WO2022205073A1 - 基于内源性脑信号的闭环神经调控系统、方法和设备 - Google Patents

基于内源性脑信号的闭环神经调控系统、方法和设备 Download PDF

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WO2022205073A1
WO2022205073A1 PCT/CN2021/084424 CN2021084424W WO2022205073A1 WO 2022205073 A1 WO2022205073 A1 WO 2022205073A1 CN 2021084424 W CN2021084424 W CN 2021084424W WO 2022205073 A1 WO2022205073 A1 WO 2022205073A1
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concentration information
regulation
signal
band
eeg
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French (fr)
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刘浩
蒋田仔
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中国科学院自动化研究所
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A61B5/14553—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases specially adapted for cerebral tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/36025—External stimulators, e.g. with patch electrodes for treating a mental or cerebral condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/3603—Control systems
    • A61N1/36031—Control systems using physiological parameters for adjustment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N2/00—Magnetotherapy
    • A61N2/004—Magnetotherapy specially adapted for a specific therapy
    • A61N2/006—Magnetotherapy specially adapted for a specific therapy for magnetic stimulation of nerve tissue

Definitions

  • the invention belongs to the fields of neuroscience and electromagnetism, and specifically relates to a closed-loop neural regulation system, method and device based on endogenous brain signals.
  • Brain neuromodulation technology is an important method to change the transmission of endogenous nerve signals in the brain through invasive or non-invasive means, using physical or chemical means such as light, magnetism, electricity, ultrasound, etc., to cause changes in brain function.
  • Neuromodulation can not only cause changes in neuron structure in a relatively short period of time, but also a specially designed regulatory paradigm can also bring about changes in functional circuits, thereby restoring or improving the plasticity of synaptic connections. It is a powerful tool for elucidating the causal relationship between brain function and behavior, and it is also an important means for the treatment of clinical neurological diseases.
  • the existing neuromodulation technology is still in the stage of technological breakthrough and development. Due to the fixed stimulation parameters, the single stimulation process, and the difficulty in quantitative evaluation of the stimulation effect, the neuromodulation technology currently used in clinical practice is often open-loop control and does not realize individualized closed-loop for patients. There are problems such as limited indications, poor stability of treatment and obvious individual differences.
  • the effect of non-invasive neuromodulation varies with different brain states, which suggests that the shortcomings of our current open-loop neuromodulation are probably due to the lack of individual endogenous brain signals for regulation, and the parameters of neuromodulation need to be combined with Dynamic control of brain function activity over time.
  • Brain functional activities include multiple processes such as neuronal activity and local energy metabolism.
  • the complex functional activities allow the brain to gather information from multiple modalities, the most important of which are the electrical activity of neurons and the changes in blood oxygen metabolism in activated areas. Only by realizing the effective extraction, analysis and fusion of these two kinds of information, can the brain function activities be organically linked.
  • the photoelectric synchronous brain activity detector can simultaneously detect the EEG signal and the cerebral blood oxygen signal to analyze the brain function signal from two dimensions. Up to now, there is no photoelectric synchronous brain activity detector to evaluate the brain state in real time, and use this to control the parameters of neural regulation to achieve the purpose of individualized closed-loop regulation.
  • the present invention provides a closed-loop neural regulation system based on endogenous brain signals, including brain signals Acquisition module, regulation processing module and regulation parameter optimization module;
  • the brain signal acquisition module is configured to acquire a multimodal brain signal in real time through a brain signal acquisition device;
  • the multimodal brain signal includes the blood oxygen level signal fNIRS and the EEG signal EEG;
  • the regulation and processing module includes a resting stage unit, a regulation stage unit and an evaluation stage unit;
  • the resting stage unit is configured to decode and obtain resting deoxyhemoglobin concentration information HbR, resting oxyhemoglobin concentration information HbO, resting stage frequency points and EEG of each sub-band of EEG based on the multimodal brain signal
  • the resting phase amplitude of each sub-band, and the sub-band correction is performed to generate individualized sub-bands;
  • the regulation stage unit is configured to obtain the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO by decoding based on the multimodal brain signal, and obtain the regulation deoxyhemoglobin through bandpass filtering and averaging processing.
  • Concentration information and regulated oxyhemoglobin concentration information intercept the latest ms EEG signal based on the multimodal brain signal, and perform band-pass filtering, fast Fourier transform and Hilber on individual sub-frequency bands for each sub-frequency band
  • the special transformation calculates the control stage amplitude and control stage phase of each sub-band; n and m are non-negative numbers;
  • the amplitude control signal stimulated by the regulating device is set based on the regulating deoxyhemoglobin concentration information and the regulating oxyhemoglobin concentration information, the regulating time at which the control signal is generated based on the amplitude control signal and the regulating phase phase, and the amplitude value stimulated by the regulating device and control time to generate control signals, and output control signals through control equipment;
  • the evaluation stage unit after the regulation stage unit completes the regulation, based on the multimodal brain signal, decodes to obtain the evaluation deoxyhemoglobin concentration information, the evaluation oxyhemoglobin concentration information, the evaluation stage frequency points of each sub-band and each sub-band.
  • the control parameter optimization module is configured to adjust the control parameters based on the blood oxygen control effect and the EEG control effect.
  • the resting phase unit comprises:
  • It is configured to perform bandpass filtering of 0.01Hz-0.2Hz on the deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO, and averagely process the 4-channel cerebral blood oxygen information to obtain resting deoxyhemoglobin concentration information HbR rest and resting oxyhemoglobin concentration information HbO rest ;
  • the EEG signal EEG is subjected to 0.5Hz-100Hz band-pass filtering and 50Hz notch processing, and is divided into delta frequency band, theta frequency band, alpha frequency band and beta frequency band according to the EEG frequency standard, and is calculated by fast Fourier transform. Resting phase amplitude of each sub-band and And calculate the frequency point of the resting stage with the highest amplitude of each sub-band and Modify each sub-band to obtain individualized sub-bands and
  • the regulation stage unit comprises:
  • It is configured to intercept the blood oxygen level signal fNIRS through the rectangular window or Hanning window of ns, and obtain the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO, based on the latest ns deoxyhemoglobin concentration information HbR and oxygenation
  • the hemoglobin concentration information HbO is subjected to band-pass filtering of 0.01Hz-0.2Hz and the average processing of 4 channels of cerebral blood oxygen information to obtain regulation deoxyhemoglobin concentration information HbR now and regulation oxyhemoglobin concentration information HbO now ;
  • the trigger threshold is determined based on the cerebral blood oxygen amplitude control signal relative to the baseline and the EEG amplitude control signal relative to the baseline, the phase control signal is obtained based on the phase of the regulation phase, and then the regulation time is obtained, and the regulation is generated according to the trigger threshold and the regulation time. Signal;
  • the cerebral blood oxygen amplitude control signal relative to the baseline is Among them, A and B are the parameters to be adjusted, and the value range is [-1, 1];
  • the EEG amplitude control signal relative to the baseline is Among them, C, D, E and F are the parameters to be adjusted, and the value range is [-1, 1];
  • the phase control signal is Among them, G, H, I and J are preset parameters, and the value range is [-1, 1];
  • Described regulation signal Dev Modu is:
  • Dev base is the preset reference value of the stimulus intensity of the control equipment
  • P is the parameter to be adjusted
  • the value range is [-1, 1].
  • the evaluation stage unit includes:
  • It is configured to perform bandpass filtering of 0.01Hz-0.2Hz on the deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO, and perform averaging processing of 4 channels of cerebral blood oxygen information to obtain the evaluation deoxyhemoglobin concentration information HbR post and evaluation Oxyhemoglobin concentration information HbO post ;
  • Bandpass filtering of 0.5Hz-100Hz and notch processing of 50Hz are performed on the EEG signal, and the and The band-pass filtering is performed, and then the fast Fourier transform is performed in the four sub-bands to calculate the frequency points in the evaluation stage. and and the corresponding evaluation stage amplitude and
  • R and S are the parameters to be adjusted, and the value range is [-1, 1];
  • the brain signal acquisition module includes:
  • the multimodal brain signals are collected at the same time by the photoelectric synchronous brain activity detector NEG.
  • the electrodes and optodes of the photoelectric synchronous brain activity detector are arranged according to the NEG-32 or NEG-8 collection caps; the fNIRS optical channel is arranged around the electrodes, 32 or 8-channel caps cover the whole brain or specific regions.
  • the modulation device comprises a transcranial magnetic stimulation device TMS or a transcranial electrical stimulation device tES.
  • the resting phase unit and the evaluation phase unit both last for 3 minutes, the user keeps the eyes closed or open in the non-task state and the regulating device is in the off state; the regulating phase unit lasts for 3 minutes. For 30 minutes, the control device is turned on, and the user keeps his eyes open or task state.
  • Another aspect of the present invention provides a closed-loop neuromodulation method based on endogenous brain signals, the method comprising:
  • Step S100 acquiring a multimodal brain signal in real time through a brain signal acquisition device;
  • the multimodal brain signal includes a blood oxygen level signal fNIRS and an EEG signal EEG;
  • Step S200 which specifically includes steps S210-S230;
  • Step S210 decode and obtain resting deoxyhemoglobin concentration information HbR, resting oxyhemoglobin concentration information HbO, resting phase frequency points of each sub-band of EEG and resting phase of each sub-band of EEG based on the multimodal brain signal stage amplitude, and perform sub-band correction to generate individualized sub-bands;
  • step S220 the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO are obtained by decoding based on the multimodal brain signal, and the regulated deoxyhemoglobin concentration information and the regulated oxygen concentration are obtained by bandpass filtering and average processing.
  • Combined hemoglobin concentration information based on the multimodal brain signal, intercept the latest ms EEG signal, and perform bandpass filtering, fast Fourier transform and Hilbert transform on each subband to calculate each subband.
  • the amplitude and phase of the regulation stage of the frequency band; n and m are non-negative numbers;
  • the amplitude control signal stimulated by the regulating device is set based on the regulating deoxyhemoglobin concentration information and the regulating oxyhemoglobin concentration information, the regulating time at which the control signal is generated based on the amplitude control signal and the regulating phase phase, and the amplitude value stimulated by the regulating device and control time to generate control signals, and output control signals through control equipment;
  • Step S230 after the control stage unit completes the control, based on the multimodal brain signal, decoding is performed to obtain the evaluation deoxyhemoglobin concentration information, the evaluation oxyhemoglobin concentration information, the evaluation stage frequency points of each sub-band and the evaluation of each sub-band. stage amplitude;
  • Step S300 adjusting the regulation parameters based on the blood oxygen regulation effect and the EEG regulation effect.
  • an electronic device which is characterized by comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, the memory stores data that can be processed by the processor.
  • the instructions are executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned closed-loop neuromodulation method based on endogenous brain signals.
  • a computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to realize the invention of claim 8.
  • a closed-loop neuromodulation method based on endogenous brain signals.
  • the closed-loop neural regulation system based on the endogenous brain signal proposed by the present invention realizes the closed-loop dynamic control of the endogenous brain signal through the real-time acquisition of the endogenous brain signal and synchronously adjusts the regulation parameters of the external regulation device. source brain signals.
  • the closed-loop neural regulation system based on endogenous brain signals collects multimodal endogenous brain signals through a photoelectric synchronous brain activity detector, and integrates the collected signals with high temporal resolution and high spatial resolution. , improving the accuracy of brain state assessment and making neural regulation more precise.
  • the closed-loop neural regulation system based on endogenous brain signals proposed by the present invention can quantitatively change regulation parameters in real-time with multiple modal brain signals, which is convenient and easy to use, and realizes integrated brain activity synchronous acquisition and individual regulation.
  • FIG. 1 is a structural block diagram of a closed-loop regulation system based on endogenous brain signals according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of the principle of a closed-loop regulation system based on endogenous brain signals according to an embodiment of the present invention
  • Fig. 3 is the photoelectric synchronous brain activity detector NEG equipment used in the embodiment of the present invention.
  • Fig. 4 is a kind of electrode and optode arrangement of NEG equipment in the embodiment of the present invention.
  • Fig. 5 is the multimodal brain signal collected by NEG equipment in the embodiment of the present invention.
  • FIG. 6 is a schematic diagram of an endogenous brain signal-based closed-loop regulation system applied to a transcranial magnetic stimulation device TMS in an embodiment of the present invention
  • the invention provides a closed-loop neural regulation system based on endogenous brain signals.
  • the system collects endogenous brain signals in real time and adjusts the regulation parameters of external regulation equipment synchronously, so as to realize the closed-loop dynamic control of inner and outer regulation by exogenous stimulation signals. source brain signals.
  • a closed-loop neural regulation system based on endogenous brain signals of the present invention includes a brain signal acquisition module, a regulation processing module and a regulation parameter optimization module;
  • the brain signal acquisition module is configured to acquire a multimodal brain signal in real time through a brain signal acquisition device;
  • the multimodal brain signal includes the blood oxygen level signal fNIRS and the EEG signal EEG;
  • the regulation and processing module includes a resting stage unit, a regulation stage unit and an evaluation stage unit;
  • the resting stage unit is configured to decode and obtain resting deoxyhemoglobin concentration information HbR, resting oxyhemoglobin concentration information HbO, resting stage frequency points and EEG of each sub-band of EEG based on the multimodal brain signal
  • the resting phase amplitude of each sub-band, and the sub-band correction is performed to generate individualized sub-bands;
  • the regulation stage unit is configured to obtain the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO by decoding based on the multimodal brain signal, and obtain the regulation deoxyhemoglobin through bandpass filtering and averaging processing. Concentration information and regulated oxyhemoglobin concentration information; based on the multimodal brain signal, the latest ms EEG signal is intercepted, and band-pass filtering, fast Fourier transform and Hilbert of individual sub-bands are performed on each sub-band. Transform and calculate the control stage amplitude and control stage phase of each sub-band; n and m are non-negative numbers;
  • the amplitude control signal stimulated by the regulating device is set based on the regulating deoxyhemoglobin concentration information and the regulating oxyhemoglobin concentration information, the regulating time at which the control signal is generated based on the amplitude control signal and the regulating phase phase, and the amplitude value stimulated by the regulating device and control time to generate control signals, and output control signals through control equipment;
  • the evaluation stage unit after the regulation stage unit completes the regulation, based on the multimodal brain signal, decodes to obtain the evaluation deoxyhemoglobin concentration information, the evaluation oxyhemoglobin concentration information, the evaluation stage frequency points of each sub-band and each sub-band.
  • the control parameter optimization module is configured to adjust the control parameters based on the blood oxygen control effect and the EEG control effect.
  • each functional module in the embodiment of the present invention will be described in detail below with reference to FIG. 1 .
  • the closed-loop neural regulation system based on endogenous brain signals includes a step brain signal acquisition module, a regulation processing module, and a regulation parameter optimization module.
  • Each functional module is described in detail as follows:
  • This embodiment applies the closed-loop neural regulation method based on endogenous brain signals to the closed-loop regulation of the motor area of TMS by transcranial magnetic stimulation;
  • the brain signal acquisition module is configured to acquire a multimodal brain signal in real time through a brain signal acquisition device;
  • the multimodal brain signal includes the blood oxygen level signal fNIRS and the EEG signal EEG;
  • the brain signal acquisition module includes:
  • the photoelectric synchronous brain activity detector NEG collects multimodal brain signals simultaneously.
  • the electrodes and optodes of the photoelectric synchronous brain activity detector are arranged according to the NEG-32 or NEG-8 collection caps, supporting 32 or 8
  • the fNIRS signal of the channel, 32 or 8 EEG channels are arranged.
  • the specific arrangement is shown in Figure 4.
  • the photoelectric synchronous brain activity detector of NEG-32 is used.
  • the device has 32 channels of EEG and 32 channels of near-infrared.
  • the arrangement of the collection caps is in the form of 10-20 arrangement
  • the EEG channels used are 9 channels such as FC1, FC3, FC5, C1, C3, C5, CP1, CP3, CP5, etc.
  • the used near-infrared channels are 4 channels consisting of two light sources S and two probes D
  • fNIRS light channels are arranged around the electrodes, and 32 or 8 channel caps cover the whole brain or specific regions.
  • the collected signal is shown in Figure 5.
  • the photoelectric synchronous brain activity detector of NEG-32 can directly collect the blood oxygen level signal fNIRS and the EEG signal EEG;
  • the regulation and processing module includes a resting stage unit, a regulation stage unit and an evaluation stage unit;
  • the resting stage unit and the evaluation stage unit both last for 3 minutes, the user keeps the eyes closed or open in the non-task state and the control device is in the closed state; the control stage unit lasts for 30 minutes , the control device is turned on, and the user keeps his eyes open or task state.
  • the resting stage unit is configured to decode and obtain resting deoxyhemoglobin concentration information HbR, resting oxyhemoglobin concentration information HbO, resting stage frequency points and EEG of each sub-band of EEG based on the multimodal brain signal
  • the resting phase amplitude of each sub-band, and the sub-band correction is performed to generate individualized sub-bands;
  • the resting stage specifically includes: configuring the deoxyhemoglobin concentration information HbR and the oxyhemoglobin concentration information HbO to perform bandpass filtering of 0.01Hz-0.2Hz, and averaging the cerebral blood oxygen information of the four channels Process to obtain resting deoxyhemoglobin concentration information HbR rest and resting oxyhemoglobin concentration information HbO rest ; the number of channels of blood oxygen information at this stage can be adjusted according to the device settings or user needs in specific application scenarios, which is not detailed here limited;
  • the 9 electrodes can be processed by Laplace spatial filtering
  • the EEG signal EEG is subjected to band-pass filtering of 0.5Hz-100Hz and notch processing of 50Hz, and is divided into delta frequency band (0.5-5Hz), ⁇ frequency band (5-8Hz), ⁇ frequency band (8 frequency bands) according to the EEG frequency standard. -12Hz) and ⁇ -band (12-30Hz), the resting phase amplitude of each sub-band is calculated by fast Fourier transform and And calculate the frequency point of the resting stage with the highest amplitude of each sub-band and Modify each sub-band to obtain individualized sub-bands and
  • the individualized sub-frequency bands can be adapted to the EEG signals of different individuals, so as to avoid the influence of individual differences on the effect of the present invention.
  • the regulation stage unit is configured to obtain the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO by decoding based on the multimodal brain signal, and obtain the regulation deoxyhemoglobin through bandpass filtering and averaging processing.
  • Concentration information and regulated oxyhemoglobin concentration information based on the multimodal brain signal, intercept the latest ms EEG signal EEG, and perform band-pass filtering, fast Fourier transform and Hill on individual sub-bands for each sub-band Bert transform calculates the control stage amplitude and control stage phase of each sub-band; n and m are non-negative numbers, in this embodiment, the preferred value of n is 3, and the value of m is 2;
  • the amplitude control signal stimulated by the regulating device is set based on the regulating deoxyhemoglobin concentration information and the regulating oxyhemoglobin concentration information, the regulating time at which the control signal is generated based on the amplitude control signal and the regulating phase phase, and the amplitude value stimulated by the regulating device and control time to generate control signals, and output control signals through control equipment;
  • the regulating device includes a transcranial magnetic stimulation device TMS or a transcranial electrical stimulation device tES.
  • the regulation stage unit includes: configured to intercept the blood oxygen level signal fNIRS through a rectangular window or a Hanning window of ns, and obtain the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO, based on the latest ns
  • the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO are band-pass filtered at 0.01Hz-0.2Hz and the 4-channel cerebral blood oxygen information is averaged to obtain the regulated deoxyhemoglobin concentration information HbR now and the regulated oxyhemoglobin concentration infoHbOnow ;
  • the trigger threshold is determined based on the cerebral blood oxygen amplitude control signal relative to the baseline and the EEG amplitude control signal relative to the baseline, the phase control signal is obtained based on the phase of the regulation phase, and then the regulation time is obtained, and the regulation is generated according to the trigger threshold and the regulation time. Signal;
  • the cerebral blood oxygen amplitude control signal relative to the baseline is Among them, A and B are parameters to be adjusted, and the value range is [-1, 1]; in this embodiment, in order to highlight the activation of the concerned motion area, preferably A is 1, and B is 0;
  • the EEG amplitude control signal relative to the baseline is Among them, C, D, E and F are the parameters to be adjusted, and the value range is [-1, 1]; in this embodiment, because the ⁇ rhythm of the brain motor area is obvious, preferably C, D, F are set to 0, and E is set to 1;
  • the phase control signal is Among them, G, H, I and J are preset parameters, and the value range is [-1, 1]; in this embodiment, due to the obvious alpha rhythm in the brain motor area, the preferred G, H, and J are set to 0 , I take 1;
  • the regulatory signal is:
  • the Dev base is the preset reference value of the stimulation intensity of the control device, P is the parameter to be adjusted, and the value range is [-1, 1]; in this embodiment, the Dev base of the TMS is based on the commonly used clinical resting motion threshold RMT Determine, the P value depends on the specific TMS equipment, preferably 0.5; according to actual observation or treatment needs, ANG modu can be set to other angles or angle ranges, and the control device can output arbitrary waveforms according to the control signal.
  • the control signal generated by the present invention combines the advantages of high temporal resolution of brain electrical signals and the advantages of high spatial resolution of blood oxygen level signals, so that the nerve regulation is more precise, can adapt to more symptoms, and improves the universality of nerve regulation; Due to individual differences, different users also have differences in the performance of cerebral blood oxygen signal and EEG signal. Some users have good cerebral blood oxygen signal performance but average EEG signal performance, or cerebral blood oxygen signal performance is average but EEG signal performance. The performance is good, and the problem that a single modal control method is difficult to deal with individual differences of users can be solved by the regulation method of the present invention that fuses two modal signals, that is, the present invention does not control the acquisition of the cerebral blood oxygen signal and the acquisition of the EEG signal. A simple combination of regulation.
  • the neural regulation method proposed by the present invention reduces the reduction of the brain voltage to a certain extent because it integrates the information extracted from the cerebral blood oxygen signal and the EEG signal for regulation.
  • the influence of EEG signal interference on regulation is reduced, and continuous and precise regulation is realized.
  • the evaluation stage unit after the regulation stage unit completes the regulation, based on the multimodal brain signal, decodes to obtain the evaluation deoxyhemoglobin concentration information, the evaluation oxyhemoglobin concentration information, the evaluation stage frequency points of each sub-band and each sub-band.
  • the evaluation stage unit includes: a band-pass filter configured to perform 0.01Hz-0.2Hz bandpass filtering on the deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO, and perform an average of 4 channels of cerebral blood oxygen information processing to obtain evaluation deoxyhemoglobin concentration information HbR post and evaluation oxyhemoglobin concentration information HbO post ;
  • Bandpass filtering of 0.5Hz-100Hz and notch processing of 50Hz are performed on the EEG signal, and the and The band-pass filtering is performed, and then the fast Fourier transform is performed in the four sub-bands to calculate the frequency points in the evaluation stage. and and the corresponding evaluation stage amplitude and
  • R and S are parameters to be adjusted, and the value range is [-1, 1]; in this embodiment, due to the activation of the motion area, preferably R is 1, and S is 0;
  • the control parameter optimization module is configured to adjust the control parameters based on the blood oxygen control effect and the EEG control effect; in the specific application process of the present invention, the parameters are artificially adjusted according to observation needs or actual adjustment needs, so as to achieve better good control effect.
  • the present invention collects real-time brain signals of users through a radio and television synchronous brain activity detector to obtain fNIRS signals and EEG signals, and processes the fNIRS signals through brain signals to obtain HbO signals and HbR signals; Sub-band phase and sub-band amplitude; extract sub-band phase and sub-band amplitude from HbO signal, HbR signal EEG signal to obtain regulation effect evaluation, optimize regulation parameters based on regulation effect evaluation, and then regulate stimulation in real time.
  • the closed-loop regulation system based on endogenous brain signals is applied to the transcranial magnetic stimulation device TMS as shown in Figure 6.
  • the system for closed-loop neural regulation based on endogenous brain signals provided in the above-mentioned embodiments is only illustrated by the division of the above-mentioned functional modules. That is, the modules or steps in the embodiments of the present invention are decomposed or combined. For example, the modules in the above-mentioned embodiments can be combined into one module, or can be further split into multiple sub-modules, so as to complete all the above descriptions. or some functions.
  • the names of the modules and steps involved in the embodiments of the present invention are only for distinguishing each module or step, and should not be regarded as an improper limitation of the present invention.
  • the closed-loop neural regulation method based on endogenous brain signals according to the second embodiment of the present invention, the specific steps include:
  • Step S100 acquiring a multimodal brain signal in real time through a brain signal acquisition device;
  • the multimodal brain signal includes a blood oxygen level signal fNIRS and an EEG signal EEG;
  • Step S200 which specifically includes steps S210-S230;
  • Step S210 decode and obtain resting deoxyhemoglobin concentration information HbR, resting oxyhemoglobin concentration information HbO, resting phase frequency points of each sub-band of EEG and resting phase of each sub-band of EEG based on the multimodal brain signal stage amplitude, and perform sub-band correction to generate individualized sub-bands;
  • step S220 the latest ns deoxyhemoglobin concentration information HbR and oxyhemoglobin concentration information HbO are obtained by decoding based on the multimodal brain signal, and the regulated deoxyhemoglobin concentration information and the regulated oxygen concentration are obtained by bandpass filtering and average processing.
  • Combined hemoglobin concentration information based on the EEG signal in the multimodal brain signal, individualized sub-band band-pass filtering, fast Fourier transform and Hilbert transform are performed on each sub-band to calculate the regulation of each sub-band Stage amplitude and regulation stage phase;
  • the amplitude control signal stimulated by the regulating device is set based on the regulating deoxyhemoglobin concentration information and the regulating oxyhemoglobin concentration information, the regulating time at which the control signal is generated based on the amplitude control signal and the regulating phase phase, and the amplitude value stimulated by the regulating device and control time to generate control signals, and output control signals through control equipment;
  • Step S230 after the control stage unit completes the control, based on the multimodal brain signal, decoding is performed to obtain the evaluation deoxyhemoglobin concentration information, the evaluation oxyhemoglobin concentration information, the evaluation stage frequency points of each sub-band and the evaluation of each sub-band. stage amplitude;
  • Step S300 adjusting the regulation parameters based on the blood oxygen regulation effect and the EEG regulation effect.
  • An electronic device is characterized by comprising: at least one processor; and a memory connected in communication with at least one of the processors; wherein, the memory stores data executable by the processor.
  • the instructions are used to be executed by the processor to implement the above-mentioned closed-loop neuromodulation method based on endogenous brain signals.
  • a computer-readable storage medium is characterized in that, the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to realize the above-mentioned endogenous-based Closed-loop neuromodulation of brain signals.

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Abstract

一种基于内源性脑信号的闭环神经调控系统、方法及设备，属于神经科学和电磁学领域，其包括：通过脑信号采集设备实时获取多模态脑信号，进而获得被试的脑部血氧水平信号和脑电信号，基于脑部血氧水平信号和脑电信号进行分析和融合生成调控信号对脑部神经进行调控，根据调控效果评价优化调控参数，进而实现外源性刺激信号闭环动态地控制内源性脑信号。通过实时采集内源性的脑信号，同步调整外部调控设备的调控参数，实现了外源性刺激信号对内源性脑信号的精确闭环控制，旨在解决现有的神经调控技术无法结合光电同步脑活动实时对神经进行调控的问题。

Description

基于内源性脑信号的闭环神经调控系统、方法和设备 技术领域
本发明属于神经科学和电磁学领域，具体涉及了一种基于内源性脑信号的闭环神经调控系统、方法及设备。
背景技术
脑神经调控技术是通过侵入性或非侵入性手段，利用光、磁、电、超声等物理或化学等外部技术手段改变脑部内源神经信号传递，从而引起脑功能变化的重要方法。神经调控不仅能在较短时间内引起神经元结构变化，而且特定设计的调控范式还可带来功能环路的改变，从而恢复或者提高神经突触连接可塑性，是解析神经元信号传导、研究神经环路、阐明脑功能与行为因果关系的得力工具，又是治疗临床神经系统疾病的重要手段。
然而现有的神经调控技术仍处于技术突破和发展阶段，由于刺激参数固定、刺激流程单一、刺激效果难以定量评估，目前应用于临床的神经调控技术常为开环控制并未实现患者个体化闭环的治疗，存在着适应症有限、治疗稳定性不佳以及个体差异明显等问题。无创神经调控的效果随着大脑状态的不同而不同，这就启示我们当前开环神经调控存在的缺点，很可能是因为没有结合个体的内源性脑信号来调控，神经调控的参数需结合给定时间的大脑功能活动进行动态控制。
脑功能活动包括神经元活动和局部能量代谢等多个过程，复杂的功能活动使得脑汇集了多个模态的信息，其中最为重要的是神经元的电活动和激活区域的血氧代谢变化，只有实现这两种信息的有效提取、分析和融合，才能将脑功能活动有机的联系起来。光电同步脑活动检测仪可以同时检测脑电信号和脑血氧信号从而从两个维度解析脑功能信号。 截至目前，尚没有结合光电同步脑活动检测仪来实时评估脑状态，并以此来控制神经调控的参数，实现个体化闭环调控目的。
发明内容
为了解决现有技术中的上述问题，即现有的神经调控技术无法结合光电同步脑活动实时对神经进行调控，本发明提供了一种基于内源性脑信号的闭环神经调控系统，包括脑信号采集模块、调控处理模块和调控参数优化模块；
所述脑信号采集模块，配置为通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
所述调控处理模块，包括静息阶段单元、调控阶段单元和评价阶段单元；
所述静息阶段单元，配置为基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
所述调控阶段单元，配置为通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号截取最新ms的脑电信号EEG，并对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；n和m为非负数；
基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和 调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
所述评价阶段单元，在调控阶段单元完成调控之后，基于所述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
所述调控参数优化模块，配置为基于所述血氧调控效果和脑电调控效果调整调控参数。
在一些优选的实施方式中，所述静息阶段单元包括：
配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并对4个通道脑血氧信息平均处理获得静息脱氧血红蛋白浓度信息HbR rest和静息氧合血红蛋白浓度信息HbO rest；
对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，根据脑电频率标准分为δ频段、θ频段、α频段和β频段，通过快速傅里叶变换计算出各子频段的静息阶段幅值
Figure PCTCN2021084424-appb-000001
和
Figure PCTCN2021084424-appb-000002
并计算各子频段幅值最高的静息阶段频点
Figure PCTCN2021084424-appb-000003
和
Figure PCTCN2021084424-appb-000004
对各子频段进行修正获得个体化的子频段
Figure PCTCN2021084424-appb-000005
Figure PCTCN2021084424-appb-000006
和
Figure PCTCN2021084424-appb-000007
Figure PCTCN2021084424-appb-000008
在一些优选的实施方式中，所述调控阶段单元包括：
配置为通过ns的矩形窗或汉宁窗截取血氧水平信号fNIRS，获得最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，基于所述最新最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波并对4个通道脑血氧信息平均处理获得调控脱氧血红蛋白浓度信息HbR now和调控氧合血红蛋白浓度信息HbO now；
通过ms的矩形窗或汉宁窗截取所述脑电信号EEG，获得最新ms的脑电信号，基于所述最新ms的脑电信号进行0.5Hz-100Hz的带通滤波及50Hz的陷波处理，接着进行
Figure PCTCN2021084424-appb-000009
Figure PCTCN2021084424-appb-000010
和
Figure PCTCN2021084424-appb-000011
Figure PCTCN2021084424-appb-000012
的带通滤波，再在4个子频段中分别进行快速傅里叶变换和希尔伯特变换计算静息阶段频点
Figure PCTCN2021084424-appb-000013
和
Figure PCTCN2021084424-appb-000014
对应的调控阶段幅值
Figure PCTCN2021084424-appb-000015
和
Figure PCTCN2021084424-appb-000016
与调控阶段幅值相应的调控阶段相位为
Figure PCTCN2021084424-appb-000017
Figure PCTCN2021084424-appb-000018
和
Figure PCTCN2021084424-appb-000019
基于相对于基线的脑血氧幅度控制信号、相对于基线的脑电幅度控制信号确定触发阈值，基于所述调控阶段相位获取相位控制信号进而获取调控时刻，根据所述触发阈值和调控时刻生成调控信号；
所述相对于基线的脑血氧幅度控制信号为
Figure PCTCN2021084424-appb-000020
Figure PCTCN2021084424-appb-000021
其中A和B为待调整参数，取值范围为[-1,1]；
所述相对于基线的脑电幅度控制信号为
Figure PCTCN2021084424-appb-000022
Figure PCTCN2021084424-appb-000023
其中C、D、E和F为待调整参数，取值范围为[-1,1]；
所述相位控制信号为
Figure PCTCN2021084424-appb-000024
Figure PCTCN2021084424-appb-000025
其中，G、H、I和J为预设的参数，取值范围为[-1,1]；
所述触发阈值为Modu Thr＝M*Hb modu+N*EEG modu，调控 时刻为Time＝ANG modu，其中M和N为待调整参数，取值范围为[-1,1]；
所述调控信号Dev Modu为：
Figure PCTCN2021084424-appb-000026
其中Dev base为预设的调控设备刺激强度基准值，P为待调整参数，取值范围为[-1,1]。
在一些优选的实施例中，所述评价阶段单元包括：
配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并进行4个通道的脑血氧信息平均处理获得评价脱氧血红蛋白浓度信息HbR post和评价氧合血红蛋白浓度信息HbO post；
对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，并进行
Figure PCTCN2021084424-appb-000027
Figure PCTCN2021084424-appb-000028
和
Figure PCTCN2021084424-appb-000029
的带通滤波，再在4个子频段中分别进行快速傅里叶变换计算评价阶段频点
Figure PCTCN2021084424-appb-000030
和
Figure PCTCN2021084424-appb-000031
和对应的评价阶段幅值
Figure PCTCN2021084424-appb-000032
和
Figure PCTCN2021084424-appb-000033
血氧调控效果为：
Figure PCTCN2021084424-appb-000034
其中R和S为待调整参数，取值范围为[-1,1]；
脑电调控效果为：
Figure PCTCN2021084424-appb-000035
Figure PCTCN2021084424-appb-000036
其中U、V、W和X为待调整参数，取值范围为[-1,1]。
在一些优选的实施方式中，所述脑信号采集模块包括：
通过光电同步脑活动检测仪NEG同时收集多模态脑信号，光电同步脑活动检测仪的电极和光极按照NEG-32或NEG-8采集帽排布；fNIRS光通道排布在电极周围，32或8通道帽子覆盖全脑或特定区域。
在一些优选的实施方式中，所述调控设备包括经颅磁刺激设备TMS或经颅电刺激设备tES。
在一些优选的实施方式中，所述静息阶段单元和评价阶段单元均持续3分钟，使用者保持非任务状态下的闭眼或睁眼状态且调控设备处于关闭状态；所述调控阶段单元持续30分钟，调控设备处于开启状态，使用者保持睁眼状态或任务状态。
本发明的另一方面，提出了一种基于内源性脑信号的闭环神经调控方法，所述方法包括：
步骤S100，通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
步骤S200，其具体包括步骤S210-步骤S230；
步骤S210，基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
步骤S220，通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号截取最新ms的脑电信号EEG，并对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；n和m为非负数；
基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
步骤S230，在调控阶段单元完成调控之后，基于所述多模 态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
步骤S300，基于所述血氧调控效果和脑电调控效果调整调控参数。
本发明的第三方面，提出了一种电子设备，其特征在于，包括：至少一个处理器；以及与至少一个所述处理器通信连接的存储器；其中，所述存储器存储有可被所述处理器执行的指令，所述指令用于被所述处理器执行以实现上述的基于内源性脑信号的闭环神经调控方法。
本发明的第四方面，提出了一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于被所述计算机执行以实现权利要求8所述的基于内源性脑信号的闭环神经调控方法。
本发明的有益效果：
(1)本发明提出的基于内源性脑信号的闭环神经调控系统，通过实时采集内源性的脑信号，同步调整外部调控设备的调控参数，实现了外源性刺激信号闭环动态地控制内源性脑信号。
(2)本发明提出的基于内源性脑信号的闭环神经调控系统，通过光电同步脑活动检测仪采集多模态的内源性脑信号，融合高时间分辨率、高空间分辨率的采集信号，提高了脑部状态评估的精确度，使神经调控更精确。
(3)本发明提出的基于内源性脑信号的闭环神经调控系统，过多模态脑信号实时定量的改变调控参数，方便易用，实现了一体化脑活动同步采集和个体化调控。
附图说明
通过阅读参照以下附图所作的对非限制性实施例所作的详细描述，本申请的其它特征、目的和优点将会变得更明显：
图1是本发明实施例的基于内源性脑信号闭环调控系统的结构框图；
图2是本发明实施例的基于内源性脑信号闭环调控系统的原理示意图；
图3是本发明实施例中运用的光电同步脑活动检测仪NEG设备；
图4是本发明实施例中NEG设备的一种电极和光极排布；
图5是本发明实施例中NEG设备采集的多模态脑信号；
图6是本发明实施例中的基于内源性脑信号闭环调控系统应用于经颅磁刺激设备TMS的示意图；
具体实施方式
下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅用于解释相关发明，而非对该发明的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与有关发明相关的部分。
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本申请。
本发明提供一种基于内源性脑信号的闭环神经调控系统，本系统通过实时采集内源性的脑信号，同步调整外部调控设备的调控参数，实现了外源性刺激信号闭环动态地控制内源性脑信号。
本发明的一种基于内源性脑信号的闭环神经调控系统，包括脑信号采集模块、调控处理模块和调控参数优化模块；
所述脑信号采集模块，配置为通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
所述调控处理模块，包括静息阶段单元、调控阶段单元和评价阶段单元；
所述静息阶段单元，配置为基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
所述调控阶段单元，配置为通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号截取最新ms的脑电信号EEG，对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；n和m为非负数；
基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
所述评价阶段单元，在调控阶段单元完成调控之后，基于所 述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
所述调控参数优化模块，配置为基于所述血氧调控效果和脑电调控效果调整调控参数。
为了更清晰地对本发明基于内源性脑信号的闭环神经调控系统进行说明，下面结合图1对本发明实施例中各功能模块展开详述。
本发明第一实施例的基于内源性脑信号的闭环神经调控系统，包括步骤脑信号采集模块、调控处理模块和调控参数优化模块，各功能模块详细描述如下：
本实施例将基于内源性脑信号的闭环神经调控方法应用于经颅磁刺激TMS的运动区闭环调控；
所述脑信号采集模块，配置为通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
在本实施例中，脑信号采集模块包括：
通过光电同步脑活动检测仪NEG如图3所示，同时收集多模态脑信号，光电同步脑活动检测仪的电极和光极按照NEG-32或NEG-8采集帽排布，支持32或8个通道的fNIRS信号、32或8个脑电通道排布，具体排布如图4所示，使用NEG-32的光电同步脑活动检测仪，该设备具有32通道的脑电和32通道的近红外，采集帽子的排布按照10-20排布形式，其 中用到的EEG通道为FC1、FC3、FC5、C1、C3、C5、CP1、CP3、CP5等9个通道、用到的近红外通道为两个光源S和两个探头D组成的4个通道；fNIRS光通道排布在电极周围，32或8通道帽子覆盖全脑或特定区域。采集到的信号如图5所示。使用NEG-32的光电同步脑活动检测仪可直接采集到血氧水平信号fNIRS和脑电信号EEG；
所述调控处理模块，包括静息阶段单元、调控阶段单元和评价阶段单元；
在本实施例中，所述静息阶段单元和评价阶段单元均持续3分钟，使用者保持非任务状态下的闭眼或睁眼状态且调控设备处于关闭状态；所述调控阶段单元持续30分钟，调控设备处于开启状态，使用者保持睁眼状态或任务状态。
所述静息阶段单元，配置为基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
在本实施例中，静息阶段具体包括：配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并对4个通道脑血氧信息平均处理获得静息脱氧血红蛋白浓度信息HbR rest和静息氧合血红蛋白浓度信息HbO rest；本阶段的血氧信息的通道数可根据具体应用场景中的设备设置或用户需求进行调整，此处不做具体限定；
在本实施例中，为突出运动区C3的脑电信号，可对9个电极采取拉普拉斯空间滤波处理；
对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，根据脑电频率标准分为δ频段(0.5-5Hz)、θ频段(5-8Hz)、α频段(8-12Hz)和β频段(12-30Hz)，通过快速傅里叶变换计算出各子 频段的静息阶段幅值
Figure PCTCN2021084424-appb-000037
和
Figure PCTCN2021084424-appb-000038
并计算各子频段幅值最高的静息阶段频点
Figure PCTCN2021084424-appb-000039
和
Figure PCTCN2021084424-appb-000040
对各子频段进行修正获得个体化的子频段
Figure PCTCN2021084424-appb-000041
Figure PCTCN2021084424-appb-000042
和
Figure PCTCN2021084424-appb-000043
所述个体化的子频段可适应不同个体的脑电信号，避免个体差异对本发明的效果造成影响。
所述调控阶段单元，配置为通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号，截取最新ms的脑电信号EEG，并对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；n和m为非负数，在本实施例中，优选的n取值为3，m取值为2；
基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
在本实施例中，所述调控设备包括经颅磁刺激设备TMS或经颅电刺激设备tES。
在本实施例中，调控阶段单元包括：配置为通过ns的矩形窗或汉宁窗截取血氧水平信号fNIRS，获得最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，基于所述最新最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波并对4个通道脑血氧信息平均处理获得调控脱氧血红蛋白浓度信息HbR now和调控氧合血红蛋白浓度信息HbO now；
通过ms的矩形窗或汉宁窗截取所述脑电信号EEG，获得最新ms的脑电信号，基于所述最新ms的脑电信号进行0.5Hz-100Hz的带 通滤波及50Hz的陷波处理，接着进行
Figure PCTCN2021084424-appb-000044
Figure PCTCN2021084424-appb-000045
和
Figure PCTCN2021084424-appb-000046
Figure PCTCN2021084424-appb-000047
的带通滤波，再在4个子频段中分别进行快速傅里叶变换和希尔伯特变换计算静息阶段频点
Figure PCTCN2021084424-appb-000048
和
Figure PCTCN2021084424-appb-000049
对应的调控阶段幅值
Figure PCTCN2021084424-appb-000050
和
Figure PCTCN2021084424-appb-000051
与调控阶段幅值相应的调控阶段相位为
Figure PCTCN2021084424-appb-000052
Figure PCTCN2021084424-appb-000053
和
Figure PCTCN2021084424-appb-000054
基于相对于基线的脑血氧幅度控制信号、相对于基线的脑电幅度控制信号确定触发阈值，基于所述调控阶段相位获取相位控制信号进而获取调控时刻，根据所述触发阈值和调控时刻生成调控信号；
所述相对于基线的脑血氧幅度控制信号为
Figure PCTCN2021084424-appb-000055
Figure PCTCN2021084424-appb-000056
其中A和B为待调整参数，取值范围为[-1,1]；在本实施例中，为突出关心运动区的激活，可优选的A取1，B取0,；
所述相对于基线的脑电幅度控制信号为
Figure PCTCN2021084424-appb-000057
Figure PCTCN2021084424-appb-000058
其中C、D、E和F为待调整参数，取值范围为[-1,1]；在本实施例中，由于大脑运动区α节律明显，优选地C、D、F取0，E取1；
所述相位控制信号为
Figure PCTCN2021084424-appb-000059
Figure PCTCN2021084424-appb-000060
其中，G、H、I和J为预设的参数，取值范围为[-1,1]；在本实施例中，由于在大脑运动区α节律明显，优选的G、H、J取0，I取1；
所述触发阈值为Modu Thr＝M*Hb modu+N*EEG modu，调控时刻为Time＝ANG modu，其中M和N为待调整参数，取值范围为[-1,1]；在本实施例中，TMS对EEG和fNIRS信号均有影响，所以M和N均取1；
所述调控信号为：
Figure PCTCN2021084424-appb-000061
其中Dev base为预设的调控设备刺激强度基准值，P为待调整参数，取值范围为[-1,1]；在本实施例中，TMS的Dev base按照临床常用的静息运动阈值RMT确定，P值按照具体的TMS设备的情况而定，优选的取0.5；根据实际观测或治疗需要，ANG modu可设定为其他角度或角度范围，调控设备可根据所述调控信号输出任意波形。本发明通生成的调控信号融合了脑电信号高时间分辨率的优点和血氧水平信号高空间分辨率的优点使神经调控更精确，能够适应更多的症状提高了神经调控的泛用性；由于存在个体差异，不同的使用者对脑血氧信号表现和脑电信号表现也存在差异，部分使用者脑血氧信号表现良好而脑电信号表现一般或脑血氧信号表现一般而脑电信号表现良好，通过本发明的融合两种模态信号的调控方法能够解决单一模态调控方法难以应对使用者个体差异的问题，即本发明并非将获取脑血氧信号进行调控和获取脑电信号进行调控的简单结合。另外，由于在实际的运用场景中，探测到的脑电信号容易受到干扰，本发明提出的神经调控方法由于其融合了脑血氧信号和脑电信号提取的信息进行调控，一定程度上地减小了脑电信号受干扰对调控带来的影响，实现了持续的精确调控。
所述评价阶段单元，在调控阶段单元完成调控之后，基于所述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
基于所述各子频段的评价阶段幅值和各子频段的静息阶段 幅值计算脑电调控效果；
在本实施例中，评价阶段单元包括：配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并进行4个通道的脑血氧信息平均处理获得评价脱氧血红蛋白浓度信息HbR post和评价氧合血红蛋白浓度信息HbO post；
对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，并进行
Figure PCTCN2021084424-appb-000062
Figure PCTCN2021084424-appb-000063
和
Figure PCTCN2021084424-appb-000064
的带通滤波，再在4个子频段中分别进行快速傅里叶变换计算评价阶段频点
Figure PCTCN2021084424-appb-000065
和
Figure PCTCN2021084424-appb-000066
和对应的评价阶段幅值
Figure PCTCN2021084424-appb-000067
和
Figure PCTCN2021084424-appb-000068
血氧调控效果为：
Figure PCTCN2021084424-appb-000069
其中R和S为待调整参数，取值范围为[-1,1]；在本实施例中，由于关心运动区的激活，优选的R取1，S取0；
脑电调控效果为：
Figure PCTCN2021084424-appb-000070
Figure PCTCN2021084424-appb-000071
其中U、V、W和X为待调整参数，取值范围为[-1,1]在本实施例中，由于在大脑运动区α节律明显，优选的U、V、X取0，W取1。
所述调控参数优化模块，配置为基于所述血氧调控效果和脑电调控效果调整调控参数；本发明在具体运用过程中，根据观测需要或是实际调节需要，人为地调控参数，以达到更好的控制效果。
如图2所示，本发明通过广电同步脑活动检测仪对使用者进行实时脑信号采集，获得fNIRS信号和EEG信号，对fNIRS信号经过脑信号处理得HbO信号和HbR信号；同时对EEG信号提取子频段相位和子频段幅值；通过HbO信号、HbR信号EEG信号提取子频段相位和子频段幅值获 取调控效果评价，基于调控效果评价进行调控参数优化进而实时调控刺激。
基于内源性脑信号闭环调控系统应用于经颅磁刺激设备TMS上如图6所示。
需要说明的是，上述实施例提供的基于内源性脑信号的闭环神经调控的系统，仅以上述各功能模块的划分进行举例说明，在实际应用中，可以根据需要而将上述功能分配由不同的功能模块来完成，即将本发明实施例中的模块或者步骤再分解或者组合，例如，上述实施例的模块可以合并为一个模块，也可以进一步拆分成多个子模块，以完成以上描述的全部或者部分功能。对于本发明实施例中涉及的模块、步骤的名称，仅仅是为了区分各个模块或者步骤，不视为对本发明的不当限定。
本发明第二实施例的基于内源性脑信号的闭环神经调控方法，具体步骤包括：
步骤S100，通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
步骤S200，其具体包括步骤S210-步骤S230；
步骤S210，基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
步骤S220，通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号中的脑电信号EEG，对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；
基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
步骤S230，在调控阶段单元完成调控之后，基于所述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
步骤S300，基于所述血氧调控效果和脑电调控效果调整调控参数。
所属技术领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的方法的具体工作过程及有关说明，可以参考上述系统实施例中的对应功能模块，在此不再赘述。
本发明第三实施例的一种电子设备，其特征在于，包括：至少一个处理器；以及与至少一个所述处理器通信连接的存储器；其中，所述存储器存储有可被所述处理器执行的指令，所述指令用于被所述处理器执行以实现上述的基于内源性脑信号的闭环神经调控方法。
本发明第四实施例的一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于被所述计算机执行以实现上述的基于内源性脑信号的闭环神经调控方法。
所属技术领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的存储装置、处理装置的具体工作过程及有关说明，可以参考前述方法实施例中的对应过程，在此不再赘述。
术语“第一”、“第二”等是用于区别类似的对象，而不是用于描述或表示特定的顺序或先后次序。
术语“包括”或者任何其它类似用语旨在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备/装置不仅包括那些要素，而且还包括没有明确列出的其它要素，或者还包括这些过程、方法、物品或者设备/装置所固有的要素。
至此，已经结合附图所示的优选实施方式描述了本发明的技术方案，但是，本领域技术人员容易理解的是，本发明的保护范围显然不局限于这些具体实施方式。在不偏离本发明的原理的前提下，本领域技术人员可以对相关技术特征做出等同的更改或替换，这些更改或替换之后的技术方案都将落入本发明的保护范围之内。

Claims (10)

  1. 一种基于内源性脑信号的闭环神经调控系统，其特征在于，所述系统包括：脑信号采集模块、调控处理模块和调控参数优化模块；
    所述脑信号采集模块，配置为通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
    所述调控处理模块，包括静息阶段单元、调控阶段单元和评价阶段单元；
    所述静息阶段单元，配置为基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
    所述调控阶段单元，配置为通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号截取最新ms的脑电信号EEG，并对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变换计算各子频段的调控阶段幅值和调控阶段相位；
    基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
    所述评价阶段单元，在调控阶段单元完成调控之后，基于所述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
    基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、 静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
    基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
    所述调控参数优化模块，配置为基于所述血氧调控效果和脑电调控效果调整调控参数。
  2. 根据权利要求1所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述静息阶段单元包括：
    配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并对4个通道脑血氧信息平均处理获得静息脱氧血红蛋白浓度信息HbR rest和静息氧合血红蛋白浓度信息HbO rest；
    对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，根据脑电频率标准分为δ频段、θ频段、α频段和β频段，通过快速傅里叶变换计算出各子频段的静息阶段幅值
    Figure PCTCN2021084424-appb-100001
    和
    Figure PCTCN2021084424-appb-100002
    并计算各子频段幅值最高的静息阶段频点
    Figure PCTCN2021084424-appb-100003
    和
    Figure PCTCN2021084424-appb-100004
    对各子频段进行修正获得个体化的子频段
    Figure PCTCN2021084424-appb-100005
    Figure PCTCN2021084424-appb-100006
    和
    Figure PCTCN2021084424-appb-100007
    Figure PCTCN2021084424-appb-100008
  3. 根据权利要求2所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述调控阶段单元包括：
    配置为通过ns的矩形窗或汉宁窗截取血氧水平信号fNIRS，获得最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，基于所述最新最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息 HbO进行0.01Hz-0.2Hz的带通滤波并对4个通道脑血氧信息平均处理获得调控脱氧血红蛋白浓度信息HbR now和调控氧合血红蛋白浓度信息HbO now；n为非负数；
    通过ms的矩形窗或汉宁窗截取所述脑电信号EEG，获得最新ms的脑电信号，基于所述最新ms的脑电信号进行0.5Hz-100Hz的带通滤波及50Hz的陷波处理，接着进行
    Figure PCTCN2021084424-appb-100009
    Figure PCTCN2021084424-appb-100010
    和
    Figure PCTCN2021084424-appb-100011
    的带通滤波，再在4个子频段中分别进行快速傅里叶变换和希尔伯特变换计算静息阶段频点
    Figure PCTCN2021084424-appb-100012
    和
    Figure PCTCN2021084424-appb-100013
    对应的调控阶段幅值
    Figure PCTCN2021084424-appb-100014
    和
    Figure PCTCN2021084424-appb-100015
    与调控阶段幅值相应的调控阶段相位为
    Figure PCTCN2021084424-appb-100016
    和
    Figure PCTCN2021084424-appb-100017
    m为非负数；
    基于相对于基线的脑血氧幅度控制信号和相对于基线的脑电幅度控制信号确定触发阈值，基于所述调控阶段相位获取相位控制信号进而获取调控时刻，根据所述触发阈值和调控时刻生成调控信号；
    所述相对于基线的脑血氧幅度控制信号为
    Figure PCTCN2021084424-appb-100018
    Figure PCTCN2021084424-appb-100019
    其中A和B为待调整参数，取值范围为[-1,1]；
    所述相对于基线的脑电幅度控制信号为
    Figure PCTCN2021084424-appb-100020
    Figure PCTCN2021084424-appb-100021
    其中C、D、E和F为待调整参数，取值范围为[-1,1]；
    所述相位控制信号为
    Figure PCTCN2021084424-appb-100022
    Figure PCTCN2021084424-appb-100023
    其中，G、H、I和J为预设的参数，取值范围为[-1,1]；
    所述触发阈值为Modu Thr＝M*Hb modu+N*EEG modu，调控时刻为Time＝ANG modu，其中M和N为待调整参数，取值范围为[-1,1]；
    所述调控信号Dev Modu为：
    Figure PCTCN2021084424-appb-100024
    其中Dev base为预设的调控设备刺激强度基准值，P为待调整参数，取值范围为[-1,1]。
  4. 根据权利要求3所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述评价阶段单元包括：
    配置为对所述脱氧血红蛋白浓度信息HbR、氧合血红蛋白浓度信息HbO进行0.01Hz-0.2Hz的带通滤波，并进行4个通道的脑血氧信息平均处理获得评价脱氧血红蛋白浓度信息HbR post和评价氧合血红蛋白浓度信息HbO post；
    对所述脑电信号EEG进行0.5Hz-100Hz的带通滤波以及50Hz的陷波处理，并进行
    Figure PCTCN2021084424-appb-100025
    Figure PCTCN2021084424-appb-100026
    和
    Figure PCTCN2021084424-appb-100027
    的带通滤波，再在4个子频段中分别进行快速傅里叶变换计算评价阶段频点
    Figure PCTCN2021084424-appb-100028
    和
    Figure PCTCN2021084424-appb-100029
    和对应的评价阶段幅值
    Figure PCTCN2021084424-appb-100030
    和
    Figure PCTCN2021084424-appb-100031
    血氧调控效果为：
    Figure PCTCN2021084424-appb-100032
    其中R和S为待调整参数，取值范围为[-1,1]；
    脑电调控效果为：
    Figure PCTCN2021084424-appb-100033
    Figure PCTCN2021084424-appb-100034
    其中U、V、W和X为待调整参数，取值范围为[-1,1]。
  5. 根据权利要求1所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述脑信号采集模块包括：
    通过光电同步脑活动检测仪NEG同时收集多模态脑信号，光电同步脑活动检测仪的电极和光极按照NEG-32或NEG-8采集帽排布；fNIRS 光通道排布在电极周围，32或8通道帽子覆盖全脑或特定区域。
  6. 根据权利要求1所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述调控设备包括经颅磁刺激设备TMS或经颅电刺激设备tES。
  7. 根据权利要求1所述的基于内源性脑信号的闭环神经调控系统，其特征在于，所述静息阶段单元和评价阶段单元均持续3分钟，使用者保持非任务状态下的闭眼或睁眼状态且调控设备处于关闭状态；所述调控阶段单元持续30分钟，调控设备处于开启状态，使用者保持睁眼状态或任务状态。
  8. 一种基于内源性脑信号的闭环神经调控方法，其特征在于，所述方法包括：
    步骤S100，通过脑信号采集设备实时获取多模态脑信号；所述多模态脑信号包括血氧水平信号fNIRS和脑电信号EEG；
    步骤S200，其具体包括步骤S210-步骤S230；
    步骤S210，基于所述多模态脑信号，进行解码获取静息脱氧血红蛋白浓度信息HbR、静息氧合血红蛋白浓度信息HbO、EEG各子频段的静息阶段频点和EEG各子频段的静息阶段幅值，并进行子频段修正生成个体化子频段；
    步骤S220，通过基于所述多模态脑信号，进行解码获取最新ns的脱氧血红蛋白浓度信息HbR和氧合血红蛋白浓度信息HbO，通过带通滤波和平均处理的方式获得调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息；基于所述多模态脑信号截取最新ms的脑电信号EEG，并对各子频段进行个体化子频段的带通滤波、快速傅里叶变换和希尔伯特变 换计算各子频段的调控阶段幅值和调控阶段相位；n和m为非负数；
    基于所述调控脱氧血红蛋白浓度信息和调控氧合血红蛋白浓度信息设置调控设备刺激的幅度控制信号，基于所述幅度控制信号和调控阶段相位生成控制信号的调控时刻，基于所述调控设备刺激的幅值和调控时刻生成调控信号，通过调控设备输出调控信号；
    步骤S230，在调控阶段单元完成调控之后，基于所述多模态脑信号，进行解码获取评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、各子频段的评价阶段频点和各子频段的评价阶段幅值；
    基于所述评价脱氧血红蛋白浓度信息、评价氧合血红蛋白浓度信息、静息脱氧血红蛋白浓度信息和静息氧合血红蛋白浓度信息计算血氧调控效果；
    基于所述各子频段的评价阶段幅值和各子频段的静息阶段幅值计算脑电调控效果；
    步骤S300，基于所述血氧调控效果和脑电调控效果调整调控参数。
  9. 一种电子设备，其特征在于，包括：至少一个处理器；以及与至少一个所述处理器通信连接的存储器；其中，所述存储器存储有可被所述处理器执行的指令，所述指令用于被所述处理器执行以实现权利要求8所述的基于内源性脑信号的闭环神经调控方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于被所述计算机执行以实现权利要求8所述的基于内源性脑信号的闭环神经调控方法。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106175701A (zh) * 2016-09-30 2016-12-07 中国科学院电子学研究所 一种神经信息光电调控与双模检测系统
WO2017112679A1 (en) * 2015-12-22 2017-06-29 University Of Florida Research Foundation, Inc. Multimodal closed-loop brain-computer interface and peripheral stimulaiton for neuro-rehabilitation
CN107405101A (zh) * 2015-03-02 2017-11-28 迈恩德玛泽股份有限公司 大脑活动测量和反馈系统
CN108065932A (zh) * 2017-09-13 2018-05-25 中科搏锐（北京）科技有限公司 脑功能活动检测与调控联合方法、装置及系统
CN109925582A (zh) * 2019-03-20 2019-06-25 天津大学 双模态脑-机交互式运动神经反馈训练装置和方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9204831B2 (en) * 2011-12-21 2015-12-08 Mitsunori MINAMI Brain state support apparatus and program
US11944447B2 (en) * 2016-11-03 2024-04-02 Institute Of Automation, Chinese Academy Of Sciences Neurovascular coupling analytical method based on electroencephalogram and functional near-infrared spectroscopy
CN108937956A (zh) * 2018-06-04 2018-12-07 义乌市杰联电子科技有限公司 一种新型TMS-fNIRS脑成像装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107405101A (zh) * 2015-03-02 2017-11-28 迈恩德玛泽股份有限公司 大脑活动测量和反馈系统
WO2017112679A1 (en) * 2015-12-22 2017-06-29 University Of Florida Research Foundation, Inc. Multimodal closed-loop brain-computer interface and peripheral stimulaiton for neuro-rehabilitation
CN106175701A (zh) * 2016-09-30 2016-12-07 中国科学院电子学研究所 一种神经信息光电调控与双模检测系统
CN108065932A (zh) * 2017-09-13 2018-05-25 中科搏锐（北京）科技有限公司 脑功能活动检测与调控联合方法、装置及系统
CN109925582A (zh) * 2019-03-20 2019-06-25 天津大学 双模态脑-机交互式运动神经反馈训练装置和方法

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