those who have not been taking drugs for a long time, have not taken a large amount of drugs, and have not been seriously physically dependent; however, it cannot meet the requirements of patients who need acute drug treatment.

[0005] 4) Acupuncture and moxibustion for drug addiction treatment: Acupuncture and moxibustion has a history of thousands of years in China. Stimulating specific acupuncture points can generate and secrete a variety of chemical substances in the brain that have similar effects to morphine, breaking through the inhibition caused by exogenous opium substances and gradually restoring the function of releasing endogenous opium peptides. This method must be performed by a professional doctor who must have an accurate understanding of the acupuncture points, and it takes a long time to take effect and the treatment period is long.

[0006] The above methods all require a long period of time and the cooperation of professional doctors in treatment. The treatment effect is closely related to the doctor's experience, and it is impossible to provide real-time and effective feedback treatment based on the actual situation of the individual. Summary of the invention

[0007] The present invention solves the problem that the existing technology cannot perform real-time and effective memory intervention according to the actual situation of the individual, and proposes a brain wave intervention method based on artificial intelligence. By intervening and rewriting the memory during the memory consolidation window period, and by intervening in the brain's brain waves, the user can intervene and rewrite the memory more attentively, making the memory rewriting more profound and efficient.

[0008] In order to achieve the above objectives, the following technical solutions are proposed: A brain wave intervention method based on artificial intelligence, comprising the following steps: S1, collects the user's memory emotion data and brain wave data in real time; S2, using VR equipment to awaken episodic memory; S3, determine the memory consolidation window period based on memory emotion data; S4, using VR devices to play intervention memory scenarios during the memory consolidation window and calculating the user's concentration based on brain wave data; S5, when the user's concentration is not within the set threshold range, the user's brain wave intervention is performed.

[0009] The present invention uses VR equipment to play simulated scenarios for awakening specific memories, thereby triggering the user's specific memories, such as the memory of taking drugs, and then plays intervention scenarios for intervening in memory during the memory consolidation window period to divert and confuse the memory of taking drugs. According to the user's own concentration, through external brain wave input, the user's concentration is improved, and the intervention scenario of intervening in memory can be remembered more attentively, achieving the effect of diluting the memory of taking drugs. The present invention can effectively intervene in the user's specific memory, making the memory rewriting more profound and efficient.

[0010] Preferably, the remembered emotion data includes blood pressure data or heart rate data.

[0011] Preferably, the S2 specifically comprises the following steps: The VR device stores several simulation scenarios for awakening specific memories and intervention scenarios for intervening in memories. The user selects a simulation scenario to awaken the specific memory. In the present invention, the simulation scenario pre-stored in the VR device is first used to awaken the specific memory, and then the intervention scenario is played when the brain is in the memory consolidation window period of the specific memory, so as to divert and confuse the specific memory, and effectively intervene in the user's specific memory.

[0012] Preferably, S3 specifically includes the following steps: Obtain the emotional characteristics of a specific memory; until the real-time memory emotional data meets the emotional characteristics, this moment is taken as the starting moment of the memory consolidation window period, and 30 minutes to 40 minutes from the starting moment is taken as the memory consolidation window period.

[0013] Preferably, the process of obtaining the emotion characteristics under the specific memory is as follows: Obtain the user's memory emotion data under a certain special memory; The trigger average value N and the trigger fluctuation value P of the memory emotion data are calculated, and the trigger average value N and the trigger fluctuation value P are used as the emotion characteristics under the specific memory.

[0014] In the present invention, specific memory will generate specific memory emotion data, and the average value and amplitude within a period of time are relatively stable. The memory emotion data mean and its fluctuation value can be used to quantify and calculate the memory emotion data characteristics. When the triggering average value N and the triggering fluctuation value P meet the emotional characteristics of a certain specific memory, it is considered that the specific memory has been triggered, and then 30 to 40 minutes after the specific memory is triggered is used as the memory consolidation window period.

[0015] Preferably, the process of determining whether the memory emotion data meets the emotion characteristics is as follows: S301, acquiring historical memory emotion data; acquiring memory emotion data at a certain time interval, grouping the acquired memory emotion data according to the time sequence, and taking all memory emotion data before the current acquisition time as historical memory emotion data; S302, calculating a memory emotion data change value according to the historical memory emotion data; Take the most recent set of historical memory emotion data before the previous collection moment and calculate the mean to get each set of change values ​​E[X(n)]: E[X(n)]=X(n)-An Among them: X(n) is the nth memory emotion data of the group, An is the mean value of the group data; the calculation formula of An is: S303, determine whether An at the current collection time is between 90%N and 110%N, if not, return to S1, if yes, proceed to S304; S304, judging whether: |Emax-Emin|<110%P Where: Emax is the maximum change value, Emin is the minimum change value, and L is the allowable fluctuation value; if not, no processing is performed and the process returns to S1; if so, the memory emotion data is determined to meet the emotion characteristics and the current moment is recorded.

[0016] Preferably, the process of calculating the user's concentration based on brain wave data is as follows: obtain the user's brain wave data when concentrating without intervention, and divide the real-time acquired brain wave data by the brain wave data when concentrating to obtain a concentration value.

[0017] Preferably, the threshold range of concentration setting is 0.9-1.1.

[0018] Preferably, the process of performing brain wave intervention on the user is as follows: Obtain brain wave data when the user is concentrating without intervention; Get real-time brain wave data; The brain wave data that needs to be compensated is obtained based on the brain wave data during concentration and the real-time brain wave data; The brain wave data that needs to be compensated is sent to the user's brain for coordination.

[0019] The present invention improves the user's concentration through external brain wave input according to the user's own concentration, so that the user can remember the intervention scenario of the intervention memory more attentively, thereby achieving the effect of weakening the memory of drug use. The present invention can effectively intervene in the user's specific memory, making the memory rewriting more profound and efficient.

[0020] The beneficial effects of the present invention are as follows: the present invention plays a simulated scenario for awakening a specific memory through a VR device, thereby triggering the user's specific memory, such as the memory of taking drugs, and then plays an intervention scenario for intervening in the memory during the memory consolidation window period to divert and confuse the memory of taking drugs, and improves the user's concentration through external brain wave input according to the user's own concentration level, so that the user can remember the intervention scenario of the intervention memory more attentively, thereby achieving the effect of diluting the memory of taking drugs. The present invention can effectively intervene in the user's specific memory, making the memory rewriting more profound and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of an embodiment. DETAILED DESCRIPTION

[0022] Example: This embodiment proposes a brain wave intervention method based on artificial intelligence, referring to Figure 1 , including the following steps: S1, real-time collection of the user's memory emotion data and brain wave data; the memory emotion data includes blood pressure data or heart rate data. In this embodiment, the heart rate data is used for illustration.

[0023] S2, using VR equipment to awaken the situational memory; S2 specifically includes the following steps: The VR device stores several simulation scenarios for awakening specific memories and intervention scenarios for intervening in memories. The user selects a simulation scenario to awaken the specific memory. In this embodiment, the awakening of drug taking memory is taken as an example. In the present invention, the simulation scenario pre-stored in the VR device is first used to awaken the drug taking memory, and then the intervention scenario is played when the brain is in the memory consolidation window period of the drug taking memory, so as to divert and confuse the specific memory, and effectively intervene in the user's specific memory.

[0024] S3, determining the memory consolidation window period according to the memory emotion data; S3 specifically includes the following steps: The emotional characteristics of a specific memory are obtained, which are the emotional characteristics of drug taking memories in this embodiment; until the real-time memory emotional data meets the emotional characteristics, this moment is used as the starting moment of the memory consolidation window period, and 30 minutes to 40 minutes from the starting moment is used as the memory consolidation window period.

[0025] The process of obtaining the emotional features under a specific memory is as follows: Obtain the user's memory emotion data under a certain special memory; Calculate the trigger average value N and trigger fluctuation value P of the memory emotion data, and use the trigger average value N and trigger fluctuation value P as the emotion characteristics of the specific memory. In the present invention, the specific memory will generate specific memory emotion data, and the average value and amplitude within a period of time are relatively stable. The memory emotion data mean and its fluctuation value can be used to quantify and calculate the memory emotion data characteristics. When the trigger average value N and the trigger fluctuation value P meet the emotion characteristics of a certain specific memory, it is considered that the specific memory has been triggered, and 30 minutes to 40 minutes after the specific memory is triggered is used as the memory consolidation window period.

[0026] Taking the memory emotion data obtained from a certain user under drug taking memory as an example, the heart rate data was obtained every 15s for 5 minutes, and the data details were as follows: 114 times / minute, 116 times / minute, 115 times / minute, 122 times / minute, 120 times / minute, 115 times / minute, 116 times / minute, 115 times / minute, 126 times / minute, 122 times / minute, 118 times / minute, 119 times / minute, 119 times / minute, 122 times / minute, and 120 times / minute. The emotional characteristics of the user under drug taking memory are calculated, the triggering average value N = (114 + 116 + 115 + 122 + 120 + 115 + 116 + 115 + 126 + 122 + 118 + 119 + 119 + 122 + 120) / 15 = 118.6 times / minute, and the triggering fluctuation value P = 126-114 = 12.

[0027] The process of determining whether the memory emotion data meets the emotion characteristics is as follows: S301, obtaining historical memory emotion data; obtaining memory emotion data at a certain time interval, such as 5 minutes, grouping the obtained memory emotion data according to the time sequence, collecting it once every 15 seconds, and taking the data collected every minute as a group, and taking all memory emotion data before the current collection time as historical memory emotion data; It lasts for 5 minutes, and the data details are as follows: 115 times / min, 117 times / min, 116 times / min, 123 times / min, 121 times / min, 116 times / min, 117 times / min, 116 times / min, 127 times / min, 123 times / min, 119 times / min, 120 times / min, 120 times / min, 124 times / min, and 120 times / min.

[0028] The first group was 115 times / minute, 117 times / minute, and 116 times / minute; the second group was 123 times / minute, 121 times / minute, and 116 times / minute; the third group was 117 times / minute, 116 times / minute, and 127 times / minute; the fourth group was 123 times / minute, 119 times / minute, and 120 times / minute; the fifth group was 120 times / minute, 123 times / minute, and 120 times / minute.

[0029] S302, calculating a memory emotion data change value according to the historical memory emotion data; Take the most recent set of historical memory emotion data before the previous collection moment and calculate the mean to get each set of change values ​​E[X(n)]: E[X(n)]=X(n)-An Among them: X(n) is the nth memory emotion data of the group, An is the mean value of the group data; the calculation formula of An is: The fifth group of historical memory emotion data that is the most recent before the current moment is used to calculate the mean value to obtain the change value E[X(n)] and An of each group; An=(120+123+120) / 3=121; E[X(1)]=120-121=-1, E[X(2)]=123-121=2, E[X(3)]=120-121=-1.

[0030] S303, determine whether An=121 at the current acquisition time is between 90%N=0.9×118.6=106.74 and 110%N=1.1×118.6=130.46, if not, return to S1, if yes, proceed to S304; S304, judging whether: |Emax-Emin|<90%P; 90%P=0.9×12=10.8 That is, it is determined that 2-(-1)=3 and 3<10.8, so the condition is met.

[0031] Wherein: Emax is the maximum change value, Emin is the minimum change value, and L is the allowable fluctuation value; if not, no processing is performed and the process returns to S301; if so, it is determined that the memory emotion data meets the emotion characteristics and the current moment is recorded.

[0032] S4, using VR equipment to play intervention memory scenario content during the memory consolidation window period and calculating the user's concentration based on brain wave data; the process of calculating the user's concentration based on brain wave data is as follows: obtain the user's brain wave data when the user is concentrating without intervention. In this embodiment, the brain wave data is the brain wave frequency. In this embodiment, the brain wave frequency of the user when the user is concentrating without intervention is 12 times per second. Divide the real-time acquired brain wave data by the brain wave data when concentrating to obtain the concentration value. That is, divide the real-time acquired brain wave frequency by 12 to obtain the concentration value

[0026] S5, when the user's concentration is not within the set threshold range, the concentration threshold range is set to 0.9-1.1, and brain wave intervention is performed on the user.

[0033] The process of brain wave intervention for the user is as follows: Obtain brain wave data when the user is concentrating without intervention; Get real-time brain wave data; The brain wave data that needs to be compensated is obtained based on the brain wave data during concentration and the real-time brain wave data; The brain wave data that needs to be compensated is sent to the user's brain for coordination.

[0034] The present invention uses VR equipment to play simulated scenarios for awakening specific memories, thereby triggering the user's specific memories, such as the memory of taking drugs, and then plays intervention scenarios for intervening in memory during the memory consolidation window period to divert and confuse the memory of taking drugs. According to the user's own concentration, through external brain wave input, the user's concentration is improved, and the intervention scenario of intervening in memory can be remembered more attentively, achieving the effect of diluting the memory of taking drugs. The present invention can effectively intervene in the user's specific memory, making the memory rewriting more profound and efficient.

Claims

1. A brain wave intervention method based on artificial intelligence, characterized in that: The following steps are involved: S1, collects the user's memory emotion data and brain wave data in real time; S2, using VR equipment to awaken episodic memory; S3, determine the memory consolidation window period based on memory emotion data; S4, using VR devices to play intervention memory scenario content during the memory consolidation window and calculating the user's concentration based on brain wave data; S5, when the user's concentration is not within the set threshold range, the user's brain wave intervention is performed.

2. The method for brain wave intervention based on artificial intelligence according to claim 1, characterized in that: The memorized emotion data includes blood pressure data or heart rate data.

3. The method for brain wave intervention based on artificial intelligence according to claim 1, characterized in that: The S2 specifically includes the following steps: The VR device stores several simulation scenarios for awakening specific memories and intervention scenarios for intervening in memories. The user selects a simulation scenario to awaken a specific memory.

4. The method for brain wave intervention based on artificial intelligence according to claim 1, characterized in that: The S3 specifically includes the following steps: Obtain the emotional characteristics of a specific memory; until the real-time memory emotional data meets the emotional characteristics, this moment is taken as the starting moment of the memory consolidation window period, and 30 minutes to 40 minutes from the starting moment is taken as the memory consolidation window period.

5. The method for brain wave intervention based on artificial intelligence according to claim 4 is characterized in that: The process of obtaining the emotional features under a specific memory is as follows: Obtain the user's memory emotion data under a certain special memory; The trigger average value N and the trigger fluctuation value P of the memory emotion data are calculated, and the trigger average value N and the trigger fluctuation value P are used as the emotion characteristics under the specific memory.

6. The method for brain wave intervention based on artificial intelligence according to claim 5, characterized in that: The process of determining whether the memory emotion data meets the emotion characteristics is as follows: S301, acquiring historical memory emotion data; acquiring memory emotion data at a certain time interval, grouping the acquired memory emotion data according to the time sequence, and taking all memory emotion data before the current acquisition time as historical memory emotion data; S302, calculating a memory emotion data change value according to the historical memory emotion data; Take the most recent set of quality memory emotion data before the previous collection moment and calculate the mean to get each set of change values ​​E[X(n)]: E[X(n)]=X(n)-An Among them: X(n) is the nth memory emotion data of the group, An is the mean value of the group data; the calculation formula of An is: S303, determine whether An at the current collection time is between 90%N and 110%N, if not, return to S1, if yes, proceed to S304; S304, judging whether: |Emax-Emin|<110%P Wherein: Emax is the maximum change value, Emin is the minimum change value, and L is the allowable fluctuation value; if not, no processing is performed and the process returns to S301; if so, it is determined that the memory emotion data meets the emotion characteristics and the current moment is recorded.

7. The method of brain wave intervention based on artificial intelligence according to claim 1, characterized in that: The process of calculating the user's concentration based on brain wave data is as follows: obtain the user's brain wave data when concentrating without intervention, and divide the real-time acquired brain wave data by the brain wave data when concentrating to obtain the concentration value.

8. The method for brain wave intervention based on artificial intelligence according to claim 7, characterized in that: The threshold range for concentration setting is 0.9-1.

1.

9. The method of brain wave intervention based on artificial intelligence according to claim 1, characterized in that: The process of brain wave intervention for the user is as follows: Obtain brain wave data when the user is concentrating without intervention; Get real-time brain wave data; The brain wave data that needs to be compensated is obtained based on the brain wave data during concentration and the real-time brain wave data; The brain wave data that needs to be compensated is sent to the user's brain for coordination.

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