WO2024242582A1 - Procédé de suivi des états psycho-émotionnels d'un utilisateur, et de leur correction - Google Patents

Procédé de suivi des états psycho-émotionnels d'un utilisateur, et de leur correction Download PDF

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Publication number
WO2024242582A1
WO2024242582A1 PCT/RU2023/000167 RU2023000167W WO2024242582A1 WO 2024242582 A1 WO2024242582 A1 WO 2024242582A1 RU 2023000167 W RU2023000167 W RU 2023000167W WO 2024242582 A1 WO2024242582 A1 WO 2024242582A1
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WIPO (PCT)
Prior art keywords
alpha
user
seconds
states
individual
Prior art date
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PCT/RU2023/000167
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English (en)
Russian (ru)
Inventor
Евгений Олегович САВИЦКИЙ
Алексей Сергеевич ЗУБАРЕВ
Эрнест Эдемович АСАНОВ
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"neiry" LLC
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"neiry" LLC
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Publication date
Priority claimed from RU2023113057A external-priority patent/RU2814781C1/ru
Application filed by "neiry" LLC filed Critical "neiry" LLC
Publication of WO2024242582A1 publication Critical patent/WO2024242582A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • 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
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • 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]
    • A61B5/375—Electroencephalography [EEG] using biofeedback
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training

Definitions

  • This technical solution relates to the field of computer technology, in particular, to a method for tracking the psycho-emotional states of the user and their correction.
  • a solution selected as the closest analogue, WO2022169376A1 is known from the prior art.
  • This invention discloses a hardware and software complex for improving the user's cognitive functions, comprising: a computing device configured to display information and containing a client application; a neurocomputer interface containing a neuroamplifier; a software interface configured to control the operating modes of the neuroamplifier, namely configured to: connect to the neuroamplifier and activate commands for obtaining electrode resistances and/or for removing and recording data from an electroencephalogram of the brain (EEG); a server contained in the software interface and including: a communication module with the neurocomputer interface; a software interface core; an EEG signal classification module; a module for sending data to the client application.
  • EEG electroencephalogram of the brain
  • the proposed technical solution is aimed at eliminating the shortcomings of the current level of technology and differs from known solutions in that the proposed method ensures high-quality and accurate tracking of the user's psycho-emotional states.
  • This solution uses an algorithm for cleaning the EEG signal from myographic (muscle) artifacts, which allows using this system (method) outside laboratory conditions.
  • the technical problem that the claimed solution is aimed at solving is the creation of a method for tracking the psycho-emotional states of the user and correcting them. Additional embodiments of the present invention are presented in the dependent claims of the invention. The technical result consists of precise and high-quality tracking of the user’s psycho-emotional states, as well as the ability to correct them.
  • the claimed result is achieved by implementing a method for tracking the user's psycho-emotional states and correcting them, which includes the following stages: a portable neural interface, in the mode of taking data from the electroencephalogram of the brain (EEG), polls the user's brain; the collected EEG data of the user are transmitted via a Bluetooth communication channel to a computing device for further processing; the computing device receives EEG data from the neuroamplifier, after which it filters and preprocesses them; the filtered and preprocessed data are classified, while for calculating psycho-emotional and psychophysiological states, and their further detection and correction, an individual calibration mechanism is used, which includes: a cycle: 20 seconds of closed eyes, 20 seconds of open eyes, 20 seconds of closed eyes, 20 seconds of open eyes, 40 seconds of calm wakefulness; In this case, during the calibration process for calculating psycho-emotional and psychophysiological states, the following values are calculated: the value of the individual Alpha rhythm peak (iAPF); the value of Alpha rhythm peak suppression; individual Alpha range boundaries; calculation of the
  • filtering and pre-processing of data is carried out using a fourth-order Butterworth filter, which is used for basic filtering of incoming data; by using the fast Fourier transform (FFT), the power spectral density (PSD) of the main rhythms of the brain is calculated - Delta, Theta, Alpha, Beta; by using power and amplitude filtering, myographic artifacts are eliminated and the useful physiological EEG signal is isolated.
  • FFT fast Fourier transform
  • PSD power spectral density
  • the following values are calculated: the value of the individual Alpha rhythm peak (iAPF), defined as the greatest power in the range of Alpha rhythm measurements at a certain frequency; the value of the Alpha rhythm peak suppression, defined as the difference in power between the absolute peak in the Alpha range during the calibration stages with open and closed eyes; the individual Alpha range boundaries are defined as the first value for the high-frequency alpha range, and the last value for the low-frequency alpha range, where the difference in power with open and closed eyes will be positive.
  • iAPF individual Alpha rhythm peak
  • the individual Alpha range boundaries are defined as the first value for the high-frequency alpha range, and the last value for the low-frequency alpha range, where the difference in power with open and closed eyes will be positive.
  • the value of the frequency of the individual peak of the Alpha rhythm is determined as the frequency value in the range of Alpha rhythm measurements from 7 to 13 Hz, at which the power is greatest.
  • the values of the rhythm power and their ratios are accumulated in a cyclic buffer with a size of 5 seconds, until a total of 30 seconds of artifact-free values are accumulated, and the root mean square averaging method is used as the averaging method.
  • all measurement results are saved in a local database and after the end of the session, together with the raw EEG data, are sent to a remote web platform to track statistics and progress.
  • a portable neurointerface in the mode of taking data from the electroencephalogram of the brain brain (EEG), conducts surveys of the user's brain at a frequency of 250 Hz, for each of the electrodes with a set of 8 readings.
  • EEG electroencephalogram of the brain brain
  • This technical solution is intended for individual use and is created to analyze the user’s psycho-emotional state, as well as to correct (alleviate) negative conditions (with regular use).
  • the method for tracking the psycho-emotional states of the user and their correction is implemented by means of the following technical elements: a neurocomputer interface (can be implemented in various form factors) containing a neuroamplifier; a computing device that provides reception, filtering, analysis and processing of data coming from the neuroamplifier; a cloud web platform, as well as a local database (all measurement results after the end of the session, together with raw EEG data, are sent to a remote web platform to track statistics and progress).
  • a neurocomputer interface can be implemented in various form factors
  • a computing device that provides reception, filtering, analysis and processing of data coming from the neuroamplifier
  • a cloud web platform as well as a local database (all measurement results after the end of the session, together with raw EEG data, are sent to a remote web platform to track statistics and progress).
  • the target electrodes are leads T3, T4, 01, 02, with a reference electrode on the forehead.
  • the sampling frequency is 250 Hz.
  • the device must be turned on with the button and the Bluetooth module must be paired, according to the user manual, with the target device (computing device), then put on the head, ensuring that the reference electrode touches the skin of the forehead, and the impedance values of the target electrodes in the application interface are less than 1 MOhm (megaohm). After this, it is possible to switch to the EEG signal measurement mode.
  • a module responsible for displaying and entering user information on a computing device.
  • Modules communicate with each other via the C API.
  • a search for a neurointerface ready for connection via Bluetooth is performed upon request. If such a neurointerface is found, an indication of the quality of the electrode placement is displayed. If the quality is satisfactory and the resistance values for each electrode are less than 1000 kOhm, it becomes possible to switch the device to the EEG recording mode.
  • the neurointerface conducts surveys at a frequency of 250 Hz for each of the electrodes; when 8 readings are collected, they are packed into a BLE packet and sent via the Bluetooth communication channel to the computing device.
  • the kernel module implements buffering functionality, with data output for processing every 0.1 sec. After receiving EEG data from the buffer, filtering and transformation algorithms are applied to them:
  • FFT fast Fourier transform
  • PSD power spectral density
  • Classification of psycho-emotional states is possible after passing an individual calibration, which takes 2 minutes.
  • the user is asked to sit quietly and go through a cycle: 20 seconds of closed eyes, 20 seconds of open eyes, 20 seconds of closed eyes, 20 seconds of open eyes, 40 seconds of calm wakefulness.
  • the following parameters are measured: Artifact-free values of the Alpha, Beta, Theta rhythm powers, the value of the individual Alpha rhythm peak (iAPF), individual Alpha range boundaries, Alpha rhythm subrange powers. Measurements are made with a sliding window of 5 seconds, with subsequent averaging - separately for the calibration stage with closed eyes, separately for the stage with open eyes.
  • the Alpha, Beta, Theta rhythms of the brain are measured, including the A1 and A2 subranges, and the baseline level characteristic of the given user is calculated (Baseline).
  • Baseline calculation when calculating Baseline, the values of rhythm power and their ratios are accumulated in a cyclic buffer with a size of 5 seconds, until a total of 30 seconds of artifact-free values are accumulated, the mean square averaging method is used as the averaging method. The 75th percentile of the resulting numerical series is taken as the Baseline value.
  • the following values are displayed in the user interface: the iAPF value and a description of the values.
  • This technical solution allows the user to monitor their condition in real time in monitoring mode.
  • the following metrics are available to the user on the display device: - Cognitive Score (cognitive load level in percent) - values in real time or as a graph for a period of up to 60 minutes.
  • the application Based on the detected states, the application displays notifications with recommendations for undergoing biofeedback training (different for different negative states).
  • the training helps users relieve stress and experience deep relaxation, suitable for users who find it difficult to relax.
  • the training allows the user to gradually learn to concentrate and not be distracted by external stimuli, suitable for users who easily relax, but relaxation is easily knocked down.
  • the trainings have a similar structure: they consist of three levels, with each level becoming more difficult to complete, and each level requires a total of 100 points. At the end of each training, the user is shown the result in the interface - the maximum number of points scored by the user during the training.
  • the module contains a receiving buffer for data coming via the Bluetooth channel to ensure a constant delay of 4 ms (for a sampling frequency of 250 Hz) when outputting data to the state classification modules.
  • the module also contains filters for clearing the signal from interference and a module for calculating Band Powers (power spectral density values). After signal cleaning and calculating the powers, the data is sent to the state classification modules for further processing and output to the display device.
  • Mind Tracker BCI An additional advantage of the system for detecting psycho-emotional states and correcting them (Mind Tracker BCI) is its flexible architecture: it can be integrated with various interfaces and various signal classifiers.
  • the value of the individual peak of the Alpha rhythm is defined as the greatest power in the range of Alpha rhythm measurements at a certain frequency (in a particular embodiment of the described method, the value of the frequency of the individual peak of the Alpha rhythm (iAPF) is defined as the frequency value in the range of Alpha rhythm measurements from 7 to 13 Hz, at which the power is greatest);
  • the magnitude of the suppression of the Alpha rhythm peak is defined as the difference in power between the absolute peak in the Alpha range during the calibration stages with open and closed eyes;
  • the power spectral density (PSD) of the ranges of the main rhythms of the brain, as well as their subranges, are measured using a sliding window of 5 seconds.
  • FS (a + 0) / p, where a is the power in the Alpha rhythm range, 0 is the power in the Theta rhythm range, and the power in the Beta rhythm range.
  • Alpha Gravity Index (GS) A2/A1, where A2 is the power in the Alpha rhythm sub-range from the iAPF value to the upper limit, and A1 is the power in the Alpha rhythm sub-range from the iAPF value to the lower limit.
  • Concentration index B/A, where B is the power in the Beta rhythm subrange, and A is the power in the Alpha rhythm subrange
  • the metric values with decoding are displayed to the user for monitoring.
  • iAPF Cognitive Score graph (scale from 0 to 100%), indication of psycho-emotional states.
  • iAPF - values from 7 to 13 Hz
  • Cognitive Score - a graph of involvement in the presented task (productivity graph): for users with low (iAPF ⁇ 10) it is calculated based on the fatigue index, with correction factors; for users with high alpha frequency (iAPF>10) it is calculated based on the Alpha Gravity Index (GS), with correction factors.
  • Indication of psycho-emotional states is implemented in the form of indicators: Relaxation, Involvement, Mild fatigue, Moderate fatigue, Chronic fatigue, Anxiety, Stress. State indicators become active as soon as the system algorithm determines them.
  • the algorithm recognizes each state individually, based on the values of the indices described above.
  • the mechanics are based on auditory feedback, which depends on maintaining the level of its Alpha peak, determined at the calibration stage (the amount of Alpha rhythm suppression). When maintaining relaxation, the sounds of nature are clearly expressed, but when falling, extraneous sounds of artificial origin are mixed in (for example, the sounds of a highway, a construction site, etc.)
  • All measurement results are sent and saved to a local Postgress SQL database and after the end of the session, together with the raw EEG data, are sent to a remote web platform, which allows you to track statistics and progress when reusing the system.
  • the proposed technical solution allows tracking metrics and states “at the moment”, as well as session-wise - keeping statistics by days, weeks, months.
  • recommendations are displayed on the display device to help get rid of such states (recommendations for planning the “work/rest” mode, or recommendations for completing training).

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Pathology (AREA)
  • Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Hospice & Palliative Care (AREA)
  • Developmental Disabilities (AREA)
  • Child & Adolescent Psychology (AREA)
  • Social Psychology (AREA)
  • Educational Technology (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne un procédé de suivi des états psycho-émotionnels d'un utilisateur. On effectue un sondage du cerveau de l'utilisateur en mode de collecte de données EEG à l'aide d'une neuro-interface portative. Les données EEG collectées sont transmises par un canal de communication Bluetooth vers un dispositif informatique en vue de leur traitement ultérieur. Afin de calculer les états psycho-émotionnels et psycho-physiologiques et de les détecter ultérieurement, on utilise un mécanisme d'étalonnage individuel qui comprend le cycle suivant: 20 secondes yeux fermés, 20 secondes yeux ouverts, 20 secondes yeux fermés, 20 secondes yeux ouverts, 40 secondes de veille au repos. Lors de l'étalonnage pour calculer les états psycho-émotionnels et psycho-physiologiques, on calcule les valeurs suivantes: valeur du pic individuel de rythme alpha (IAPF); valeur de suppression du pic de rythme alpha; limites individuelles de la plage alpha; niveau de base d'indice de fatigue cognitive, indice de concentration, indice "alpha gravity" caractéristique pour un utilisateur donné (baseline). Les données de mesure déchiffrées sont présentées à un utilisateur pour le contrôle. Sur la base des états détectés, on présente à l'utilisateur une notification avec des recommandations sur le déroulement d'un entraînement avec une liaison biologique retour.
PCT/RU2023/000167 2023-05-19 2023-06-01 Procédé de suivi des états psycho-émotionnels d'un utilisateur, et de leur correction Ceased WO2024242582A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
RU2023113057A RU2814781C1 (ru) 2023-05-19 Способ для отслеживания психоэмоциональных состояний пользователя и их коррекции
RU2023113057 2023-05-19

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106725462A (zh) * 2017-01-12 2017-05-31 兰州大学 基于脑电信号的声光睡眠干预系统和方法
US20180242919A1 (en) * 2017-02-24 2018-08-30 Cerebral Diagnostics Canada Incorporated Diagnosis of migraine via expert system
US20220047204A1 (en) * 2018-09-24 2022-02-17 JÓHANNSSON Magnús Methods, Computer-Readable Media and Devices for Producing an Index
WO2022169376A1 (fr) 2021-02-05 2022-08-11 Общество С Ограниченной Ответственностью "Нейри" Complexe matériel-logiciel pour améliorer les fonctions cognitives d'un utilisateur

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106725462A (zh) * 2017-01-12 2017-05-31 兰州大学 基于脑电信号的声光睡眠干预系统和方法
US20180242919A1 (en) * 2017-02-24 2018-08-30 Cerebral Diagnostics Canada Incorporated Diagnosis of migraine via expert system
US20220047204A1 (en) * 2018-09-24 2022-02-17 JÓHANNSSON Magnús Methods, Computer-Readable Media and Devices for Producing an Index
WO2022169376A1 (fr) 2021-02-05 2022-08-11 Общество С Ограниченной Ответственностью "Нейри" Complexe matériel-logiciel pour améliorer les fonctions cognitives d'un utilisateur

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