CN107817731A - Merge unmanned the platform control system and control method of myoelectricity and brain electric information - Google Patents
Merge unmanned the platform control system and control method of myoelectricity and brain electric information Download PDFInfo
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Abstract
The invention discloses a kind of unmanned platform control system for merging myoelectricity and brain electric information, including：Eeg signal acquisition processing module, it is communicated by bluetooth with use processing module；Gesture electromyographic signal collection module, it is communicated by spi bus with described information fusion treatment module；Gesture motion signal acquisition module, it is communicated by serial ports with described information fusion treatment module；Use processing module, it is communicated by serial ports with wireless communication module；Wireless communication module, it is communicated by serial ports with unmanned platform controller.The invention also discloses a kind of unmanned platform control method for merging myoelectricity and brain electric information.Beneficial effects of the present invention：The gesture motion of the whole arm of operator can not only be identified, also can correctly capture operation person action be intended to, realize the accurate control to unmanned platform.
Description
Technical field
The present invention relates to man-machine interactively technical field, in particular to nobody of a kind of fusion myoelectricity and brain electric information
Platform control system and control method.
Background technology
Electromyographic signal is one kind bioelectrical signals as caused by neuron-muscular activity, has been contained much related to limb motion
The information of connection, be muscle bioelectric at skin surface time and synthesis result spatially.EEG signals are a kind of
Very faint non-stationary signal, the bioelectrical activity information of a large amount of cranial nerve cells is contained, embody the thinking activities of people
With limb action behavior.According to the characteristics of electromyographic signal and EEG signals, both connected applications field of human-computer interaction has been arrived into.Hand
Gesture acts one kind as human body behavior act, has the characteristics of convenient and swift, implication is enriched, be easy-to-understand, can allow people
Interacted in daily life in a manner of one kind is more natural, more direct.
At present, gesture interaction is most direct operating method in man-machine interaction, only to operator in existing gesture interaction
Hand motion be identified, the gesture motion of the whole arm (including forearm, upper arm and hand) of operator can not be carried out
Accurate fusion recognition, but it is also possible to trigger hand to act in the motion process of forearm and upper arm, therefore,
The influence of rejecting relative motion is needed during gesture identification, to obtain the correct gesture motion of operator.It is meanwhile existing
Operator's EEG signals and operator's electromyographic signal can not be carried out appropriate fusion treatment by gesture interaction, lead to not correctly catch
Catch operator and correctly act intention, and then cause to cause the unmanned platform of control malfunction and unmanned platform is occurred
It is dangerous.
The content of the invention
To solve the above problems, it is an object of the invention to provide a kind of unmanned platform behaviour for merging myoelectricity and brain electric information
Control system and control method, the gesture motion of the whole arm of operator can not only be identified, also can correct capture operation person
Action be intended to, realize the accurate control to unmanned platform.
The invention provides a kind of unmanned platform control system for merging myoelectricity and brain electric information, including：
Eeg signal acquisition processing module, it is communicated by bluetooth with use processing module, the brain telecommunications
Number acquisition processing module is used for the brain electric information of acquisition operations person, and will the brain electric information that collected carry out treatment classification after export
Fusion treatment is carried out to described information fusion treatment module；
Gesture electromyographic signal collection module, it is communicated by spi bus with described information fusion treatment module, described
Gesture electromyographic signal collection module is used for the hand myoelectric information of acquisition operations person, and the hand myoelectric information denoising that will be collected
And output to described information fusion treatment module carries out fusion treatment after analog-to-digital conversion；
Gesture motion signal acquisition module, it is communicated by serial ports with described information fusion treatment module, the hand
The forearm and humeral movement information of potential motion signal acquisition module acquisition operations person, and by the movable information collected and the hand
The myoelectric information of gesture electromyographic signal collection module output carries out fusion treatment by described information fusion treatment module；
Use processing module, it is communicated by serial ports with wireless communication module, described information fusion treatment mould
Block is adopted for receiving brain electric information, the gesture electromyographic signal collection module that the eeg signal acquisition processing module collects
The forearm and humeral movement information that the hand myoelectric information and the gesture motion signal acquisition module collected collects, by hand
Myoelectric information and forearm and humeral movement use processing, the arm action information of operator is obtained, is realized to operator's hand
The identification of gesture action, then the judgement by arm action information and brain electric information fusion treatment progress coherence, so as to realize operation
The differentiation that person's gesture motion is intended to, the false triggering action caused by passive action or deliberately action is rejected, is obtaining operator just
True gesture motion, and the instruction of correct gesture motion is transmitted to the wireless communication module；
Wireless communication module, it is communicated by serial ports with unmanned platform controller, and the wireless communication module is used for
The gesture motion instruction of described information fusion treatment module output is received, and the gesture motion instruction of operator is transmitted to described
Unmanned platform controller；
Unmanned platform controller, it receives gesture motion instruction, and it is corresponding to control coupled unmanned platform to carry out
Gesture motion.
Improved as of the invention further, the eeg signal acquisition processing module by the dry electrodes of A2, the dry electrodes of Fp2,
The dry electrodes of C4 and brain electro-detection form with process chip, the dry electrode of the A2, the one of the dry electrodes of the Fp2 and the dry electrodes of the C4
End is connected with operator's brain, the other end of the dry electrode of the A2, the dry electrodes of the Fp2 and the dry electrodes of the C4 with it is described
Brain electro-detection is connected with the input of process chip；
The gesture electromyographic signal collection module is made up of eight surface myoelectric poles and modulus conversion chip, wherein four institutes
The one end for stating surface myoelectric pole is connected with one arm of operator, one end of the remaining four surface myoelectric pole with operation
Person's another arm is connected, and the input of the other end of the eight surface myoelectric pole with the modulus conversion chip is connected；
The gesture motion signal acquisition module is made up of four Inertial Measurement Units, inertia measurement list described in two of which
One end of member is connected with one arm of operator respectively, and one end of remaining two Inertial Measurement Units is another with operator respectively
One arm is connected；
Described information fusion treatment module is made up of gesture information processing MCU and flesh brain use processing MCU, the mould
The input that the output end of number conversion chip handles MCU by spi bus with the gesture information is connected, and four inertia are surveyed
The other end for measuring unit is connected by serial ports with gesture information processing MCU input, and the gesture information handles MCU
Output end be connected by serial ports with the input of the flesh brain use processing MCU, the brain electro-detection and process chip
Output end be connected by Bluetooth chip with the input of the flesh brain use processing MCU；
The wireless communication module is made up of a pair of data radio stations, two data radio station composition T-R radio station, the flesh brain
Use processing MCU output end is connected by serial ports with a wherein data radio station, and another data radio station passes through serial ports
It is connected with the unmanned platform controller.
As further improvement of the invention, A2 dry electrode, the dry electrodes of the Fp2 and the dry electrodes of the C4 glue respectively
Be attached to operator's brain auris dextra hang down A2 regions, right forehead Fp2 regions and right crown center C4 regions, and by wire with it is described
Brain electro-detection is connected with process chip, wherein, the positioning of the dry electrode of the A2, the dry electrodes of the Fp2 and the dry electrodes of the C4 according to
According to international 10-20 modular systems, the voltage of the dry electrodes of A2 in the vertical A2 regions of auris dextra is as reference voltage.
As further improvement of the invention, four surface myoelectric poles, four surface fleshes are arranged on every arm of operator
Electrode is pasted onto in a manner of annular array at musculus extensor digitorum, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and the musculus flexor carpi ulnaris of operator's forearm
Skin surface, and be connected by wire with the modulus conversion chip.
As further improvement of the invention, two Inertial Measurement Units, an inertia are arranged on every arm of operator
Measuring unit is tied up near wrist joint by elastic band, and another Inertial Measurement Unit is tied up near elbow joint by elastic band.
As further improvement of the invention, the Inertial Measurement Unit includes power module, three axis accelerometer, three axles
Gyroscope, three axis magnetometer, communication module and microprocessor, the three axis accelerometer, the three-axis gyroscope and described three
Axle magnetometer is connected with the microprocessor, and the microprocessor is handled by the communication module and the gesture information
MCU is connected, and the power module provides power supply, the three axis accelerometer, the three axis accelerometer for the Inertial Measurement Unit
Instrument, the three axis magnetometer are respectively used to acceleration, angular speed and magnetic field intensity during acquisition operations person's arm motion, described micro-
Processor is handled the acceleration, angular speed and magnetic field intensity that collect, parses the forearm and upper arm of every arm
Athletic posture information.
As further improvement of the invention, four surface myoelectric poles on every arm of operator, which gather to refer to respectively, always stretches
Flesh signal, musculus extensor brevis pollicis signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal, through the modulus conversion chip denoising and modulus
The hand motion information of operator is obtained after conversion；
The movable information of two Inertial Measurement Units difference acquisition operations person's forearm and upper arm on every arm of operator；
The gesture information processing MCU carries out fusion treatment to the movable information of hand action message and forearm and upper arm,
The arm action information of operator is obtained, realizes the identification to two arm actions of operator；
A2 dry electrode, the dry electrodes of the Fp2 and the dry electrodes of the C4 gather A2 regions EEG signals, Fp2 areas respectively
Domain EEG signals and C4 regions EEG signals, the brain of operator is obtained after the brain electro-detection and process chip treatment classification
Course；
The flesh brain use processing MCU is carried out at fusion to the arm action information and human thinking information of operator
Reason, and the coherence of arm action message and human thinking information is judged, so as to realize that operator's gesture motion is intended to
Differentiation, obtain the correct gesture motion of operator.
Present invention also offers a kind of unmanned platform control method for merging myoelectricity and brain electric information, including：
Step 1, the vertical A2 regions of auris dextra, right forehead Fp2 regions and right crown center C4 regions difference on operator head
Paste A2 dry electrode, the dry electrodes of Fp2 and the dry electrodes of C4；
An inertia measurement is pinioned near the wrist joint of two arms of operator and near elbow joint with elastic band respectively
Unit；
Skin at the musculus extensor digitorum of the forearm of two arms of operator, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and musculus flexor carpi ulnaris
A surface myoelectric pole is pasted respectively in skin surface；
Step 2, operator starts gesture motion；
A2 dry electrode, the dry electrodes of the Fp2 and the dry electrodes of the C4 gather A2 regions EEG signals, Fp2 areas respectively
Domain EEG signals and C4 regions EEG signals；
Two Inertial Measurement Units difference acquisition operations of the wrist joint of every arm of operator nearby and near elbow joint
Acceleration, angular speed and magnetic field intensity when person's forearm and humeral movement；
Musculus extensor digitorum signal, the thumb that four surface myoelectric poles on the forearm of every arm of operator gather hand respectively are short
Extensor signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal；
Step 3, brain electro-detection and process chip are to the A2 regions EEG signals, Fp2 regions EEG signals and the C4 that collect
Region EEG signals carry out treatment classification, obtain the human thinking information of operator；
Inertial Measurement Unit obtains the movable information of operator's forearm and upper arm, to collect acceleration, angular speed and
Magnetic field intensity is handled, and parses the forearm of every arm and the athletic posture information of upper arm；
The signal denoising that modulus conversion chip collects to surface myoelectric pole handles and analog-to-digital conversion, obtains operator every
The hand motion information of arm；
Step 4, gesture information processing MCU is carried out at fusion to the movable information of hand action message and forearm and upper arm
Reason, obtains the arm action signal i.e. arm action information of operator, realizes the identification to two arm actions of operator；
Step 5, flesh brain use processing MCU merges to the arm action information and human thinking information of operator
Processing, and the coherence of arm action message and human thinking information is judged, reject because of passive action or deliberately action
Caused false triggering action, realizes the differentiation that operator's gesture motion is intended to, and obtains the correct gesture motion of operator；
Step 6, the flesh brain use processing MCU is instructed correct gesture motion by a pair of wireless digital broadcasting stations
It is sent on unmanned platform master controller；
Step 7, the unmanned platform master controller instructs according to the gesture motion of operator, controls unmanned platform to carry out phase
The gesture motion answered.
Further improved as of the invention, in step 3, using wavelet transformation to the A2 regions brain telecommunications after denoising
Number, the time-domain information and frequency domain information of Fp2 regions EEG signals and C4 regions EEG signals analyzed, obtain A2 regions respectively
The frequency and power spectrum function of EEG signals, Fp2 regions EEG signals and C4 regions EEG signals；
In step 4, the time-domain information and frequency domain information of the arm action signal after denoising are entered using wavelet transformation
Row analysis, obtain the frequency and power spectrum function of arm action signal.
Further improved as of the invention, in step 5, reference voltage is used as using the voltage of the dry electrodes of A2；
The coherence factor of the power spectrum function of Fp2 regions EEG signals and the power spectrum function of arm action signal is calculated,
When the coherence factor is less than threshold value set in advance, judges that arm action is no intention action, be otherwise voluntary action；
The coherence factor of the power spectrum function of C4 regions EEG signals and the power spectrum function of arm action signal is calculated, when
When the coherence factor is less than threshold value set in advance, judge that arm action is otherwise positive action for passive action.
Beneficial effects of the present invention are：
1st, by three dry electrodes of brain, the human thinking that can catch fast operator is intended to, and identification gesture is accused dynamic
Make；
2nd, by four surface myoelectric poles of forearm, the hand motion of operator can be identified；
3rd, by forearm from wrist joint at a certain distance from, upper arm is from binding two inertia respectively at a certain distance from elbow joint
Measuring unit, the movable information of forearm and upper arm can be obtained, so, after the movable information for rejecting forearm and upper arm, you can obtain
Obtain the action message of hand so that the action of identification is more accurately and reliably；
4th, the identification of the whole arm action of operator can be achieved, include the identification of upper arm, forearm and hand motion；
5th, human body electroencephalogram's signal and arm motion signal have organically been merged, has been capable of detecting when the meaning of operator's gesture motion
Figure, the false triggering caused by no intention action or passive action is rejected, the reliability of manipulation is improved, it is correct to obtain operator
Gesture motion, realize the accurate manipulation to unmanned platform.
Brief description of the drawings
Fig. 1 is the signal of the unmanned platform control system of a kind of the fusion myoelectricity and brain electric information described in the embodiment of the present invention
Figure.
Embodiment
The present invention is described in further detail below by specific embodiment and with reference to accompanying drawing.
Embodiment 1, as shown in figure 1, the unmanned platform manipulation of a kind of the fusion myoelectricity and brain electric information of the embodiment of the present invention
System, including：Eeg signal acquisition processing module, gesture electromyographic signal collection module, gesture motion signal acquisition module, information
Fusion treatment module, wireless communication module, unmanned platform controller.
Eeg signal acquisition processing module is communicated by bluetooth with use processing module, at eeg signal acquisition
The brain electric information that module is used for acquisition operations person is managed, and output to information after the brain electric information collected progress treatment classification is melted
Close processing module and carry out fusion treatment.
Gesture electromyographic signal collection module is communicated by spi bus with use processing module, gesture myoelectricity letter
Number acquisition module is used for the hand myoelectric information of acquisition operations person, and by the hand myoelectric information denoising collected and analog-to-digital conversion
After export to use processing module carry out fusion treatment.
Gesture motion signal acquisition module is communicated by serial ports with use processing module, and gesture motion signal is adopted
Collect the forearm and humeral movement information of module acquisition operations person, and by the movable information collected and gesture electromyographic signal collection mould
The myoelectric information of block output carries out fusion treatment by use processing module.
Use processing module is communicated by serial ports with wireless communication module, and use processing module is used to connect
Receive the brain electric information that collects of eeg signal acquisition processing module, the hand muscle telecommunications that gesture electromyographic signal collection module collects
The forearm and humeral movement information that breath and gesture motion signal acquisition module collect, by hand myoelectric information and forearm and upper arm
Movable information fusion treatment, the arm action information of operator is obtained, realize the identification to operator's gesture motion, then by arm
Action message carries out the judgement of coherence with brain electric information fusion treatment, so as to realize the differentiation of operator's gesture motion intention,
The false triggering action caused by passive action or deliberately action is rejected, obtains the correct gesture motion of operator, and will be correct
Gesture motion instruction transmit to wireless communication module.
Wireless communication module is communicated by serial ports with unmanned platform controller, and wireless communication module is used for receive information
The gesture motion instruction of fusion treatment module output, and the gesture motion instruction of operator is transmitted to unmanned platform controller.
Unmanned platform controller receives gesture motion instruction, and controls coupled unmanned platform to carry out corresponding gesture
Action.
The specific design of above-mentioned modules and connection are as follows：
Eeg signal acquisition processing module is by the dry electrodes of A2, the dry electrodes of Fp2, the dry electrodes of C4 and brain electro-detection and process chip
Composition, one end of the dry electrodes of A2, the dry electrodes of Fp2 and the dry electrodes of C4 are connected with operator's brain, the dry electrodes of A2, the dry electrodes of Fp2
It is connected with the other end of the dry electrodes of C4 with brain electro-detection with the input of process chip.
Gesture electromyographic signal collection module is made up of eight surface myoelectric poles and modulus conversion chip, wherein four surface fleshes
One end of electrode is connected with one arm of operator, one end of remaining four surface myoelectric pole with operator's another arm
It is connected, the other end of eight surface myoelectric pole is connected with the input of modulus conversion chip.
Gesture motion signal acquisition module is made up of four Inertial Measurement Units, one end of two of which Inertial Measurement Unit
Be connected respectively with one arm of operator, one end of remaining two Inertial Measurement Units respectively with operator's another arm phase
Even.
Use processing module is made up of gesture information processing MCU and flesh brain use processing MCU, analog-to-digital conversion core
The input that the output end of piece handles MCU by spi bus with gesture information is connected, and the other end of four Inertial Measurement Units is equal
The input for handling MCU with gesture information by serial ports is connected, and gesture information processing MCU output end is believed by serial ports and flesh brain
Breath fusion treatment MCU input is connected, and the output end of brain electro-detection and process chip is melted by Bluetooth chip and flesh brain information
The input for closing processing MCU is connected.
Wireless communication module is made up of a pair of data radio stations, two data radio station composition T-R radio station, flesh brain information fusion
Processing MCU output end is connected by serial ports with a wherein data radio station, and another data radio station is flat with nobody by serial ports
Platform controller is connected.
Further, the dry electrodes of A2, the dry electrodes of Fp2 and the dry electrodes of C4 are respectively adhered on the vertical A2 areas of auris dextra of operator's brain
Domain, right forehead Fp2 regions and right crown center C4 regions, and be connected by wire with brain electro-detection with process chip, wherein, A2
The basis on location world 10-20 modular systems of the dry electrode of dry electrode, Fp2 and the dry electrodes of C4, the dry electrodes of A2 in the vertical A2 regions of auris dextra
Voltage as reference voltage.Brain electro-detection preferably uses ThinkGear AM chips with process chip, and it can be directly with doing
Electrode is connected, and can detect atomic weak EEG signals, may filter that noise, anti-interference, restores original EEG signals.
10-20 modular systems, the measurement of its fore-and-aft direction be on the basis of the median line being linked to be from the nasion to occipital tuberosity,
The distance is divided into 10 equal portions, mark is carried out by 10%, 20%, 20%, 20%, 20%, 10% order, in this line or so etc.
Away from corresponding site calibrate left and right forehead point (Fp1, Fp2), volume point (F3, F4), intermediate point (C3, C4), summit (P3, P4) and
Rest the head on point (O1, O2), the position of forehead point on the nasion equivalent to the nasion to occipital tuberosity 10% at, volume point phase after forehead point
When twice in the nasion to forehead point distance, i.e., at nasion median line distance 20%, center, the interval pushed up, rest the head on all points are equal backward
For 20%.Because the EEG signals of human body include spontaneous EEG signals and gesture motion induction property EEG signals.Spontaneous brain
Electric signal is the spontaneous caused rhythmic electric potential signal of brain, can pass through the EEG signals in the Fp2 regions on head and carry out
Detection.Gesture motion induction property EEG signals can be detected by the EEG signals in head C4 regions.Therefore, in brain
When arranging electrode, it is arranged in hang down A2 regions, right forehead Fp2 regions, three claw in right crown center C4 regions of auris dextra and does
Electrode, in subsequent treatment, the voltage of voltage using the dry electrodes of A2 of the dry electrodes of Fp2 and the dry electrodes of C4 is used as reference voltage.
Further, four surface myoelectric poles are arranged on every arm of operator, four surface myoelectric poles are with annular array
Mode is pasted onto the skin surface at musculus extensor digitorum, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and the musculus flexor carpi ulnaris of operator's forearm, and leads to
Wire is crossed with modulus conversion chip to be connected.Left arm and right arm arrange four surface myoelectric poles, to distinguish left hand and the right side
The hand motion information of hand.Modulus conversion chip preferably uses ADS1298 chips, and it has been internally integrated 8 low noises and may be programmed
Gain amplifier (PGA) and 8 24 high-resolution analog-digital converters (can be to 8 surface myoelectric poles on two arms of reply
Electromyographic signal carries out analog-to-digital conversion, does not disturb mutually, improves conversion efficiency and accuracy), common-mode rejection ratio reaches -115dB,
Built-in drive circuit can effectively suppress Hz noise.SPI digital interfaces are integrated with, sample frequency reaches as high as 32kHz.Use
The chip can greatly improve level of integrated system, while also improve the stability of system.ADS1298 major function is logical
Cross control its internal register to realize, such as signal input mode, sampling rate, multiplication factor.ADS1298 passes through SPI
Communicated with ppu, realize the synchronous transmitting-receiving of data.ADS1298 reference voltage may be configured as 2.4V or 4V, because
Resolution ratio for it is 24b, so minimum distinguishable voltage is respectively 0.286 μ F, 0.477 μ F.And the amplitude of EEG signals
Generally 0.001~0.1mV, so enhanced processing, its amplification carried need not be passed through again before signal enters ADS1298
Module can just meet to require, thus enormously simplify signal conditioning circuit, greatly reduce overall brain flesh signal acquisition dress
The area and volume put.
Further, two Inertial Measurement Units are arranged on every arm of operator, an Inertial Measurement Unit passes through pine
Taut band is tied up near wrist joint and (preferably tied up at forearm is from 4~5cm of wrist joint), and another Inertial Measurement Unit passes through elastic band
Tie up near elbow joint and (preferably tie up at upper arm is from 6~7cm of elbow joint).Left arm and right arm arrange two inertia measurements
Unit, to distinguish left arm, the forearm of right arm and humeral movement information.Human arm will parse arm in motion
All actions are extremely complex.Because the arm of people includes hand, forearm and upper arm, humeral movement can drive forearm to transport
Dynamic, i.e. the athletic meeting of shoulder joint influences the motion of upper arm and forearm, and the motion of elbow joint influences the motion of forearm, hand exercise meeting
Forearm motion is driven, i.e., carpal athletic meeting influences the motion of forearm and hand.Although four surface fleshes are arranged on forearm
Electrode (obtains hand exercise letter to obtain the electromyographic signal at musculus extensor digitorum, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and musculus flexor carpi ulnaris
Breath), still, the motion of forearm and upper arm also results in hand and moved, therefore, if only four surface fleshes of forward arm
Electrode can not entirely accurate acquisition hand movable information, wherein forearm and the movable information of humeral movement may be mingled with.
Therefore, by forearm from wrist joint at a certain distance from, upper arm is from binding two inertia measurements respectively at a certain distance from elbow joint
Unit, to obtain the movable information of forearm and upper arm, so, after the movable information for rejecting forearm and upper arm, you can obtain hand
The action message in portion so that the action of identification is more accurately and reliably.
Further, Inertial Measurement Unit include power module, three axis accelerometer, three-axis gyroscope, three axis magnetometer,
Communication module and microprocessor, three axis accelerometer, three-axis gyroscope and three axis magnetometer are connected with microprocessor, microprocessor
Device handles MCU with gesture information by communication module and is connected, and power module provides power supply, 3-axis acceleration for Inertial Measurement Unit
Acceleration, angular speed and magnetic field when meter, three-axis gyroscope, three axis magnetometer are respectively used to acquisition operations person's arm motion is strong
Degree, microprocessor handled the acceleration, angular speed and magnetic field intensity that collect, parses the forearm of every arm and upper
The athletic posture information of arm.Inertial Measurement Unit in navigation field is used in human body gesture identification, utilizes its three dimensions
Acceleration, angular speed and magnetic field intensity, the posture of forearm and upper arm can be calculated, obtain accurate athletic posture information, it is real
Existing arm and humeral movement information accurately identify.
Based on above-mentioned setting, four surface myoelectric poles on every arm of operator gather musculus extensor digitorum signal, thumb respectively
Extensor hallucis brevis signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal, behaviour is obtained after modulus conversion chip denoising and analog-to-digital conversion
The hand motion information of author.Two Inertial Measurement Units difference acquisition operations person's forearm and upper arm on every arm of operator
Movable information.Gesture information handles MCU and carries out fusion treatment to the movable information of hand action message and forearm and upper arm, obtains
The arm action information of extract operation person, realizes the identification to two arm actions of operator.The dry electrodes of A2, the dry electrodes of Fp2 and C4
Dry electrode gathers A2 regions EEG signals, Fp2 regions EEG signals and C4 regions EEG signals respectively, through brain electro-detection and processing
The human thinking information of operator is obtained after chip treatment classification.Arm actions of the flesh brain use processing MCU to operator
Information and human thinking information carry out fusion treatment, and the coherence of arm action message and human thinking information is sentenced
It is disconnected, so as to realize the differentiation of operator's gesture motion intention, obtain the correct gesture motion of operator.
Embodiment 2, a kind of unmanned platform control method for merging myoelectricity and brain electric information, including：
Step 1, the vertical A2 regions of auris dextra, right forehead Fp2 regions and right crown center C4 regions difference on operator head
Paste A2 dry electrode, the dry electrodes of Fp2 and the dry electrodes of C4；
An inertia measurement is pinioned near the wrist joint of two arms of operator and near elbow joint with elastic band respectively
Unit；
Skin at the musculus extensor digitorum of the forearm of two arms of operator, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and musculus flexor carpi ulnaris
A surface myoelectric pole is pasted respectively in skin surface.
Step 2, operator starts gesture motion；
The dry electrodes of A2, the dry electrodes of Fp2 and the dry electrodes of C4 gather respectively A2 regions EEG signals, Fp2 regions EEG signals and
C4 regions EEG signals；
Musculus extensor digitorum signal, the thumb that four surface myoelectric poles on the forearm of every arm of operator gather hand respectively are short
Extensor signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal；
Two Inertial Measurement Units difference acquisition operations of the wrist joint of every arm of operator nearby and near elbow joint
Acceleration, angular speed and magnetic field intensity when person's forearm and humeral movement.
Step 3, brain electro-detection and process chip are to the A2 regions EEG signals, Fp2 regions EEG signals and the C4 that collect
Region EEG signals carry out treatment classification, obtain the human thinking information of operator；
Wherein, when handling eeg signal classification, can according to the frequency of EEG signals come distinguish be which region brain
Electric signal, due to using ThinkGear AM chips, even if atomic weak EEG signals may also detect that, while can also mistake
Noise is filtered, restores the original EEG signals of regional.A2 regions EEG signals are as reference signal, the brain in Fp2 regions
Electric signal is used to identify spontaneous EEG signals, and the EEG signals in head C4 regions are used to identify gesture motion induction property brain telecommunications
Number.
Inertial Measurement Unit obtains the movable information of operator's forearm and upper arm, to collect acceleration, angular speed and
Magnetic field intensity is handled, and parses the forearm of every arm and the athletic posture information of upper arm.
Wherein, when the acceleration, angular speed and magnetic field intensity to collecting are handled, first using smoothing filter pair
The signal filtering process collected, interference signal is removed, and parsed using expanded Kalman filtration algorithm in three dimensions
Athletic posture, accurately to obtain the athletic posture information of the forearm of every arm and upper arm.
The signal denoising that modulus conversion chip collects to surface myoelectric pole handles and analog-to-digital conversion, obtains operator every
The hand motion information of arm.
Wherein,, can be to 8 tables on two arms due to using ADS1298 chips when handling surface flesh signal
The electromyographic signal of facial muscle electrode carries out analog-to-digital conversion simultaneously, rejects mutual interference, it is ensured that the accuracy of identification, Ji Nengtong
When action to left hand hand and right hand hand be identified.Simultaneously as Inertial Measurement Unit has parsed every arm
Forearm and upper arm athletic posture information, at this point it is possible to reject hand exercise caused by forearm and humeral movement, obtain simple
During hand exercise, hand exercise information, more accurately identify hand motion.
Further, the A2 regions EEG signals after being handled with wavelet transformation denoising classification, Fp2 regions brain telecommunications are also needed
Number and the time-domain information and frequency domain information of C4 regions EEG signals analyzed, obtain A2 regions EEG signals, Fp2 regions respectively
The frequency and power spectrum function of EEG signals and C4 regions EEG signals, it is easy to the judgement of follow-up brain flesh signal coherency.Using
Wavelet Transformation Algorithm is relatively easy, and data processing amount is smaller, and arithmetic speed is very fast, and real-time is preferable.
Step 4, gesture information processing MCU is carried out at fusion to the movable information of hand action message and forearm and upper arm
Reason, obtains the arm action signal i.e. arm action information of operator, realizes the identification to two arm actions of operator.
In fusion treatment, the movable information of upper arm, forearm and hand is merged, can be achieved what arm completely acted
Identification.
Further, the time-domain information and frequency domain information of the arm action signal after denoising are carried out with wavelet transformation
Analysis, the frequency and power spectrum function of arm action signal are obtained, is easy to the judgement of follow-up brain flesh signal coherency.
Step 5, flesh brain use processing MCU merges to the arm action information and human thinking information of operator
Processing, and the coherence of arm action message and human thinking information is judged, reject because of passive action or deliberately action
Caused false triggering action, realizes the differentiation that operator's gesture motion is intended to, and obtains the correct gesture motion of operator.
Specifically, using following determination methods：
Reference voltage is used as using the voltage of the dry electrodes of A2；
The coherence factor of the power spectrum function of Fp2 regions EEG signals and the power spectrum function of arm action signal is calculated,
When the coherence factor is less than threshold value set in advance, judges that arm action is no intention action, be otherwise voluntary action；
The coherence factor of the power spectrum function of C4 regions EEG signals and the power spectrum function of arm action signal is calculated, when
When the coherence factor is less than threshold value set in advance, judge that arm action is otherwise positive action for passive action.
Wherein, in the threshold value setting to coherence factor, obtained by multigroup experimental data.For example, manipulator is allowed to make
Multigroup gesture motion, the voluntary action of identical gesture motion (hand and/or upper arm and/or forearm act various combinations),
No intention action is respectively done once, records Fp2 regions EEG signals, arm action signal when doing voluntary action, no intention action respectively
Time domain, frequency domain information, calculate Fp2 regions EEG signals, the coherence of arm action signal of record, determine to judge actively
Action and the coherence factor passively acted.Allow manipulator to make multigroup gesture motion, identical gesture motion (hand and/or
Upper arm and/or forearm act various combinations) positive action, passive action respectively do once, record does positive action and quilt respectively
C4 regions EEG signals, the time domain of arm action signal, frequency domain information during action, calculate the C4 regions brain telecommunications of record
Number, electromyographic signal carry out coherence respectively, determine the coherence factor for judging positive action and passively acting.
When calculating coherence factor, using equation below：
Wherein,
SS,N(f) it is EEG signals and the cross-spectrum of arm action signal,
SSS(f) composing certainly for arm action signal,
SNN(f) composing certainly for EEG signals,
Wherein, Si(f) it is arm electromyographic signal function, Ni(f) it is EEG signals function, Si *(f) it is arm electromyographic signal
Conjugate function, Ni *(f) it is EEG signals conjugate function.
Step 6, correct gesture motion is instructed and sent by flesh brain use processing MCU by a pair of wireless digital broadcasting stations
Onto unmanned platform master controller.
Step 7, unmanned platform master controller instructs according to the gesture motion of operator, controls unmanned platform to carry out corresponding
Gesture motion.Wherein, unmanned platform can be unmanned aerial vehicle, unmanned automobile etc..
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (10)
- A kind of 1. unmanned platform control system for merging myoelectricity and brain electric information, it is characterised in that including：Eeg signal acquisition processing module, it is communicated by bluetooth with use processing module, and the EEG signals are adopted Collect the brain electric information that processing module is used for acquisition operations person, and exported after the brain electric information collected is carried out into treatment classification to institute State use processing module and carry out fusion treatment；Gesture electromyographic signal collection module, it is communicated by spi bus with described information fusion treatment module, the gesture Electromyographic signal collection module is used for the hand myoelectric information of acquisition operations person, and by the hand myoelectric information denoising collected and mould Output to described information fusion treatment module carries out fusion treatment after number conversion；Gesture motion signal acquisition module, it is communicated by serial ports with described information fusion treatment module, the gesture fortune The forearm and humeral movement information of dynamic signal acquisition module acquisition operations person, and by the movable information collected and the gesture flesh The myoelectric information of electrical signal collection module output carries out fusion treatment by described information fusion treatment module；Use processing module, it is communicated by serial ports with wireless communication module, and described information fusion treatment module is used Collected in receiving brain electric information, the gesture electromyographic signal collection module that the eeg signal acquisition processing module collects Hand myoelectric information and the forearm that collects of the gesture motion signal acquisition module and humeral movement information, by hand myoelectricity Information and forearm and humeral movement use processing, the arm action information of operator is obtained, realizes and operator's gesture is moved The identification of work, then the judgement by arm action information and brain electric information fusion treatment progress coherence, so as to realize operator's hand The differentiation that gesture action is intended to, the false triggering action caused by passive action or deliberately action is rejected, it is correct to obtain operator Gesture motion, and the instruction of correct gesture motion is transmitted to the wireless communication module；Wireless communication module, it is communicated by serial ports with unmanned platform controller, and the wireless communication module is used to receive The gesture motion instruction of described information fusion treatment module output, and by the instruction of the gesture motion of operator transmit to it is described nobody Platform controller；Unmanned platform controller, it receives gesture motion instruction, and controls unmanned platform to carry out corresponding gesture motion.
- 2. unmanned platform control system according to claim 1, it is characterised in that the eeg signal acquisition processing module It is made up of the dry electrodes of A2, the dry electrodes of Fp2, the dry electrodes of C4 and brain electro-detection and process chip, the dry electrode of the A2, the Fp2 are done One end of electrode and the dry electrodes of the C4 is connected with operator's brain, A2 dry electrode, the dry electrodes of the Fp2 and the C4 The other end of dry electrode is connected with the brain electro-detection with the input of process chip；The gesture electromyographic signal collection module is made up of eight surface myoelectric poles and modulus conversion chip, wherein four tables One end of facial muscle electrode is connected with one arm of operator, and one end of remaining four surface myoelectric poles is another with operator One arm is connected, and the input of the other end of the eight surface myoelectric pole with the modulus conversion chip is connected；The gesture motion signal acquisition module is made up of four Inertial Measurement Units, Inertial Measurement Unit described in two of which One end is connected with one arm of operator respectively, one end of remaining two Inertial Measurement Units respectively with operator's another Arm is connected；Described information fusion treatment module is made up of gesture information processing MCU and flesh brain use processing MCU, and the modulus turns The output end for changing chip is connected by spi bus with gesture information processing MCU input, four inertia measurement lists The input that the other end of member handles MCU by serial ports with the gesture information is connected, and the gesture information processing MCU's is defeated Go out end be connected by serial ports with the input of the flesh brain use processing MCU, the brain electro-detection and process chip it is defeated Go out end by Bluetooth chip with the input of the flesh brain use processing MCU to be connected；The wireless communication module is made up of a pair of data radio stations, two data radio station composition T-R radio station, the flesh brain information Fusion treatment MCU output end is connected by serial ports with a wherein data radio station, and another data radio station passes through serial ports and institute Unmanned platform controller is stated to be connected.
- 3. unmanned platform control system according to claim 2, it is characterised in that the dry electricity of the dry electrode of the A2, the Fp2 Pole and the dry electrodes of the C4 are respectively adhered on the vertical A2 regions of auris dextra, right forehead Fp2 regions and the right crown center of operator's brain C4 regions, and being connected by wire with the brain electro-detection with process chip, wherein, the dry electrode of the A2, the dry electrodes of the Fp2 With the basis on location world 10-20 modular systems of the dry electrodes of the C4, the voltage of the dry electrodes of A2 in the vertical A2 regions of auris dextra is as base Quasi- voltage.
- 4. unmanned platform control system according to claim 2, it is characterised in that four are arranged on every arm of operator Surface myoelectric pole, four surface myoelectric poles are pasted onto the musculus extensor digitorum of operator's forearm, musculus extensor brevis pollicis in a manner of annular array, referred to Skin surface at musculus flexor superficialis and musculus flexor carpi ulnaris, and be connected by wire with the modulus conversion chip.
- 5. unmanned platform control system according to claim 2, it is characterised in that two are arranged on every arm of operator Inertial Measurement Unit, an Inertial Measurement Unit are tied up near wrist joint by elastic band, and another Inertial Measurement Unit passes through Elastic band is tied up near elbow joint.
- 6. unmanned platform control system according to claim 2, it is characterised in that the Inertial Measurement Unit includes power supply Module, three axis accelerometer, three-axis gyroscope, three axis magnetometer, communication module and microprocessor, the three axis accelerometer, The three-axis gyroscope and the three axis magnetometer are connected with the microprocessor, and the microprocessor passes through the communication mould Block is connected with gesture information processing MCU, and the power module provides power supply for the Inertial Measurement Unit, and three axle adds Acceleration, angle speed when speedometer, the three-axis gyroscope, the three axis magnetometer are respectively used to acquisition operations person's arm motion Degree and magnetic field intensity, the microprocessor are handled the acceleration, angular speed and magnetic field intensity that collect, parse every The forearm of arm and the athletic posture information of upper arm.
- 7. unmanned platform control system according to claim 2, it is characterised in that four tables on every arm of operator Facial muscle electrode gathers musculus extensor digitorum signal, musculus extensor brevis pollicis signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal respectively, through described The hand motion information of operator is obtained after modulus conversion chip denoising and analog-to-digital conversion；The movable information of two Inertial Measurement Units difference acquisition operations person's forearm and upper arm on every arm of operator；The gesture information processing MCU carries out fusion treatment to the movable information of hand action message and forearm and upper arm, obtains The arm action information of operator, realizes the identification to two arm actions of operator；A2 dry electrode, the dry electrodes of the Fp2 and the dry electrodes of the C4 gather A2 regions EEG signals, Fp2 regions brain respectively Electric signal and C4 regions EEG signals, the human thinking of operator is obtained after the brain electro-detection and process chip treatment classification Information；The flesh brain use processing MCU carries out fusion treatment to the arm action information and human thinking information of operator, And the coherence of arm action message and human thinking information is judged, so as to realize sentencing for operator's gesture motion intention Not, the correct gesture motion of operator is obtained.
- A kind of 8. control method for the unmanned platform control system for merging myoelectricity and brain electric information, it is characterised in that including：Step 1, auris dextra vertical A2 regions, right forehead Fp2 regions and right crown center C4 regions on operator head are pasted respectively The dry electrodes of A2, the dry electrodes of Fp2 and the dry electrodes of C4；An Inertial Measurement Unit is pinioned near the wrist joint of two arms of operator and near elbow joint with elastic band respectively；Skin table at the musculus extensor digitorum of the forearm of two arms of operator, musculus extensor brevis pollicis, musculus flexor digitorum sublimis and musculus flexor carpi ulnaris A surface myoelectric pole is pasted respectively in face；Step 2, operator starts gesture motion；A2 dry electrode, the dry electrodes of the Fp2 and the dry electrodes of the C4 gather A2 regions EEG signals, Fp2 regions brain respectively Electric signal and C4 regions EEG signals；Before two Inertial Measurement Units difference acquisition operations person of the wrist joint of every arm of operator nearby and near elbow joint Acceleration, angular speed and magnetic field intensity when arm and humeral movement；Four surface myoelectric poles on the forearm of every arm of operator gather musculus extensor digitorum signal, the musculus extensor brevis pollicis of hand respectively Signal, musculus flexor digitorum sublimis signal and musculus flexor carpi ulnaris signal；Step 3, brain electro-detection and process chip are to the A2 regions EEG signals, Fp2 regions EEG signals and the C4 regions that collect EEG signals carry out treatment classification, obtain the human thinking information of operator；Inertial Measurement Unit obtains the movable information of operator's forearm and upper arm, to the acceleration, angular speed and magnetic field collected Intensity is handled, and parses the forearm of every arm and the athletic posture information of upper arm；The signal denoising that modulus conversion chip collects to surface myoelectric pole handles and analog-to-digital conversion, obtains every arm of operator Hand motion information；Step 4, gesture information processing MCU carries out fusion treatment to the movable information of hand action message and forearm and upper arm, obtains The arm action signal of extract operation person is arm action information, realizes the identification to two arm actions of operator；Step 5, flesh brain use processing MCU is carried out at fusion to the arm action information and human thinking information of operator Reason, and the coherence of arm action message and human thinking information is judged, reject because of passive action or deliberately act institute Caused by false triggering act, realize operator's gesture motion be intended to differentiation, obtain the correct gesture motion of operator；Step 6, correct gesture motion is instructed and sent by the flesh brain use processing MCU by a pair of wireless digital broadcasting stations Onto unmanned platform master controller；Step 7, the unmanned platform master controller instructs according to the gesture motion of operator, controls unmanned platform to carry out corresponding Gesture motion.
- 9. control method according to claim 8, it is characterised in that in step 3, using wavelet transformation to denoising after A2 regions EEG signals, Fp2 regions EEG signals and C4 regions EEG signals time-domain information and frequency domain information analyzed, The frequency and power spectrum function of A2 regions EEG signals, Fp2 regions EEG signals and C4 regions EEG signals are obtained respectively；In step 4, the time-domain information and frequency domain information of the arm action signal after denoising are divided using wavelet transformation Analysis, obtain the frequency and power spectrum function of arm action signal.
- 10. control method according to claim 9, it is characterised in that in step 5, base is used as using the voltage of the dry electrodes of A2 Quasi- voltage；The coherence factor of the power spectrum function of Fp2 regions EEG signals and the power spectrum function of arm action signal is calculated, when this When coherence factor is less than threshold value set in advance, judges that arm action is no intention action, be otherwise voluntary action；The coherence factor of the power spectrum function of C4 regions EEG signals and the power spectrum function of arm action signal is calculated, when the phase When responsibility number is less than threshold value set in advance, judge that arm action is otherwise positive action for passive action.
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