CN111671396A - Sleep dream feedback method based on EEG signal - Google Patents

Sleep dream feedback method based on EEG signal Download PDF

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Publication number
CN111671396A
CN111671396A CN202010522123.7A CN202010522123A CN111671396A CN 111671396 A CN111671396 A CN 111671396A CN 202010522123 A CN202010522123 A CN 202010522123A CN 111671396 A CN111671396 A CN 111671396A
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eeg signal
dream
sleeper
sleep
signals
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王晓岸
卢树强
马鹏程
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Beijing Brain Up Technology Co ltd
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Beijing Brain Up Technology Co ltd
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4812Detecting sleep stages or cycles
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/725Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

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Abstract

The invention discloses a sleep dream feedback method and device based on an EEG signal. The system comprises three parts of a specially-made EEG signal monitoring device, an EEG signal processing system and a dream analysis algorithm. The working principle of the platform is as follows: (1) the method comprises the following steps that specially-made EEG signal monitoring equipment collects brain wave data of a sleeper and transmits the brain wave data to a signal processing system in real time; (2) the signal processing system receives the data, decodes and preprocesses the data, and then identifies the signal through a brain wave data machine learning algorithm; (3) when the system identifies the signal data characteristics of the sleeper entering the dream, the signal data characteristics are fed back to the special EEG signal monitoring equipment; (4) the device prompts the sleeper for the stimulation of the dreaming state. The invention is designed according to the characteristics of the sleep dream EEG signal, realizes the stimulation prompt for the sleeper to enter the dream, and leads the sleeper to realize the state of entering the dream.

Description

Sleep dream feedback method based on EEG signal
Technical Field
The invention belongs to the technical field of EEG signal identification, and particularly relates to a sleep dream feedback method based on an EEG signal.
Background
The dream is the expression of the desire of the brain during the rest time, when a person enters a sleeping state or a relaxing state, the desire hidden in the subconscious mind can secretly emerge from the consciousness level and express the person in various images, and thus the dream is formed. But we can not know the state of entering the dream and even forget the dream content when waking up. With continuous website of EEG signal identification technology, sleep brain wave monitoring technology is more and more mature, and sleep stages of sleepers can be judged according to characteristics of different brain waves in sleep states.
The sleep EEG monitoring technology has the problems that no method for feeding back dreams to sleepers and prompting the sleepers to enter dreams is available in the prior art, and the sleepers cannot be helped to perceive their dreams only by recording the sleeping states of the sleepers.
Disclosure of Invention
The invention aims to provide a sleep dream feedback method based on an EEG signal, which aims to solve the problem that the sleep EEG signal monitoring technology in the prior art does not feed back a sleeper so as to remind the sleeper of entering a dream state. The method is designed based on the sleep EEG signal characteristics, can monitor the brain wave signals of the sleeper, distinguish different sleep stages, and give a prompt and feedback when the sleeper enters the dream state, so that the sleeper perceives that the sleeper enters the dream state.
In order to achieve the above purpose, the invention provides the following technical scheme: a sleep dream feedback method based on EEG signal is disclosed. The method comprises the steps of acquisition, transmission, discrimination and feedback of the sleep dream EEG signal, and the step applies three parts of specially-made EEG signal monitoring equipment, an EEG signal processing system and a dream analysis algorithm. The method comprises the following steps of collecting, transmitting, judging and feeding back EEG signals based on the sleep dream:
the first step is as follows: collecting electroencephalogram signals through a special EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the sleep signals reach the dream features;
the fourth step: when the monitored signal reaches the dream characteristics, a stimulation instruction is fed back to the special EEG signal monitoring equipment to stimulate the sleep vision so as to remind the sleeper of entering the dream state.
Preferably: the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: electrode arrays of special EEG signal monitoring equipment worn by a sleeper are positioned on 4 forehead parts and 1 each of left and right ear parts of a head and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
Preferably: the third step of carrying out signal decoding, preprocessing, feature extraction and analysis discrimination comprises the following calculation processes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different sleep stages;
the fifth step: by establishing and continuously perfecting parameter feature libraries at different sleep stages, whether real-time brain wave signal feature parameters reach threshold values of the dream stage or not is contrastively analyzed.
Preferably: the fourth step is that the feedback step for reminding the sleeper to enter the dream state comprises the following specific processes:
the first step is as follows: the EEG signal analysis system analyzes the characteristics of the brain wave signals of the sleeper in real time, and transmits a stimulation instruction to a special EEG signal monitoring device when the characteristic parameters reach a dreaming stage threshold;
the second step is that: after the special EEG signal monitoring equipment receives the stimulation instruction, the light source of the equipment is controlled to be lightened to give visual stimulation to the sleeper, so that the sleeper realizes that the sleeper enters a dream state;
the third step: if the characteristic parameters of the brain wave signals of the sleeper are analyzed in real time and continuously kept at the threshold of the dream stage, the stimulation instruction is continuously transmitted to the special EEG signal monitoring equipment, and the visual stimulation is continuously carried out;
the fourth step: if the characteristic parameters of the brain wave signals of the sleeper deviate from the threshold of the dream stage, the EEG signal analysis system stops transmitting the stimulation instructions to the special EEG signal monitoring equipment and stops the visual stimulation.
Compared with the prior art, the invention has the beneficial effects that:
(1) the sleep EEG signal characteristic-based sleep identification method is designed based on the sleep EEG signal characteristics, the new sleep brain wave model is analyzed by analyzing signal data through a machine learning algorithm, and the signal data characteristics of the sleep in the dream state can be rapidly and accurately judged;
(2) according to the specially-made EEG signal monitoring equipment, the electrode array arrangement is reasonably carried out, so that enough brain wave information can be obtained, and the reliability of EEG signal monitoring is ensured;
(3) the invention realizes the feedback and prompt of the sleeping person entering the dream state through the designed controllable light source, and can lead the sleeping person to perceive the sleeping person entering the dream in real time.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of a tailored EEG signal monitoring device of the present invention;
FIG. 3 is a schematic diagram of the sleep EEG signal monitoring, transmission and dream feedback reminder of the present invention;
FIG. 4 is a flow chart of an application of the EEG signal processing system of the present invention for parsing, processing and analysis;
FIG. 5 is a schematic diagram of the dream analysis algorithm of the present invention;
FIG. 6 is a graphical representation of the features of the EEG signal profile for different stages of sleep in accordance with 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a technical solution: a sleep dream feedback method based on EEG signal includes a set of steps of collection, transmission, discrimination and feedback of sleep dream EEG signal, which is specifically:
the first step is as follows: collecting electroencephalogram signals through a special EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the sleep signals reach the dream features;
the fourth step: when the monitored signal reaches the dream characteristics, a stimulation instruction is fed back to the special EEG signal monitoring equipment to stimulate the sleep vision so as to remind the sleeper of entering the dream state.
In this embodiment, preferably, the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: as shown in fig. 2, the electrode arrays of the special EEG signal monitoring equipment worn by the sleeper are located at 4 forehead and 1 each of the left and right ears of the head, and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
In this embodiment, preferably, as shown in fig. 3 to 5, the third step of performing the signal decoding, preprocessing, feature extraction, and analysis and discrimination steps includes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different sleep stages;
the fifth step: by establishing and continuously perfecting parameter feature libraries at different sleep stages, whether real-time brain wave signal feature parameters reach threshold values of the dream stage or not is contrastively analyzed.
In this embodiment, preferably, as shown in fig. 6, the fourth feedback step of reminding the sleeper to enter the dream state includes the following specific processes:
the first step is as follows: the EEG signal analysis system analyzes the characteristics of the brain wave signals of the sleeper in real time, and transmits a stimulation instruction to a special EEG signal monitoring device when the characteristic parameters reach a dreaming stage threshold;
the second step is that: after the special EEG signal monitoring equipment receives the stimulation instruction, the light source of the equipment is controlled to be lightened to give visual stimulation to the sleeper, so that the sleeper realizes that the sleeper enters a dream state;
the third step: if the characteristic parameters of the brain wave signals of the sleeper are analyzed in real time and continuously kept at the threshold of the dream stage, the stimulation instruction is continuously transmitted to the special EEG signal monitoring equipment, and the visual stimulation is continuously carried out;
the fourth step: if the characteristic parameters of the brain wave signals of the sleeper deviate from the threshold of the dream stage, the EEG signal analysis system stops transmitting the stimulation instructions to the special EEG signal monitoring equipment and stops the visual stimulation.

Claims (4)

1. The sleep dream feedback method based on the EEG signal is characterized in that: the method comprises the steps of collecting, transmitting, distinguishing and feeding back the sleep dream EEG signal, wherein the steps of collecting, transmitting, distinguishing and feeding back the sleep dream EEG signal are as follows:
the first step is as follows: collecting electroencephalogram signals through a special EEG signal monitoring device;
the second step is that: amplifying and coding the signals, and transmitting the signals to an EEG signal analysis system;
the third step: the EEG signal analysis system decodes, preprocesses and extracts features, and applies a machine learning algorithm to analyze signals to judge whether the sleep signals reach the dream features;
the fourth step: when the monitored signal reaches the dream characteristics, a stimulation instruction is fed back to the special EEG signal monitoring equipment to stimulate the sleep vision so as to remind the sleeper of entering the dream state.
2. The EEG signal-based sleep dream feedback method of claim 1, wherein: the specific flow of the signal acquisition and transmission step in the first step and the second step is as follows:
the first step is as follows: electrode arrays of special EEG signal monitoring equipment worn by a sleeper are positioned on 4 forehead parts and 1 each of left and right ear parts of a head and are respectively used for acquiring forehead brain wave signals and reference brain wave signals;
the second step is that: the specially-made EEG signal monitoring equipment amplifies and codes signals through an external transmission part thereof, integrates the signals into high-frequency digital signals and transmits the high-frequency digital signals to an EEG signal analysis system.
3. The EEG signal-based sleep dream feedback method of claim 1, wherein: the third step of carrying out signal decoding, preprocessing, feature extraction and analysis discrimination comprises the following calculation processes:
the first step is as follows: decoding the high-frequency digital signal to restore the high-frequency digital signal into a multi-channel brain wave signal;
the second step is that: filtering and denoising the signal by a regression method, a self-adaptive filtering method and an independent component analysis method, and removing interference and noise;
the third step: performing time domain and frequency domain parameter extraction and characteristic change on the processed brain wave data, and classifying;
the fourth step: calculating the characteristic combination of a time domain and a frequency domain by using a random forest machine learning algorithm to obtain parameters of different sleep stages;
the fifth step: by establishing and continuously perfecting parameter feature libraries at different sleep stages, whether real-time brain wave signal feature parameters reach threshold values of the dream stage or not is contrastively analyzed.
4. The EEG signal-based sleep dream feedback method of claim 1, wherein: the fourth step is that the feedback step for reminding the sleeper to enter the dream state comprises the following specific processes:
the first step is as follows: the EEG signal analysis system analyzes the characteristics of the brain wave signals of the sleeper in real time, and transmits a stimulation instruction to a special EEG signal monitoring device when the characteristic parameters reach a dreaming stage threshold;
the second step is that: after the special EEG signal monitoring equipment receives the stimulation instruction, the light source of the equipment is controlled to be lightened to give visual stimulation to the sleeper, so that the sleeper realizes that the sleeper enters a dream state;
the third step: if the characteristic parameters of the brain wave signals of the sleeper are analyzed in real time and continuously kept at the threshold of the dream stage, the stimulation instruction is continuously transmitted to the special EEG signal monitoring equipment, and the visual stimulation is continuously carried out;
the fourth step: if the characteristic parameters of the brain wave signals of the sleeper deviate from the threshold of the dream stage, the EEG signal analysis system stops transmitting the stimulation instructions to the special EEG signal monitoring equipment and stops the visual stimulation.
CN202010522123.7A 2020-06-11 2020-06-11 Sleep dream feedback method based on EEG signal Pending CN111671396A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113180694A (en) * 2021-04-07 2021-07-30 北京脑陆科技有限公司 Data real-time labeling method and system based on EEG signal
CN113208621A (en) * 2021-04-06 2021-08-06 北京脑陆科技有限公司 Dreaming interaction method and system based on EEG signal
CN113208627A (en) * 2021-04-07 2021-08-06 北京脑陆科技有限公司 Dreaming environment discrimination method and system based on electroencephalogram EEG signals

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US20160106950A1 (en) * 2014-10-19 2016-04-21 Curzio Vasapollo Forehead-wearable light stimulator having one or more light pipes
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CN108837271A (en) * 2018-03-26 2018-11-20 广东欧珀移动通信有限公司 The output method and Related product of electronic device, prompt information
CN109770896A (en) * 2019-01-08 2019-05-21 平安科技(深圳)有限公司 Dreamland image reproducing method, device and storage medium, server

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160106950A1 (en) * 2014-10-19 2016-04-21 Curzio Vasapollo Forehead-wearable light stimulator having one or more light pipes
CN105078436A (en) * 2015-09-11 2015-11-25 上海卓易科技股份有限公司 Sleep monitoring system and method capable of preventing nightmare
CN105797270A (en) * 2016-03-07 2016-07-27 宁波力芯科信息科技有限公司 Smart system and method for promoting sleep quality
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CN109770896A (en) * 2019-01-08 2019-05-21 平安科技(深圳)有限公司 Dreamland image reproducing method, device and storage medium, server

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113208621A (en) * 2021-04-06 2021-08-06 北京脑陆科技有限公司 Dreaming interaction method and system based on EEG signal
CN113180694A (en) * 2021-04-07 2021-07-30 北京脑陆科技有限公司 Data real-time labeling method and system based on EEG signal
CN113208627A (en) * 2021-04-07 2021-08-06 北京脑陆科技有限公司 Dreaming environment discrimination method and system based on electroencephalogram EEG signals

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