CN111466908A - Method for screening audio capable of affecting appetite by utilizing EEG (electroencephalogram) - Google Patents
Method for screening audio capable of affecting appetite by utilizing EEG (electroencephalogram) Download PDFInfo
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Abstract
The invention discloses a method for screening audio frequency capable of influencing appetite by utilizing EEG, which screens out the audio frequency capable of influencing the appetite by analyzing EEG generated when listening to the audio frequency, has good creativity, avoids the trouble of collecting the appetite data of a tested object when training a second L STM neural network by analyzing the EEG by a first L STM neural network, can directly collect the brain wave of the tested object and put the brain wave into the first L STM neural network for analysis to obtain the appetite data of the tested object, greatly accelerates the speed of generating the training data of the second L STM neural network, improves the efficiency for screening the audio frequency capable of influencing the appetite, has extremely high safety compared with the method for restraining the appetite by utilizing drugs, a tested person does not need to take any drug which can cause harm, only needs to listen to music to play a certain effect of restraining the appetite, and has lower cost compared with the method for restraining the appetite by utilizing drugs.
Description
Technical Field
The invention relates to the field related to an EEG detection technology, in particular to a method for screening an audio frequency capable of influencing appetite by utilizing an EEG.
Background
The existing methods for controlling appetite physiologically generally comprise two methods, namely drug control. Such as fenfluramine, which acts on the brain feeding center to suppress appetite. But it causes heart valve damage and pulmonary hypertension, depression, nausea, dizziness and sleepiness. There were several fatal medical events, which were released in 1997. Yet another is aversion therapy. The conditioned reflex method is adopted, and target behaviors needing to be abstained are combined with unpleasant or punitive stimulation, so that the target behaviors are removed from the attractiveness of the patient through aversive conditioned reflex, and symptoms are removed. Such as shock, by creating a penalty feedback on appetite, thereby reducing appetite. This method causes intense discomfort to the person.
It is now known that a variety of behaviors can affect appetite in different situations. Such as satiety, and watching pictures of nausea, smelling a taste of food, etc. The existing scholars screen the audio capable of suppressing the appetite by analyzing the audio bpm and judging whether the bpm is more than 140, but the problems of low precision and poor effect exist.
To this end, we propose a method for screening for appetites-affecting audio using EEG.
Disclosure of Invention
The present invention is directed to a method for screening audio frequency capable of affecting appetite by using EEG, so as to solve the problems mentioned in the background art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of screening for appetite-affecting audio using EEG, comprising the steps of:
s1, a testee wears a brain electricity acquisition instrument to acquire brain electricity data in real time, time slicing is switched once at fixed time, the testee watches different food pictures on different time slices randomly, the testee scores food eating desire in the pictures according to the testee, then the electroencephalogram sequence signal of each slice is used as the input of a L STM network, the score is used as a corresponding label, and the prediction score precision of the L STM network is continuously improved through training;
s2, constructing a new L STM model, enabling a testee to wear an electroencephalogram acquisition instrument to acquire electroencephalogram data in real time, enabling the testee to listen to sounds with different frequencies and rhythms in the process, similarly switching a time slice at a fixed time, playing different audios by different time slices, and enabling the audios of the same time slice to correspond to the acquired electroencephalograms one by one, wherein electroencephalogram signals are input into the model in the first step as a label, the audio signals are input into the new L STM model, the label is an output result of the electroencephalogram signals input into the model in the first step, and the new L STM model is obtained through training, wherein the input is a section of audio, and the output is a guess of an appetite signal of the user;
s3, the testee wearing the earphone switches a time slice at a fixed time, different audio is played in different time slices, and the played audio is divided into two types, one type is the audio randomly screened from a large amount of open source audio, the other type is the audio with low score obtained in the model of the second step, the two types of audio are randomly played in different time slices, and meanwhile, the testee is allowed to score a picture of random food in each time slice according to the eating desire.
Preferably, the score value ranges from one to ten minutes, one representing the worst desire to eat and one representing the strongest desire to eat.
Preferably, the frequency of the switching time slices is one time slice of five seconds.
Preferably, the electroencephalogram signal is input at a sampling rate of 200Hz or more.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention screens out the audio frequency which can influence the appetite by analyzing the EEG generated when listening to the audio frequency, and has good creativity;
2. through the analysis of the EEG by the first L STM neural network, the trouble of acquiring the appetite data of the test object is avoided when the second L STM neural network is trained, the brain waves of the test object can be directly acquired and put into the first L STM neural network for analysis to obtain the appetite data of the test object, the speed of generating the training data of the second L STM neural network is greatly increased, and the efficiency of screening the audios influencing the appetite is improved;
3. compared with the method for suppressing the appetite by using the medicines, the method has extremely high safety, a person to be tested does not need to take any medicines which can cause harm, the person only needs to listen to music to play a certain effect of suppressing the appetite, and the cost is lower when the method is used compared with the method for suppressing the appetite by using the medicines.
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FIG. 1 is a flow chart of training using an EEG signal imported L STM neural network in a method for screening an audio frequency capable of affecting appetite by using EEG according to the present invention;
FIG. 2 is a flow chart of training using a plurality of open source audio import L STM neural networks in a method for screening appetite-affecting audio using EEG according to the present invention;
FIG. 3 is a table comparing the impact of random audio and suppressing the appetite audio on appetite in a method of using EEG to screen for appetite-influenceable audio according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
1-3, the present invention also provides a method for screening audio capable of affecting appetite using EEG, comprising the following steps:
s1, a testee wears a brain electricity acquisition instrument to acquire brain electricity data in real time, time slicing is switched once at fixed time, the testee watches different food pictures on different time slices randomly, the testee scores food eating desire in the pictures according to the testee, then the electroencephalogram sequence signal of each slice is used as the input of a L STM network, the score is used as a corresponding label, and the prediction score precision of the L STM network is continuously improved through training;
s2, constructing a new L STM model, enabling a testee to wear an electroencephalogram acquisition instrument to acquire electroencephalogram data in real time, enabling the testee to listen to sounds with different frequencies and rhythms in the process, similarly switching a time slice at a fixed time, playing different audios by different time slices, and enabling the audios of the same time slice to correspond to the acquired electroencephalograms one by one, wherein electroencephalogram signals are input into the model in the first step as a label, the audio signals are input into the new L STM model, the label is an output result of the electroencephalogram signals input into the model in the first step, and the new L STM model is obtained through training, wherein the input is a section of audio, and the output is a guess of an appetite signal of the user;
s3, the testee wearing the earphone switches a time slice at a fixed time, different audio is played in different time slices, and the played audio is divided into two types, one type is the audio randomly screened from a large amount of open source audio, the other type is the audio with low score obtained in the model of the second step, the two types of audio are randomly played in different time slices, and meanwhile, the testee is allowed to score a picture of random food in each time slice according to the eating desire.
Further, the score value ranges from one to ten minutes, one representing the worst desire to eat and one representing the strongest desire to eat.
Further, the frequency of the switching time slice is five seconds and one time slice.
Further, the electroencephalogram signal is input at a sampling rate of 200Hz or more.
In the invention, by establishing two L STM neural networks, in a first L STM neural network, the input of training data is EEG, the output of the training data is appetite, in a second L0 STM neural network, the training result of a first L STM neural network is applied, the input of the training data is audio, the output of the training data is the output generated by inputting electroencephalogram signals generated by a test object while listening to the current audio into a first L STM neural network, namely appetite, the analysis of EEG by the first L STM neural network eliminates the trouble of acquiring appetite data of the test object while training a second L neural network, the electroencephalogram of the test object can be directly acquired and put into the first L STM neural network for analysis to obtain appetite data of the test object, the speed of generating the training data of the second L STM neural network is greatly accelerated, when two L STM neural networks are fully obtained (to achieve a sufficient accuracy rate), the speed of generating the training data of the second STM neural network is greatly increased, the audio can be used for generating a great amount of audios without the influence of appetite suppression of appetite of the appetite of other drugs, the appetite of a person who is reduced by the appetite of the appetite, the appetite of the invention, the invention can be used for suppressing the appetite of the invention, the appetite of the invention, the invention can be used for the invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A method for screening appetite-affecting audio using EEG, comprising the steps of:
s1, a testee wears a brain electricity acquisition instrument to acquire brain electricity data in real time, time slicing is switched once at fixed time, the testee watches different food pictures on different time slices randomly, the testee scores food eating desire in the pictures according to the testee, then the electroencephalogram sequence signal of each slice is used as the input of a L STM network, the score is used as a corresponding label, and the prediction score precision of the L STM network is continuously improved through training;
s2, constructing a new L STM model, enabling a testee to wear an electroencephalogram acquisition instrument to acquire electroencephalogram data in real time, enabling the testee to listen to sounds with different frequencies and rhythms in the process, similarly switching a time slice at a fixed time, playing different audios by different time slices, and enabling the audios of the same time slice to correspond to the acquired electroencephalograms one by one, wherein electroencephalogram signals are input into the model in the first step as a label, the audio signals are input into the new L STM model, the label is an output result of the electroencephalogram signals input into the model in the first step, and the new L STM model is obtained through training, wherein the input is a section of audio, and the output is a guess of an appetite signal of the user;
s3, the testee wearing the earphone switches a time slice at a fixed time, different audio is played in different time slices, and the played audio is divided into two types, one type is the audio randomly screened from a large amount of open source audio, the other type is the audio with low score obtained in the model of the second step, the two types of audio are randomly played in different time slices, and meanwhile, the testee is allowed to score a picture of random food in each time slice according to the eating desire.
2. The method of claim 1 wherein the score values range from one to ten minutes, one representing the worst appetite and one representing the strongest appetite.
3. The method of claim 1, wherein the switching time slices are at a frequency of five seconds and one time slice.
4. The method of claim 1, wherein the EEG signal is input at a sampling rate of 200Hz or higher.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116999072A (en) * | 2023-08-23 | 2023-11-07 | 北京理工大学 | Appetite intervention system and method based on individualized brain electrical nerve feedback |
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| CN103370703A (en) * | 2010-03-08 | 2013-10-23 | 健康看护股份有限公司 | Method and apparatus to monitor, analyze and optimize physiological state of nutrition |
| CN107438398A (en) * | 2015-01-06 | 2017-12-05 | 大卫·伯顿 | Portable wearable monitoring system |
| CN110402138A (en) * | 2017-01-10 | 2019-11-01 | 奈克提姆制药有限公司 | The composition for reducing appetite and thirsting for, increasing satiety, improve mood and ease off the pressure |
| US20190369727A1 (en) * | 2017-06-29 | 2019-12-05 | South China University Of Technology | Human-machine interaction method based on visual stimulation |
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2020
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| JPH08112182A (en) * | 1994-10-13 | 1996-05-07 | Royal Kogyo Kk | Tableware having vocal function |
| CN103370703A (en) * | 2010-03-08 | 2013-10-23 | 健康看护股份有限公司 | Method and apparatus to monitor, analyze and optimize physiological state of nutrition |
| CN107438398A (en) * | 2015-01-06 | 2017-12-05 | 大卫·伯顿 | Portable wearable monitoring system |
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| US20190369727A1 (en) * | 2017-06-29 | 2019-12-05 | South China University Of Technology | Human-machine interaction method based on visual stimulation |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116999072A (en) * | 2023-08-23 | 2023-11-07 | 北京理工大学 | Appetite intervention system and method based on individualized brain electrical nerve feedback |
| CN116999072B (en) * | 2023-08-23 | 2024-02-27 | 北京理工大学 | Appetite intervention system and method based on individualized brain electrical nerve feedback |
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