WO2019000901A1 - 基于脑信号的控制方法、控制设备及人机交互设备 - Google Patents

基于脑信号的控制方法、控制设备及人机交互设备 Download PDF

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Publication number
WO2019000901A1
WO2019000901A1 PCT/CN2018/071728 CN2018071728W WO2019000901A1 WO 2019000901 A1 WO2019000901 A1 WO 2019000901A1 CN 2018071728 W CN2018071728 W CN 2018071728W WO 2019000901 A1 WO2019000901 A1 WO 2019000901A1
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waveform
brain
signal
cerebral oxygen
oxygen
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PCT/CN2018/071728
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English (en)
French (fr)
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李颖祎
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京东方科技集团股份有限公司
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Priority to US16/315,886 priority Critical patent/US11179090B2/en
Publication of WO2019000901A1 publication Critical patent/WO2019000901A1/zh

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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
    • A61B5/24—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
    • 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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • 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]
    • 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
    • A61B5/68—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
    • A61B5/7235—Details of waveform analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/0304—Detection arrangements using opto-electronic means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/4064—Evaluating the brain

Definitions

  • the present disclosure relates to the field of human-computer interaction technology, and in particular to a brain signal-based control method, a control device, and a human-machine interaction device.
  • brain wave also called electroencephalogram (EEG).
  • EEG electroencephalogram
  • Embodiments of the present disclosure provide a brain signal-based control method, a control device, and a human-machine interaction device.
  • an embodiment of the present disclosure provides a brain signal-based control method, including:
  • Generating an EEG signal and a cerebral oxygen signal periodically during a target time period respectively generating, according to the acquired EEG signal and the cerebral oxygen signal, the EEG signal and the cerebral oxygen signal within the target time period Varying EEG waveforms and cerebral oxygen waveforms;
  • the controlled device is controlled to perform the target operation when the electroencephalogram waveform and the cerebral oxygen waveform curve meet the condition.
  • controlling the controlled device to perform the Target operations including:
  • the controlled device is controlled to perform an operation corresponding to a calm state of the brain.
  • controlling the controlled device to perform the Target operations including:
  • determining whether the EEG waveform and the cerebral oxygen waveform are in compliance with a condition for controlling a controlled device to perform a target operation include:
  • an embodiment of the present disclosure provides a brain signal-based control device, including: an electroencephalogram signal detecting device, a brain oxygen signal detecting device, and a processor; wherein the EEG detecting device and the cerebral oxygen signal Detecting devices are respectively coupled to the processor;
  • the processor configured to control the EEG signal detecting device and the cerebral oxygen signal detecting device to periodically detect an EEG signal and a cerebral oxygen signal during a target time period, according to the detected EEG signal and the The cerebral oxygen signals respectively generate an electroencephalogram waveform and a cerebral oxygen waveform curve that characterize the electroencephalogram signal and the cerebral oxygen signal during the target period; and determine the electroencephalogram waveform and the cerebral oxygen When the waveform curve meets the condition, a control instruction is sent to the controlled device to cause the controlled device to perform a target operation corresponding to the control instruction.
  • the controlled device when the electroencephalogram waveform and the cerebral oxygen waveform curve meet the condition, the controlled device performs the The target operation corresponding to the control instruction, including:
  • the EEG detecting device includes an EEG detecting electrode.
  • the cerebral oxygen signal detecting device includes: a detecting light source, and a light sensor spaced apart from the detecting light source by a target distance;
  • the detection light source is configured to emit infrared light to the cerebral cortex so that the emitted infrared light interacts with the blood oxygen tissue of the cerebral cortex;
  • the photosensor is configured to detect the infrared light that is reflected by the cerebral cortex without interacting with the blood oxygen tissue.
  • the detecting light source comprises a first light emitting chip and a second light emitting chip packaged in the same package structure
  • the wavelength of light emitted by the first light emitting chip is about 760 nm, and the wavelength of light emitted by the second light emitting chip is about 850 nm.
  • the foregoing control device provided by the embodiment of the present disclosure further includes: a first filter amplifying circuit coupled between the processor and the EEG detecting electrode.
  • the method further includes: a second filter amplifying circuit coupled between the processor and the photosensor.
  • the foregoing control device provided by the embodiment of the present disclosure further includes: a driving circuit coupled to the detecting light source.
  • the foregoing control device provided by the embodiment of the present disclosure further includes: a wireless transmission module, configured to send the control instruction of the processor to the controlled device.
  • control device is integrated in a component worn by the head.
  • an embodiment of the present disclosure provides a human-machine interaction device, including any of the foregoing control devices and a controlled device.
  • FIG. 1 is a flow chart of a brain signal based control method according to an embodiment of the present disclosure
  • FIG. 2 is a flowchart of a brain signal based control method according to another embodiment of the present disclosure
  • FIG. 3 is a schematic structural diagram of a brain signal monitoring device according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic structural diagram of an electroencephalogram signal detecting apparatus according to an embodiment of the present disclosure
  • FIG. 5 is a schematic structural diagram of a brain oxygen signal detecting apparatus according to an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram showing the operation of a cerebral oxygen signal detecting apparatus according to an embodiment of the present disclosure
  • FIG. 7 is a schematic structural view of a detecting light source according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of coupling of a detection light source according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic structural diagram of a brain signal monitoring device according to another embodiment of the present disclosure.
  • FIG. 10 is a schematic structural view of a head mounted component according to an embodiment of the present disclosure.
  • Embodiments of the present disclosure provide a brain signal-based control method, a control device, and a human-machine interaction device for improving the accuracy of control.
  • the principle of human-computer interaction device based on brain wave control is: pre-establishing a mapping relationship between brain wave data and an operation instruction of the controlled device, and then, after acquiring brain wave data, determining an operation instruction corresponding to the brain wave data according to the mapping relationship Finally, the controlled device is instructed to execute the operation instruction.
  • the embodiment of the present disclosure provides a brain signal-based control method.
  • the brain signal-based control method provided by the embodiment of the present disclosure may include the following steps:
  • S101 Periodically acquire an EEG signal and a cerebral oxygen signal in a target period (which may be preset according to a needs of a person skilled in the art), and generate an EEG signal according to the acquired EEG signal and the cerebral oxygen signal respectively. And an electroencephalogram waveform and a brain oxygen waveform curve in which the cerebral oxygen signal changes during the target period;
  • S102 Determine whether a brain electrical waveform curve and a brain oxygen waveform curve meet conditions for controlling a controlled device to perform a target operation (the target operation may be preset according to a needs of a person skilled in the art);
  • the above control method provided by the embodiment of the present disclosure simultaneously detects the EEG signal and the cerebral oxygen signal in the target time period, and generates the EEG signal and the cerebral oxygen signal respectively according to the detected EEG signal and the cerebral oxygen signal in the target period.
  • Internally changing EEG waveform and cerebral oxygen waveform, and determining whether the EEG waveform and the cerebral oxygen waveform are in compliance with the conditions for controlling the controlled device to perform the target operation, and can avoid the data of the single waveform curve in the event of a jump or the like.
  • the cerebral oxygen signal is relatively stable compared to the EEG signal, and it is also sensitive to changes in the brain's active state. Therefore, the use of two brain signals can effectively improve the accuracy of control.
  • the EEG signal is that when the brain is active, the ion current of the neuron produces a voltage change.
  • the postsynaptic potential that occurs simultaneously in a large number of neurons forms a brain wave after summation.
  • the change in voltage is recorded over a period of time to generate brain waves (i.e., the above-described EEG waveform).
  • Brain waves record changes in the electrical activity of the brain, which is the overall reflection of the electrophysiological activity of the brain's nerve cells on the surface of the cerebral cortex or scalp.
  • the frequency of changes in brain waves is large, and as the attention drops to the calm state of the brain, the frequency of changes in brain waves decreases.
  • Cerebral oxygen signals are usually monitored using infrared detection equipment. Different tissues of the brain have different absorption and scattering characteristics for the near-infrared spectrum. The absorption of infrared light by the brain produces a local response as a function of local changes in functional activity. When the brain is in an active state, it causes local brain tissue cells and oxygen metabolism, which causes changes in blood oxygen concentration in the corresponding region. Therefore, by monitoring the blood oxygen state of brain tissue, it can also be used to evaluate brain functional activities.
  • the device for monitoring the cerebral oxygen signal emits infrared light in the near-infrared band to the cerebral cortex, and the infrared light is received by the light sensor after being reflected by the cerebral cortex. The light sensor detects the returned infrared light, thereby determining the amount of infrared light absorbed by the brain, thereby determining the hemorrhagic oxygen content and determining the active state of the brain.
  • the cerebral oxygen waveform may be a change curve of the optical signal detected by the photosensor during the target time period.
  • the controlled device is controlled to perform the target operation, which may specifically include the following cases:
  • the brain is determined to be in an active state;
  • the device performs an operation corresponding to the active state of the brain.
  • the frequency of brain waves increases, and the cerebral blood oxygen content also increases, so that the intensity of infrared light detected by the light sensor decreases. Therefore, when detecting that the frequency increase of the brain wave is greater than or equal to the first threshold and the decrease of the infrared light intensity is greater than or equal to the second threshold, it may be determined that the brain is in an active state, thereby controlling the controlled device to perform the activity corresponding to the brain active state. Operation. In practical applications, the sizes of the first threshold and the second threshold may be set according to actual requirements.
  • the active state of the brain can be divided into different levels, each level corresponding to a numerical range of an EEG waveform and a numerical range of a cerebral oxygen waveform. When it is determined that the values of the EEG waveform and the cerebral oxygen waveform are within the numerical range corresponding to a certain level, the controlled device can be controlled to perform the corresponding operation.
  • the brain is determined to be in a calm state;
  • the device performs an operation corresponding to the calm state of the brain.
  • the frequency of brain waves When the brain is in a calm state, the frequency of brain waves will decrease, and the blood oxygen content of the brain will also decrease, so that the intensity of infrared light detected by the light sensor increases. Therefore, when detecting that the frequency decrease of the brain wave is greater than or equal to the third threshold and the increase of the infrared light intensity is greater than or equal to the fourth threshold, it may be determined that the brain is in a calm state, thereby controlling the controlled device to perform a correspondence with the brain calm state. Operation.
  • the third threshold and the fourth threshold may be set according to actual needs, the first threshold may be equal to the third threshold, and the second threshold may be equal to the fourth threshold, which is not limited herein.
  • the controlling device performs the currently performed operation.
  • the EEG signal is prone to jumps and the like, in the case where the brain's attention is not concentrated, the judgment may be inaccurate. Therefore, in the above control method provided by the embodiment of the present disclosure, it is necessary to judge the EEG waveform and the brain oxygen waveform. If the change in the EEG waveform is severe and the change in the cerebral oxygen waveform is within a small range, that is, the change in the value of the cerebral oxygen waveform is less than its target threshold, there is a great possibility that it is due to the EEG signal. Caused by unexpected fluctuations. At this time, it is necessary to keep the controlled device performing the currently performed operation and avoid the erroneous operation caused by the inaccuracy of the EEG signal.
  • the controlled device can be a remotely controlled aircraft that is controlled based on changes in brain signals.
  • the frequency of the brain wave is reduced by 50%.
  • the flight may be unstable at this time, and the aircraft may not be normal due to the large deceleration. Flight or landing caused damage to the aircraft.
  • the EEG signal and the cerebral oxygen signal are simultaneously monitored, when the brain wave frequency drops sharply by 50% and the cerebral oxygen waveform does not change significantly, the aircraft can maintain the current flight state and avoid damage caused by misoperation.
  • the above control method provided by the embodiment of the present disclosure is not limited to the control of the above-mentioned controlled device, and other brain signal-based controlled devices based on the disclosed concept of the present disclosure are also within the protection scope of the present disclosure.
  • determining whether the EEG waveform and the cerebral oxygen waveform are in compliance with the condition for controlling the controlled device to perform the target operation may specifically include the following substeps. :
  • S1021 extracting an electroencephalogram characteristic in an EEG waveform, and extracting a cerebral oxygen characteristic in a cerebral oxygen waveform;
  • the EEG characteristic of the EEG waveform can be the corresponding relationship of the rate of change of the brain wave with time; the cerebral oxygen characteristic of the cerebral oxygen waveform can be the corresponding relationship of the received rate of change of the light intensity with time.
  • a threshold can be set for the fused feature, thereby determining whether the compliant feature and the threshold value are consistent with the control controlled device execution according to the relationship between the fused feature and the threshold value. The condition of the target operation.
  • the integrated area of the curve of the EEG waveform and the cerebral oxygen waveform in a certain period of time can be calculated first, and the calculated integrated area is taken as the characteristic of the two waveforms. Then, the obtained integrated area of the curve is used as a difference, and the condition is judged by comparing the difference with the set threshold, so that the controlled device is controlled to perform the target operation corresponding to the condition according to the judgment result.
  • a curve with a sharp change in the EEG waveform and the cerebral oxygen waveform can be intercepted as a feature, and the EEG characteristic curve and the cerebral oxygen characteristic curve are weighted and linearly fitted to obtain a new curve equation.
  • the curve equation is compared with the set threshold or target condition to determine whether the condition is met, and the controlled device is controlled to perform the corresponding operation when the condition is satisfied.
  • the derivative function of the EEG waveform and the brain oxygen waveform curve can be separately obtained, and the extremum of the two derivative functions can be extracted, and the extremum is compared with the set threshold to determine whether the control controlled device is satisfied to perform the corresponding operation.
  • the brain signal-based control method compares the manner in which the controlled device is controlled based on only the electroencephalogram signal in the prior art, simultaneously detects the brain electrical signal and the brain oxygen signal, and generates an electroencephalogram waveform and The cerebral oxygen waveform determines whether the EEG waveform and the cerebral oxygen waveform are in compliance with the conditions for controlling the controlled device to perform the target operation, thereby avoiding the misoperation of the single data in the event of a jump or the like, and improving the accuracy of the control.
  • an embodiment of the present disclosure provides a brain signal-based control device.
  • the structure is as shown in FIG. 3, and includes: an electroencephalogram signal detecting device 31, a brain oxygen signal detecting device 32, and a processor 33.
  • the EEG signal detecting device 31 and the cerebral oxygen signal detecting device 32 are respectively coupled to the processor 33.
  • the processor 33 is configured to control the EEG signal detecting device 31 and the cerebral oxygen signal detecting device 32 to periodically detect the EEG signal and the cerebral oxygen signal during the target period, according to the detected EEG signal and the brain
  • the oxygen signal respectively generates an electroencephalogram waveform and a brain oxygen waveform which characterize the changes of the electroencephalogram signal and the cerebral oxygen signal in the target period; and determine whether the electroencephalogram waveform and the cerebral oxygen waveform curve meet the conditions for controlling the controlled device to perform the target operation; And when it is determined that the EEG waveform and the brain oxygen waveform curve meet the condition, the control instruction is sent to the controlled device, so that the controlled device performs the target operation corresponding to the control instruction.
  • the control device simultaneously detects the brain electrical signal and the brain oxygen signal, and generates an electroencephalogram waveform and a brain oxygen waveform curve, and determines whether the brain electrical waveform curve and the brain oxygen waveform curve are It meets the conditions for controlling the controlled device to perform the target operation, which can avoid the misoperation of a single waveform curve in the event of a jump or the like, and improve the accuracy of the control device.
  • the control device may control the controlled device to perform the target operation, and specifically may include the following situations:
  • the brain is determined to be in an active state;
  • the device performs an operation corresponding to the active state of the brain.
  • the brain is determined to be in a calm state;
  • the device performs an operation corresponding to the calm state of the brain.
  • the controlled device performs the currently performed operation when the amount of change in the value of at least one of the EEG waveform and the cerebral oxygen waveform is less than the respective target threshold during the target time period.
  • the electroencephalogram signal detecting device 31 includes an electroencephalogram detecting electrode 311.
  • an electrode placed at a zero potential is referred to as a reference electrode
  • an electrode placed at a non-zero potential is referred to as a working electrode.
  • the reference electrode and the working electrode are respectively coupled to the processor by, for example, a wire, thereby amplifying a potential difference between the working electrode and the reference electrode. Specifically, as shown in FIG.
  • the electroencephalogram detecting electrode 311 may include a working electrode 3111 and a reference electrode 3112, and the working electrode 3111 is placed on the scalp, and the reference electrode 3112 is placed on the earlobe.
  • the EEG signal has strong noise background, low frequency (0.1 ⁇ 70Hz, low frequency band amplifier input 1 / f voltage noise is large) weak, high internal resistance, electrode polarization potential instability, etc., therefore, the front voltage follower should It also has high common mode rejection ratio, low input 1/f, low voltage noise, low input current noise, and drift characteristics.
  • a silver chloride powder electrode can be selected to reduce the polarization potential and improve the stability of the polarization potential.
  • the cerebral oxygen signal detecting device includes: a detecting light source 321 and a target distance from the detecting light source 321 (the target distance can be according to the field)
  • the light sensor 322 of the technician needs to be preset.
  • the detection light source 321 can be used to emit infrared light to the cerebral cortex so that the emitted infrared light interacts with the blood oxygen tissue of the cerebral cortex.
  • the light sensor 322 is used to detect infrared light that has not been reflected by blood oxygen tissue and is reflected by the cerebral cortex.
  • the detection source 321 typically employs a source of radiation.
  • the detecting light source 321 adopts a near-infrared light source, and the near-infrared light source does not impair the health of the body compared to the radiation source.
  • the near-infrared spectroscopy has obvious influence on blood flow, so it is more suitable for detecting cerebral oxygen signals.
  • the detection principle of the cerebral oxygen signal as described above is based on the absorption of near-infrared light by brain tissue blood flow and hemoglobin. As shown in FIG.
  • the detection light source 321 emits light in the near-infrared band to the cerebral cortex, and hemoglobin in the tissue related to the blood oxygen state in the cerebral cortex reflects the cerebral oxygen content, which has an absorption effect on the near-infrared light, and thus light
  • the light detected by the sensor 322 is infrared light that is not reflected by the brain and is reflected back. Then the part of the infrared light that is lost is absorbed by hemoglobin, which can indirectly reflect the state of blood oxygen in the brain by the light sensor.
  • Cerebral blood oxygenation is also positively correlated with brain activity, which correlates the intensity of the detected infrared light with brain activity.
  • the cerebral oxygen signal is the intensity of infrared light that is negatively correlated with cerebral blood oxygen.
  • the infrared light used by the detecting light source 321 can generally reach a certain depth to reach the cortex, so that the blood oxygen information is detected and reflected to the light sensor 322.
  • the emitted light of the detecting light source 321 has an influence on the light intensity detection of the light sensor 322, the target distance between the detecting light source 321 and the light sensor 322 is maintained to enable the detecting light source.
  • the light emitted by 321 is not directly received by the light sensor 322, which affects the detection result.
  • the distance between the detection light source 321 and the light sensor 322 can be set between 2-4 cm.
  • the light sensor 322 can be an optical probe.
  • the optical probe is composed of a silicon tube (PD tube), a transimpedance amplifier, a light guiding fiber, a filter, a spring case, and the like.
  • the use of the transimpedance amplifier front design overcomes the shortcomings of traditional fiber optic probes that introduce motion noise.
  • the 650nm long-wavelength filter can suppress external light interference and reduce the photocurrent noise of the PD tube.
  • the detection principle of the brain electrical signal and the brain oxygen signal is different, and the detection of the two brain signals does not interfere with each other.
  • the two kinds of brain signals can be collected simultaneously and simultaneously by separate devices, and the acquired EEG signals and cerebral oxygen signals are related to brain activity. Therefore, using two types of data can improve the judgment of brain activity. accuracy.
  • the detecting light source 321 includes a first light emitting chip 3212 and a second light emitting chip 3213 which are packaged in the same package structure 3211. Due to the high scattering and low absorption of infrared light in the near-infrared band (650-950 nm) by biological tissues (including cerebral cortex), near-infrared light can detect the cerebral cortex area at a depth of 2-3 cm below the scalp. Higher spatial resolution. The hemoglobin, in turn, has a strong absorption effect on the light of the band, and therefore, two light-emitting chips are used in the embodiment of the present disclosure.
  • the light emitted by the first light-emitting chip 3212 has a wavelength of about 760 nm and a half-wave width of about 20 nm.
  • the light emitted by the second light-emitting chip 3213 has a wavelength of about 850 nm and a half-wave width of about 35 nm.
  • the two light-emitting chips mentioned above may be light-emitting diodes, and the two light-emitting diodes may adopt three coupling modes as shown in FIG. (a) in FIG. 8 is a coupling manner in which two light emitting diodes are connected in parallel; (b) is a coupling mode of two LEDs in common cathode, and (c) is a coupling manner of two LEDs in common anode.
  • the method further includes: a first filter amplifying circuit 34 coupled between the processor 33 and the EEG detecting electrode 311 , and coupled to the processor 33 .
  • a second filter amplifying circuit 35 is connected to the light sensor 322. Since the background noise of the EEG signal detected by the EEG detecting electrode 311 and the photo sensor 322 and the light intensity signal related to the cerebral oxygen signal is large, the filter amplifying circuit can perform filtering processing and optimizing the signal on the two signals as needed. To form an effective EEG waveform and a brain oxygen waveform.
  • the above control device further includes a driving circuit 36 coupled to the detecting light source 321 .
  • the driving circuit 36 is mainly composed of an operational amplifier NPN transistor current feedback resistor, and can convert the voltage carrier signal into four 5- to 15-ma current carrier signals to drive the dual-wavelength detecting light source 321 .
  • the analog front end is available from TI's ADS1299 chip, which includes eight input multiplexers, a low noise programmable gain amplifier, and a synchronous sampling 24-bit analog-to-digital converter. Under the condition of 12 times gain and 70Hz bandwidth, the equivalent input voltage noise is lower than 1.0, which meets the requirements of medical grade EEG signal acquisition.
  • the foregoing control device provided by the embodiment of the present disclosure further includes a wireless transmission module 37 for transmitting a control instruction of the processor 33 to the controlled device.
  • the wireless transmission module 37 can adopt EMW3162, the highest network data transmission rate is 20Mbps, and has 128k SRAM buffer, which can meet the real-time data transmission requirements.
  • the wireless transmission module 37 has a built-in microcontroller STM32F205RG, which can directly program the module to realize the functions of analog front-end communication light source carrier to generate Wi-Fi network communication.
  • the above control device provided by the embodiment of the present disclosure further includes a power module (not shown), a driving circuit corresponding to the near-infrared illuminating, a filtering amplifying circuit for outputting the light, and a filtering amplification of the output of the EEG detecting electrode.
  • Power is supplied to components such as circuits.
  • the power module can include a charging circuit that can charge a single lithium battery using a 5V DC power supply, or a DC-DC boost power supply to raise the voltage of the single lithium battery to 6V.
  • Two kinds of low-dropout linear voltage regulator circuits which can be outputted to the data acquisition module (not shown) after filtering and amplifying the brain and brain oxygen signals, and the data acquisition module can transmit the wireless wireless communication module or through the USB port.
  • the collected data is transmitted by wireless or wired means, such as a computer with processor 33.
  • the power module also provides a low-noise analog power supply for the DAC module built into the wireless transmission module.
  • the 2.5V precision power supply reference is combined with a voltage follower to provide a low noise virtual ground.
  • the above control device provided by the embodiment of the present disclosure is integrated into the component worn by the head as shown in FIG.
  • the part worn by the head may be a sports bandage or a helmet or the like.
  • the detection light source 321 and the light sensor 322 may correspond to the position of the forehead of the head, and the two EEG detection electrodes 311 are located on both sides for detecting the EEG signal.
  • the brain performs motor imaging, the EEG signal and the brain oxygen signal will change accordingly.
  • the brain's EEG signal is generated under the cerebral cortex and collected by the EEG detection electrode.
  • the first filter amplification circuit EEG amplifies the signal, and finally the EEG signal is sent to the data acquisition module (such as the data acquisition card).
  • the cerebral oxygen signal is also sent to the data acquisition module after being processed by the second filter amplifying circuit. Finally, the data is transmitted to the processor 13 through the communication module, and the EEG waveform and the brain oxygen waveform are generated by the processor 13.
  • the control device determines whether the EEG waveform and the cerebral oxygen waveform are in compliance with the conditions for controlling the controlled device to perform the target operation, and finally generates a control command to control the controlled device to perform the corresponding operation.
  • an embodiment of the present disclosure further provides a human-machine interaction device, including any of the above-described brain signal-based control devices and controlled devices.
  • the controlled device may be various types of controlled devices that are controlled based on brain signals, for example, may be the above-described remote control flying device or the like according to an embodiment of the present disclosure, which is not specifically limited herein.
  • the brain signal control method, the control device and the human-machine interaction device provided by the embodiments of the present disclosure generate the characterization according to the acquired EEG signal and the cerebral oxygen signal by periodically acquiring the EEG signal and the cerebral oxygen signal in the target time period.
  • the EEG waveform and the cerebral oxygen waveform of the EEG signal and the cerebral oxygen signal change during the target period; determine whether the EEG waveform and the cerebral oxygen waveform are in compliance with the conditions for controlling the controlled device to perform the target operation; When the condition of the cerebral oxygen waveform is met, the controlled device is controlled to perform the target operation.
  • the above control method simultaneously detects an EEG signal and a brain oxygen signal, and generates an EEG waveform and a brain oxygen waveform, compared to the prior art method of controlling the controlled device based on only the EEG signal.
  • the curve, as well as determining whether the EEG waveform and the cerebral oxygen waveform are in compliance with the conditions for controlling the controlled device to perform the target operation, can prevent the data of the single waveform from causing the controlled device to perform the wrong operation when the data is hopped or the like, and improve the control.
  • the accuracy can be used to improve the control.

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Abstract

一种基于脑信号的控制方法、控制设备及人机交互设备，通过在目标时段内周期性地获取脑电信号和脑氧信号分别生成表征脑电信号和脑氧信号在目标时段内变化的脑电波形曲线和脑氧波形曲线（S101）；确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件(S102)；在脑电波形曲线和脑氧波形曲线符合条件时，控制受控设备执行目标操作(S103)。相比于现有技术中仅基于脑电信号来控制受控设备的方式，该控制方法同时检测脑电信号和脑氧信号并生成脑电波形曲线和脑氧波形曲线，确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，可以避免单一数据在发生跳变等情况造成误操作，提高控制的准确性。

Description

基于脑信号的控制方法、控制设备及人机交互设备
本申请要求于2017年6月28日递交的中国专利申请第201710510171.2号的优先权，在此全文引用上述中国专利申请公开的内容以作为本申请的一部分。
技术领域
本公开涉及人机交互技术领域，尤指一种基于脑信号的控制方法、控制设备及人机交互设备。
背景技术
人脑中有许多的神经细胞(又称为神经元)在活动着，神经元的离子电流会产生电压变动，这种微弱的生物电变化就是脑电波，也叫脑电图(Electroencephalogram，简称EEG)。近年来，随着脑电波的采集和识别技术的日渐成熟，基于脑电波控制的人机交互设备作为一种新兴体验而日益活跃。
发明内容
本公开实施例提供一种基于脑信号的控制方法、控制设备及人机交互设备。
第一方面，本公开实施例提供一种基于脑信号的控制方法，包括：
在目标时段内周期性地获取脑电信号和脑氧信号，根据获取的所述脑电信号和所述脑氧信号分别生成表征所述脑电信号和所述脑氧信号在所述目标时段内变化的脑电波形曲线和脑氧波形曲线；
确定所述脑电波形曲线和所述脑氧波形曲线是否符合控制受控设备执行目标操作的条件；以及
在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述 受控设备执行所述目标操作。
在一种可能的实现方式中，在本公开实施例提供的上述控制方法中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述受控设备执行所述目标操作，包括：
在所述目标时段内，在所述脑电波形曲线的数值增加量大于或等于第一阈值且所述脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活跃状态；控制所述受控设备执行与大脑活跃状态对应的操作；
在所述目标时段内，在所述脑电波形曲线的数值减小量大于或等于第三阈值且所述脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制所述受控设备执行与大脑平静状态对应的操作。
在一种可能的实现方式中，在本公开实施例提供的上述控制方法中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述受控设备执行所述目标操作，还包括：
在所述目标时段内，所述脑电波形曲线与所述脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值时，保持所述受控设备执行当前执行的操作。
在一种可能的实现方式中，在本公开实施例提供的上述控制方法中，确定所述脑电波形曲线和所述脑氧波形曲线是否符合控制受控设备执行目标操作的条件，包括：
提取所述脑电波形曲线中的脑电特征，并提取所述脑氧波形曲线中的脑氧特征；
将提取出的所述脑电特征与所述脑氧特征进行融合；
确定融合后的特征是否符合控制所述受控设备执行所述目标操作的条件。
第二方面，本公开实施例提供一种基于脑信号的控制设备，包括：脑电信号检测装置、脑氧信号检测装置以及处理器；其中，所述脑电信号检测装置与所述脑氧信号检测装置分别与所述处理器耦接；
所述处理器，用于控制所述脑电信号检测装置与所述脑氧信号检测装置在目标时段内周期性地检测脑电信号和脑氧信号，根据检测的所述脑电信号和所述脑氧信号分别生成表征所述脑电信号和所述脑氧信号在所述目标时段内变化的脑电波形曲线和脑氧波形曲线；以及在确定出所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，向所述受控设备发送控制指令，以使得所述受控设备执行与所述控制指令对应的目标操作。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，所述受控设备执行与所述控制指令对应的目标操作，包括：
在所述目标时段内，在所述脑电波形曲线的数值增加量大于或等于第一阈值且所述脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活跃状态；控制所述受控设备执行与大脑活跃状态对应的操作；或者
在所述目标时段内，在所述脑电波形曲线的数值减小量大于或等于第三阈值且所述脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制所述受控设备执行与大脑平静状态对应的操作；或者
在所述目标时段内，所述脑电波形曲线与所述脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值时，保持所述受控设备执行当前执行的操作。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，所述脑电信号检测装置包括脑电检测电极。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，所述脑氧信号检测装置包括：检测光源，以及与所述检测光源间隔目标距离的光传感器；其中，
所述检测光源用于向大脑皮层发射红外光，以使所发射的红外光与大脑皮层的血氧组织相互作用；
所述光传感器用于检测未与所述血氧组织作用而经过大脑皮层反射的所述红外光。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，所述检测光源包括封装于同一封装结构中的第一发光芯片和第二发光芯片；
所述第一发光芯片所发射的光的波长为约760nm，所述第二发光芯片所发射的光的波长为约850nm。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，还包括：耦接在所述处理器与所述脑电检测电极之间的第一滤波放大电路。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，还包括：耦接在所述处理器与所述光传感器之间的第二滤波放大电路。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，还包括：与所述检测光源耦接的驱动电路。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，还包括：无线传输模块，用于将所述处理器的所述控制指令发送给所述受控设备。
在一种可能的实现方式中，在本公开实施例提供的上述控制设备中，所述控制设备集成在由头部佩戴的部件中。
第三方面，本公开实施例提供一种人机交互设备，包括上述任一控制设备以及受控设备。
附图说明
图1为根据本公开实施例的基于脑信号的控制方法的流程图之一；
图2为根据本公开另一个实施例的基于脑信号的控制方法的流程图；
图3为根据本公开实施例的脑信号监测设备的结构示意图；
图4为根据本公开实施例的脑电信号检测装置的结构示意图；
图5为根据本公开实施例的脑氧信号检测装置的结构示意图；
图6为根据本公开实施例的脑氧信号检测装置的工作原理图；
图7为根据本公开实施例的检测光源的结构示意图；
图8为根据本公开实施例的检测光源的耦接示意图；
图9为根据本公开另一个实施例的脑信号监测设备的结构示意图；
图10为根据本公开实施例的头戴式部件的结构示意图。
具体实施方式
本公开实施例提供一种基于脑信号的控制方法、控制设备及人机交互设备，用以提高控制的准确性。
为使本公开的上述目的、特征和优点能够更为明显易懂，下面将结合附图和实施例对本公开做进一步说明。然而，示例的实施方式能够以多种形式实施，且不应被理解为限于在此阐述的实施方式；相反，提供这些实施方式使得本公开更全面和完整，并将示例的实施方式的构思全面地传达给本领域的技术人员。在图中相同的附图标记表示相同或类似的结构，因而将省略对它们的重复描述。本公开中所描述的表达位置与方向的词，均是以附图为例进行的说明，但根据需要也可以做出改变，所做改变均包含在本公开的保护范围内。
基于脑电波控制的人机交互设备的原理是：预先建立脑电波数据与受控设备的操作指令的映射关系，然后在获取脑电波数据后，根据该映射关系确定该脑电波数据对应的操作指令，最后指示受控设备执行该操作指令。
然而，由于人类的脑电波变化速度非常快，且在注意力不集中的情况下容易发生跳变，因此在这样的情况下受控设备所执行的操作指令可能是错误或无效的操作指令。上述问题均影响人机交互设备在进行操作时的准确性，降低用户体验。
首先，本公开实施例提供了一种基于脑信号的控制方法，如图1所示，本公开实施例提供的基于脑信号的控制方法可以包括如下步骤：
S101、在目标时段(该目标时段可根据本领域技术人员的需要预先设 定)内周期性地获取脑电信号和脑氧信号，根据获取的脑电信号和脑氧信号分别生成表征脑电信号和脑氧信号在目标时段内变化的脑电波形曲线和脑氧波形曲线；
S102、确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作(该目标操作可根据本领域技术人员的需要预先设定)的条件；
S103、在脑电波形曲线和脑氧波形曲线符合条件时，控制受控设备执行目标操作。
由于脑电信号的变化速度较快，并且很容易受到外界因素的影响，因此仅以脑电信号的变化来控制受控设备执行操作很容易出现错误或无效的操作。鉴于此，本公开实施例提供的上述控制方法，在目标时段内同时检测脑电信号和脑氧信号，根据检测的脑电信号和脑氧信号分别生成表征脑电信号和脑氧信号在目标时段内变化的脑电波形曲线和脑氧波形曲线，并确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，可以避免单一波形曲线的数据在发生跳变等情况时造成误操作，提高控制的准确性。脑氧信号相比于脑电信号来说相对稳定，且对大脑的活跃状态也有比较敏感的变化。因此，将两种脑信号配合使用，可以有效提高控制的准确性。
具体地，脑电信号是大脑在活动时，神经元的离子电流会产生电压变动。大量神经元同步发生的突触后电位经总和后形成了脑电波。在一段时间内记录电压的变化，从而生成脑电波(即，上述脑电波形曲线)。脑电波记录大脑活动时的电波变化，是脑神经细胞的电生理活动在大脑皮层或头皮表面的总体反映。大脑在活跃状态、注意力比较集中的情况下，脑电波的变化频率较大，而随着注意力下降至大脑平静状态，脑电波的变化频率也随之减小。
脑氧信号通常采用红外检测设备进行监测。大脑的不同组织对近红外光谱具有不同的吸收和散射特征。大脑对红外光的吸收会随着功能活动的局部变化而产生局部响应。当大脑处于活跃状态时，会引起局部脑组织细 胞与氧代谢，从而引起相应区域内血氧浓度的变化。因此，通过监测脑组织的血氧状态，也可用来评价大脑功能活动。在本公开实施例提供的上述控制方法中，上述用于监测脑氧信号的设备向大脑皮层发射近红外波段的红外光，红外光经过大脑皮层的反射后被光传感器接收。光传感器检测被返回的红外光，由此来确定被大脑所吸收的红外光的量，从而确定出血氧含量，并判断大脑的活跃状态。
具体来说，当大脑的工作量加大时，对氧的需求会即刻随之增大。这样在想象任务过程中，经过脑组织的血流量和血红蛋白数目就会增加，那么大脑对入射的近红外光的吸收也将随着增加。如果被吸收的光增加，则被反射回的光减少，光传感器所能检测到的光强变弱。通过这样的方式就能够监测到脑氧信息的变化。在本公开实施例中，脑氧波形曲线可为光传感器在目标时段内所检测到的光信号的变化曲线。
由此，在上述的步骤S103中，在脑电波形曲线和脑氧波形曲线符合条件时，控制受控设备执行目标操作，具体可以包括以下几种情况：
(1)在目标时段内，在脑电波形曲线的数值增加量大于或等于第一阈值且脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活跃状态；控制受控设备执行与大脑活跃状态对应的操作。
由上述的说明可知，大脑在活跃状态时，脑电波的频率会增加，脑血氧含量也会增加，使得光传感器所检测到的红外光强度减小。因此在检测脑电波的频率增加量大于或等于第一阈值且红外光强度的减小量大于或等于第二阈值时，可以确定大脑处于活跃状态，从而可控制受控设备执行与大脑活跃状态对应的操作。在实际应用中，可根据实际需求设置上述第一阈值和第二阈值的大小。大脑的活跃状态可分为不同等级，每个等级可对应于一个脑电波形曲线的数值范围和一个脑氧波形曲线的数值范围。在确定了脑电波形曲线和脑氧波形曲线的数值均属于某一等级所对应的数值范围之内时，可控制受控设备执行对应的操作。
(2)在目标时段内，在脑电波形曲线的数值减小量大于或等于第三阈 值且脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制受控设备执行与大脑平静状态对应的操作。
大脑在平静状态时，脑电波的频率会减小，脑血氧含量也会减少，使得光传感器所检测到的红外光强度增大。因此在检测脑电波的频率减小量大于或等于第三阈值且红外光强度的增大量大于或等于第四阈值时，可以确定大脑处于平静状态，从而可控制受控设备执行与大脑平静状态对应的操作。上述的第三阈值和第四阈值可根据实际需要来设置，第一阈值可等于第三阈值，第二阈值可等于第四阈值，在此不做限定。
(3)在目标时段内，脑电波形曲线与脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值(该目标阈值可根据本领域技术人员的需要预先设定)时，保持受控设备执行当前执行的操作。
如前所述，由于脑电信号很容易发生跳变等现象，在大脑的注意力不集中的情况下会造成判断不准确的情况。因此，在本公开实施例提供的上述控制方法中，需要对脑电波形曲线和脑氧波形曲线进行判断。如果脑电波形曲线的变化剧烈而脑氧波形曲线的变化在较小的范围之内，即脑氧波形曲线的数值的变化量小于其目标阈值时，则有极大的可能是由于脑电信号的意外波动而引起的。此时，需要保持受控设备执行当前执行的操作，避免由于脑电信号的不准确而造成的误操作。同样地，在脑电波形曲线的数值变化不大，而脑氧波形曲线的数值变化剧烈时，仍需要保持受控设备执行当前执行的操作。只有在脑电波形曲线和脑氧波形曲线都变化，且变化量满足上述的两种情况才控制受控设备执行相应的操作。
举例来说，受控设备可以为基于脑信号变化被控制的遥控飞机。在飞机飞行的过程中，如果注意力下降而导致脑电波的频率下降50％，在仅由脑电信号控制的情况下，此时可能会导致飞机飞行不稳定，大幅度的减速造成飞机不能正常飞行或降落而致使飞机损坏。而此时如果同时监测脑电信号和脑氧信号，在脑电波频率急剧下降50％而脑氧波形曲线并无明显变化时，可使飞机保持当前的飞行状态，避免误操作导致飞机受损。而只有 当脑电波的频率下降50％，而脑氧波形曲线的数值增大50％时，才使飞机执行降落、减速等对应的操作。本公开实施例提供的上述控制方法并不局限于对上述受控设备进行控制，基于本公开的公开构思的其它基于脑信号的受控设备也属于本公开的保护范围。
在一种可实施的方式中，如图2所示，在上述的步骤S102中，确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，具体可以包括如下子步骤：
S1021、提取脑电波形曲线中的脑电特征，并提取脑氧波形曲线中的脑氧特征；
S1022、将提取出的脑电特征与脑氧特征进行融合；
S1023、确定融合后的特征是否符合控制受控设备执行目标操作的条件。
在具体实施时，脑电波形曲线的脑电特征可为脑电波的变化率随时间的对应关系；脑氧波形曲线的脑氧特征可为接收的光强变化率随时间的对应关系。将两个随时间变化的曲线进行曲线融合后，例如进行归一化等处理，可对该融合后的特征设置阈值，从而根据融合后的特征与阈值的关系判断其是否符合控制受控设备执行目标操作的条件。
例如，可先计算脑电波形曲线和脑氧波形曲线在某一时段内的曲线积分面积，并将计算所得的积分面积作为两波形曲线的特征。再将得到的曲线积分面积做差值，通过差值与设置的阈值的比较来判断是否满足条件，从而根据判断结果控制受控设备执行与该条件对应的目标操作。或者，还可以截取脑电波形曲线和脑氧波形曲线中变化较为剧烈的一段曲线作为特征，将脑电特征曲线和脑氧特征曲线设置权重并线性拟合，以得到的新的曲线方程，将曲线方程与设置的阈值或目标条件进行比较来判断是否满足条件，在满足条件时控制受控设备执行相应的操作。再比如，可分别求脑电波形曲线和脑氧波形曲线的导函数，并提取两导函数的极值，将极值与设置的阈值进行比较，判断是否满足控制受控设备执行相应的操作。在实 际应用中，可以根据实际的判断精度以及判断条件的侧重采用以上任意一种方式进行判断。
本公开实施例提供的基于脑信号的控制方法，相比于现有技术中仅基于脑电信号来控制受控设备的方式，同时检测脑电信号和脑氧信号，并生成脑电波形曲线和脑氧波形曲线，确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，，可以避免单一数据在发生跳变等情况造成误操作，提高控制的准确性。
基于同一公开构思，本公开实施例提供一种基于脑信号的控制设备，结构如图3所示，包括：脑电信号检测装置31、脑氧信号检测装置32以及处理器33。其中，脑电信号检测装置31与脑氧信号检测装置32分别与处理器33耦接。
根据本公开的实施例，处理器33用于控制脑电信号检测装置31与脑氧信号检测装置32在目标时段内周期性地检测脑电信号和脑氧信号，根据检测的脑电信号和脑氧信号分别生成表征脑电信号和脑氧信号在目标时段内变化的脑电波形曲线和脑氧波形曲线；确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件；以及在确定出脑电波形曲线和脑氧波形曲线符合条件时，向受控设备发送控制指令，以使得受控设备执行与控制指令对应的目标操作。
本公开实施例提供的上述基于脑信号的控制设备，控制设备同时检测脑电信号和脑氧信号，并生成脑电波形曲线和脑氧波形曲线，以及确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，这可以避免单一的波形曲线在发生跳变等情况时造成误操作，提高控制设备的准确性。
根据本公开的实施例，在脑电波形曲线和脑氧波形曲线符合条件时，控制设备可控制受控设备执行目标操作，具体可以包括以下几种情况：
(1)在目标时段内，在脑电波形曲线的数值增加量大于或等于第一阈值且脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活 跃状态；控制受控设备执行与大脑活跃状态对应的操作。
(2)在目标时段内，在脑电波形曲线的数值减小量大于或等于第三阈值且脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制受控设备执行与大脑平静状态对应的操作。
(3)在目标时段内，脑电波形曲线与脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值时，保持受控设备执行当前执行的操作。
进一步地，脑电信号检测装置31包括脑电检测电极311。通过使脑电检测电极311和头皮接触，可以将大脑神经细胞产生的电位变动记录下来。其中，放置于零电位上的电极称为参考电极，放置于非零电位上的电极称为作用电极。通过诸如导线将参考电极和作用电极分别和处理器耦接，从而将作用电极和参考电极之间的电位差进行放大。具体地，如图4所示，脑电检测电极311可包括作用电极3111和参考电极3112，将作用电极3111放置于头皮上，参考电极3112放置于耳垂。由于脑电信号具有强噪声背景、低频(0.1～70Hz，低频段放大器输入1/f电压噪声较大)微弱、高内阻、电极极化电位不稳定等特点，因此，前置电压跟随器应同时具备高共模抑制比、低输入1/f、电压噪声低、输入电流噪声低、漂移特性。为了降低输出阻抗和减少引线感应工频的干扰，可选用氯化银粉末电极以降低极化电位，提高极化电位稳定性。
在具体应用中，在本公开实施例提供的上述控制设备中，如图5所示，脑氧信号检测装置包括：检测光源321，以及与检测光源321间隔目标距离(该目标距离可根据本领域技术人员的需要预先设定)的光传感器322。
其中，检测光源321可用于向大脑皮层发射红外光，以使所发射的红外光与大脑皮层的血氧组织相互作用。
光传感器322用于检测未与血氧组织作用而经过大脑皮层反射的红外光。
在实际应用中，检测光源321通常采用射线源。而在本公开实施例中检测光源321采用近红外光源，近红外光源相比于射线源不会损害身体健 康。并且近红外光谱对于血流的影响明显，因此更适合对脑氧信号进行检测。如上所述脑氧信号的检测原理是基于脑组织血流及血红蛋白对近红外光的吸收作用。如图6所示，检测光源321向大脑皮层发射近红外波段的光线，大脑皮层中与血氧状态相关的组织中的血红蛋白等体现了脑氧含量，会对近红外光有吸收作用，因此光传感器322所检测到的光为未被大脑吸收而被反射回来的红外光。那么损失的那部分红外光则被血红蛋白吸收，由此可以由光传感器间接反映大脑血氧的状态。大脑血氧状态又与大脑的活跃程度正相关，从而可将检测的红外光的强度与大脑活跃程度建立关联。而在本公开实施例中，上述的脑氧信号为与大脑血氧为负相关的红外光的强度。
在实际应用中，检测光源321所采用的红外光一般可以穿透一定深度到达皮层，从而探测到血氧信息后反射到光传感器322上。但红外光要从前额穿过整个头部在后脑枕区被探测基本上很难，因此本公开实施例采用反射式检测的方式。除此之外，还需要注意，由于检测光源321的所发射的光对光传感器322的光强检测会产生影响，因此要将检测光源321与光传感器322之间保持目标距离，以使得检测光源321所发射的光不会直接被光传感器322接收，而影响检测结果。优选地，检测光源321与光传感器322之间的距离可设置在2-4cm之间。光传感器322可采用光探头，例如光探头由硅光管(PD管)、跨阻放大器、导光光纤、滤光片、弹簧外壳等部件组成。采用跨阻放大器前置设计，克服了传统光纤探头易引入运动噪声的不足。采用650nm长波通滤光片，可抑制外界光干扰，降低PD管的光电流噪声。
在本公开实施例提供的上述控制设备中，脑电信号与脑氧信号的检测原理不同，两种脑信号的检测互不干涉。两种脑信号可以采用相互独立的装置同时同步进行采集，而采集得到的脑电信号与脑氧信号又都与大脑活跃程度相关，因此采用两种类型的数据可以提高对大脑活跃状态进行判断的准确性。
进一步地，在本公开实施例提供的上述脑氧信号检测装置中，如图7所示，检测光源321包括封装于同一封装结构3211中的第一发光芯片3212和第二发光芯片3213。由于生物组织(包括大脑皮层组织)对近红外波段(650-950nm)的红外光具有高散射且低吸收的特性，使得近红外光能够探测到头皮下2-3cm深度的大脑皮层区域，并具体较高的空间分辨率。而血红蛋白又对该波段的光具有较强的吸收作用，因此，在本公开实施例中采用两个发光芯片。其中，第一发光芯片3212所发射的光的波长为约760nm，半波宽度为约20nm。第二发光芯片3213所发射的光的波长为约850nm，半波宽度为约35nm。上述的两个发光芯片可为发光二极管，且两个发光二极管可采用如图8所示的三种耦接方式。图8中的(a)为两个发光二极管并联的耦接方式；(b)为两个发光二极管共阴极的耦接方式，而(c)为两个发光二极管共阳极的耦接方式。将两个发光芯片同时封装于同一封装结构中，则不需要单独制作两个光源的结构。采用上述多波长一体化的光源，不仅可以优化光源体积，而且能够充分消除普通的分立型光源管因为空间位置离散而对检测结果造成的影响。
在本公开实施例提供的上述控制设备中，如图9所示，还包括：耦接在处理器33与脑电检测电极311之间的第一滤波放大电路34，以及耦接在处理器33与光传感器322之间的第二滤波放大电路35。由于脑电检测电极311和光传感器322检测得到的脑电信号及与脑氧信号相关的光强信号的背景噪声较大，因此滤波放大电路可以对上述两信号根据需要进行滤放处理、优化信号，以形成有效的脑电波形曲线以及脑氧波形曲线。
进一步地，如图9所示，本公开实施例提供的上述控制设备还包括与检测光源321耦接的驱动电路36。在实际应用中，驱动电路36主要由运放NPN三极管电流反馈电阻组成，可将电压载波信号转换成4路5～15ma的电流载波信号，以驱动双波长检测光源321。模拟前端可采用TI公司的ADS1299芯片，包含8个输入多路复用器、低噪声可编程增益放大器和同步采样24位模数转换器。在12倍增益与70Hz带宽条件下，等效输入电 压噪声低于1.0，满足医疗级EEG信号采集要求。
此外，如图9所示，本公开实施例提供的上述控制设备还包括无线传输模块37，用于将处理器33的控制指令发送给受控设备。无线传输模块37可采用EMW3162，最高网络数据传输速率为20Mbps，拥有128k SRAM缓存，可满足实时数据传输需求。无线传输模块37内置微控制器STM32F205RG，可直接对模块进行编程，实现模拟前端通信光源载波生成Wi-Fi网络通讯等功能。
在本公开实施例提供的上述控制设备中，还包括电源模块(未示出)，用于对应近红外发光的驱动电路，光线采集所输出信号的滤波放大电路，脑电检测电极输出的滤波放大电路等部件供电。电源模块可包括充电电路，可使用5V直流电源为单锂电池充电，也可使用DC-DC升压电源，将单锂电池的电压升至6V。两种低压差线性稳压电路，脑电、脑氧信号进行滤波放大之后还可输出到的数据采集模块(未示出)，数据采集模块可以通过发射无线的无线通信模块，或者通过USB端口将采集的数据以无线或有线的方式发送带有处理器33的计算机等设备。电源模块还为无线传输模块内置的DAC模块提供低噪模拟电源。2.5V精密电源参考与电压跟随器组合，提供一个低噪虚拟地。
在具体实施时，本公开实施例提供的上述控制设备集成于如图10所示的由头部佩戴的部件中。该头部佩戴的部件可为运动绷带或头盔等。其中，检测光源321和光传感器322可对应于头部前额的位置，两个脑电检测电极311位于两侧，用于检测脑电信号。当大脑进行运动想象时，脑电信号和脑氧信号会出现相应的变化。大脑的脑电信号在大脑皮层下产生，并被脑电检测电极收集，然后第一滤波放大电路脑电对信号进行放大，最终脑电信号送入数据采集模块(例如数据采集卡)。脑氧信号通过经过第二滤波放大电路处理后也送入数据采集模块。最后通过通信模块将数据传输给处理器13，由处理器13生成脑电波形曲线以及脑氧波形曲线。控制设备确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条 件，最后生成控制指令，以控制受控设备进行相应的操作。
另一方面，本公开实施例还提供一种人机交互设备，包括上述任一基于脑信号的控制设备及受控设备。该受控设备可为基于脑信号被控制的各种形式的受控设备，例如，可为根据本公开实施例的上述的遥控飞行设备等，在此不做具体限定。
本公开实施例提供的基于脑信号控制方法、控制设备及人机交互设备，通过在目标时段内周期性地获取脑电信号和脑氧信号，根据获取的脑电信号和脑氧信号分别生成表征脑电信号和脑氧信号在目标时段内变化的脑电波形曲线和脑氧波形曲线；确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件；在脑电波形曲线和脑氧波形曲线符合条件时，控制受控设备执行目标操作。相比于现有技术中仅基于脑电信号来控制受控设备的方式，本公开实施例提供的上述控制方法，同时检测脑电信号和脑氧信号，并生成脑电波形曲线和脑氧波形曲线，以及确定脑电波形曲线和脑氧波形曲线是否符合控制受控设备执行目标操作的条件，可以避免单一波形曲线的数据在发生跳变等情况时造成受控设备执行错误的操作，提高控制的准确性。
尽管已描述了本公开的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本公开范围的所有变更和修改。
显然，本领域的技术人员可以对本公开进行各种改动和变型而不脱离本公开的精神和范围。这样，倘若本公开的这些修改和变型属于本公开权利要求及其等同技术的范围之内，则本公开也意图包含这些改动和变型在内。

Claims (15)

  1. 一种基于脑信号的控制方法，其中，包括：
    在目标时段内周期性地获取脑电信号和脑氧信号，根据获取的所述脑电信号和所述脑氧信号分别生成表征所述脑电信号和所述脑氧信号在所述目标时段内变化的脑电波形曲线和脑氧波形曲线；
    确定所述脑电波形曲线和所述脑氧波形曲线是否符合控制受控设备执行目标操作的条件；以及
    在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述受控设备执行所述目标操作。
  2. 如权利要求1所述的方法，其中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述受控设备执行所述目标操作，包括：
    在所述目标时段内，在所述脑电波形曲线的数值增加量大于或等于第一阈值且所述脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活跃状态；控制所述受控设备执行与大脑活跃状态对应的操作；或者
    在所述目标时段内，在所述脑电波形曲线的数值减小量大于或等于第三阈值且所述脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制所述受控设备执行与大脑平静状态对应的操作。
  3. 如权利要求1所述的方法，其中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，控制所述受控设备执行所述目标操作，还包括：
    在所述目标时段内，所述脑电波形曲线与所述脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值时，保持所述受控设备执行当前执行的操作。
  4. 如权利要求1所述的方法，其中，确定所述脑电波形曲线和所述脑氧波形曲线是否符合控制所述受控设备执行目标操作的条件，包括：
    提取所述脑电波形曲线中的脑电特征，并提取所述脑氧波形曲线中的脑氧特征；
    将提取出的所述脑电特征与所述脑氧特征进行融合；
    确定融合后的特征是否符合控制所述受控设备执行所述目标操作的条件。
  5. 一种基于脑信号的控制设备，其中，包括：脑电信号检测装置、脑氧信号检测装置以及处理器；其中，所述脑电信号检测装置与所述脑氧信号检测装置分别与所述处理器耦接；
    所述处理器，用于控制所述脑电信号检测装置与所述脑氧信号检测装置在目标时段内周期性地检测脑电信号和脑氧信号，根据检测的所述脑电信号和所述脑氧信号分别生成表征所述脑电信号和所述脑氧信号在所述目标时段内变化的脑电波形曲线和脑氧波形曲线；确定所述脑电波形曲线和所述脑氧波形曲线是否符合控制受控设备执行目标操作的条件；以及在确定出所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，向所述受控设备发送控制指令，以使得所述受控设备执行与所述控制指令对应的目标操作。
  6. 如权利要求5所述的控制设备，其中，在所述脑电波形曲线和所述脑氧波形曲线符合所述条件时，所述受控设备执行与所述控制指令对应的目标操作，包括：
    在所述目标时段内，在所述脑电波形曲线的数值增加量大于或等于第一阈值且所述脑氧波形曲线的数值减小量大于或等于第二阈值时，确定大脑处于活跃状态；控制所述受控设备执行与大脑活跃状态对应的操作；或者
    在所述目标时段内，在所述脑电波形曲线的数值减小量大于或等于第三阈值且所述脑氧波形曲线的数值增加量大于或等于第四阈值时，确定大脑处于平静状态；控制所述受控设备执行与大脑平静状态对应的操作；或者
    在所述目标时段内，所述脑电波形曲线与所述脑氧波形曲线中的至少一种的数值变化量小于各自目标阈值时，保持所述受控设备执行当前执行 的操作。
  7. 如权利要求5所述的控制设备，其中，所述脑电信号检测装置包括脑电检测电极。
  8. 如权利要求5所述的控制设备，其中，所述脑氧信号检测装置包括：检测光源，以及与所述检测光源间隔目标距离的光传感器；其中，
    所述检测光源用于向大脑皮层发射红外光，以使所发射的红外光与大脑皮层的血氧组织相互作用；
    所述光传感器用于检测未与所述血氧组织作用而经过大脑皮层反射的所述红外光。
  9. 如权利要求8所述的控制设备，其中，所述检测光源包括封装于同一封装结构中的第一发光芯片和第二发光芯片；
    所述第一发光芯片所发射的红外光的波长为约760nm，所述第二发光芯片所发射的红外光的波长为约850nm。
  10. 如权利要求7所述的控制设备，其中，还包括：耦接在所述处理器与所述脑电检测电极之间的第一滤波放大电路。
  11. 如权利要求8所述的控制设备，其中，还包括：耦接在所述处理器与所述光传感器之间的第二滤波放大电路。
  12. 如权利要求8所述的控制设备，其中，还包括：与所述检测光源耦接的驱动电路。
  13. 如权利要求5所述的控制设备，其中，还包括：无线传输模块，用于将所述处理器的所述控制指令发送给所述受控设备。
  14. 如权利要求5-13任一项所述的控制设备，其中，所述控制设备集成在由头部佩戴的部件中。
  15. 一种人机交互设备，其中，包括如权利要求5-14任一项所述的控制设备以及受控设备。
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