CN101569569B - Interface system of human brain and manipulator in micro-power wireless communication mode - Google Patents
Interface system of human brain and manipulator in micro-power wireless communication mode Download PDFInfo
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- CN101569569B CN101569569B CN2009100119984A CN200910011998A CN101569569B CN 101569569 B CN101569569 B CN 101569569B CN 2009100119984 A CN2009100119984 A CN 2009100119984A CN 200910011998 A CN200910011998 A CN 200910011998A CN 101569569 B CN101569569 B CN 101569569B
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Abstract
The invention relates to an interface system of a human brain and a manipulator in a micro-power wireless communication mode, which belongs to the intercrossed field of biomedical engineering and mechanical electronic engineering. The interface system comprises a signal acquisition device, a signal analysis module finished by a computer, and a wireless communication module, wherein the signal acquisition device acquires electroencephalogram signals; the electroencephalogram signals enter the computer, and the signal analysis module adopts power spectrum density analysis to detect the power spectrum densities of frequency points A, B and C of the electroencephalogram signals; the frequencies of the three points A, B and C are between 2 and 30 Hz; when the power spectrum densities of the frequency points is between 2 and 50 microvolts, the characteristic information of the frequency points is 1, otherwise, the characteristic information of the frequency points is 0; and the electroencephalogram signals form the characteristic information consisting of three binary numeral values, and the characteristic information is sent to the manipulator through the wireless communication module. The interface system adopts a multi-channel electroencephalogram data acquisition mode, a wireless mode can better solve the problem, and the interface system is convenient for a user to wear.
Description
Technical field
The invention belongs to the crossing domain of biomedical engineering and Mechatronic Engineering, relate to the behavior that mechanical hand is controlled in a kind of thinking with brain.In whole system, EEG signals (EEG) is combined with mechanical hand, from the brain electricity, extract the signal of effective control manipulator behavior, be translated into control signal again, and the micro-power wireless communication of employing frequency shift keying (FSK) modulation system is controlled the action of mechanical hand.
Background technology
" nature " magazines in 2008 have been delivered a newest research results of Univ. of Pittsburgh.The experimenter is implanted to the motor region of Rhesus Macacus brain with a microelectrode array, gathers the discharge signal of a plurality of neurocytes, through the real-time processing of computer, converts the control command of electronic artificial limb to.Through training after a while, monkey has learned directly to control with the cerebral nerve signal of oneself motion of artificial limb, grasps food and feeds oneself in the mouth.In this process, monkey has reached very high accuracy to the control of extracting dynamics and artificial limb movement locus, almost artificial limb has been treated as the arm next " use " of oneself.
The patent of invention of " adopting the prosthetic hand and the control method thereof of myoelectricity and brain electricity Collaborative Control " has been declared by University Of Tianjin, and output signal links to each other with electronic prosthetic hand drive circuit.
Mechanical hand has multiple classification form, be fit to the apery type that the mostly is mechanical hand that patients with amputation uses,, can be divided into the mechanical hand of different degree of freedom again according to the difference that adopts control motor number, in general, the mechanical hand that degree of freedom is many more can be finished complicated operations more more.Doing evil through another person of being used in the present patent application control is five-finger type apery mechanical hand, and this device is made up of palm and five fingers, and each finger can be finished flexibly and be made a gesture by independently direct current generator driving, gets different actions such as thing.
At present, EEG signals is extracted the direct-connected modes of holding wire that adopt in identification equipment and the mechanical hand control appliance more, and such connection must bring the inconvenience of user, tends to because the contact problems of signal transmssion line cause system reliability to descend.The action of mechanical hand is finished by joints such as motor-driven fingers, will inevitably bring bigger electrical Interference, if said two units connects by communication line, not only on the reliability of signal transmission, can descend, the interference of motor action also can have influence on EEG measuring and analyze link.
Summary of the invention
Based on the existing mechanical handfighting drill, human brain under the micro-power wireless communication mode-mechanical hand interface system is provided, realize a kind of wireless telecommunications, the high-efficiency reliable human brain-control mode of doing evil through another person.Simultaneously, five degree of freedom apery mechanical hand can better imitate the staff action, satisfies the operation requirement of patients with amputation, improves the quality of its life, reduces its degree of dependence to the kinsfolk.
Native system is made up of signals collecting control appliance, portable computer and human brain thereof-mechanical hand control software, wherein controls running software on portable computer.
The technical solution used in the present invention is: the signal analyse block and the wireless transport module that comprise signal collecting device, finished by computer, wherein signal collecting device is gathered EEG signals, EEG signals enters computer, finish analytical judgment by signal analyse block then to signal, the analysis of module by analysis, the characteristic information of different brain electricity is formed different command modes, send to mechanical hand by wireless communication module then.
Analysis module adopts three kinds of modes to analyze:
Power spectral-density analysis:
The full frequency band signal is analyzed, the variation relation of the power spectral density by obtaining a plurality of Frequency points dynamically forms a multidimensional change sequence, detects EEG signals A second every t, B, three Frequency point power spectral densities of C, A, B, C three dot frequencies are at 2～30HZ, when the power spectral density of Frequency point more than or equal to setting threshold, getting this Frequency point characteristic information is 1, otherwise is 0; EEG signals forms the characteristic information of being made up of three binary numerals, and characteristic information sends to mechanical hand by wireless communication module.
As respectively to A, B, the Frequency point of C detects, when B at first reaches threshold value, A and C component of signal reach threshold value in next time period (after 0.5 second), and B drops to normal range, just formed the characteristic information of ([0 1 0] [1 0 1]), compared the pattern of judgment signal with characteristic sequence.
A kind of optimal way: detected EEG signals 5Hz, 10Hz and 22Hz Frequency point power spectral density every 0.2～1 second.Definition ([0 1 0] [1 0 1]) respectively, ([0 1 0] [0 0 0]) and ([1 1 0] [0 1 0] [0 0 0]) are pattern 1, pattern 2 and mode 3.
Threshold decision is analyzed:
Analysis to the amplitude characteristics of EEG signals, choose amplitude range [0,100] microvolt, the EEG signals of average [30,30] microvolt, in the analysis at first to data fetch average and the amplitude of a period of time, result who obtains and parameter preset scope are compared, determine its effectiveness (amplitude range [0,100] microvolt, average [30,30] microvolt).Utilize the spectrum analysis instrument the α ripple then, relatively (amplitude threshold is set in [5 to information such as the amplitude of frequency band correspondences such as β ripple and root-mean-square with preset threshold respectively, 50] between microvolt), select to differentiate type according to it, as exceeding at α frequency band root-mean-square between threshold value [5,50] microvolt, can judge that it is movable relevant with the visual processes neuroelectricity, be defined as pattern 4, the root-mean-square of β frequency band exceeded threshold range ([10,30] microvolt) be defined as pattern 5.
Wavelet transformation analysis:
Wavelet analysis method is a kind of analytical method that satisfies preservation of energy, and it does not lose original information to paired time of signal decomposition and frequency signal independently simultaneously.Can study the dynamic characteristic of signal by selecting different scale, EEG signals is resolved into time and frequency signal independently, two yardstick equations of wavelet analysis are as follows:
In the formula
Be scaling function, ψ is a wavelet function.h
kBe one group of low-pass filtering coefficient, multiplying each other with it to get low frequency signal, and g
kBe the high-pass filtering coefficient, x and k are the ordinal number of data point in sequence.EEG signals is handled through multiresolution analysis, utilizes the h of two yardstick equations
kAnd g
kSignal decomposition is become low frequency and two parts of high frequency, if [6.25Hz, 12.5Hz] frequency band signals amplitude is [20,50] microvolt scope, and the root-mean-square of relative wavelet coefficient is between [0.1,0.5], be defined as pattern 6, for the EEG signals between [18.75Hz, 25Hz], if its amplitude is [20,30] between microvolt, and coefficient root-mean-square is defined as mode 7 between [0.1,0.5].
EEG signals is handled through multiresolution analysis, utilizes the h of two yardstick equations
kAnd g
kSignal decomposition is become low frequency and two parts of high frequency, continue also can do further decomposition decomposing the back signal.Because it has better temporal resolution, obtains the characteristic information of each frequency range, in conjunction with the wavelet coefficient after decomposing, select the type of characteristic information, further form control command.Under the situation of equipment sample frequency 200Hz, signal is done 3～6 times decompose, can obtain [6.25Hz, 12.5Hz] between signal, if this frequency band signals amplitude is in [20,50] microvolt scope, and the root-mean-square of relative wavelet coefficient is defined as pattern 6 between [0.1,0.5], and for the signal between [18.75Hz, 25Hz], if its amplitude is [20,30] between microvolt, and coefficient root-mean-square is defined as the mode 7 situation between [0.1,0.5].
Wherein the selection of wireless communication module can be wireless communication line or the synthetic ASK mode of signal amplitude that adopts the FSK mode, can also adopt modes such as bluetooth communication.Wherein the FSK mode can be used Industrial Scientific Medical (ISM) frequency range of 433MHz, uses this frequency range and need not special licence.In use, different according to mechanical hand complexity and manner of execution, corresponding the control model that three kinds of analytical methods obtain after to electroencephalogramsignal signal analyzing with the action command of mechanical hand, be delivered to the mechanical hand control unit by wireless communication module, the motion of control mechanical hand.
Before the characteristic information of EEG signals sends, carry out noise jamming and judge, detect near the component of EEG signals 50Hz Frequency point, will detect the EEG signals oscillation amplitude, carry out characteristic information and send smaller or equal to 200 microvolts.
Process at brain electricity analytical also will be carried out noise processed, it mainly is to eliminate power frequency common in the brain electro-detection to disturb and the myoelectricity artefact that noise is differentiated, by the spectrum analysis instrument, detect near the component of 50Hz Frequency point, with reference to the tupe of analog circuit, judge whether it exceeds given range to power frequency component.In time domain, as detect overflow after EEG signals is amplified (greater than 500 microvolts) or significantly vibration (amplitude is greater than 200 microvolts) etc. do not meet the signal of EEG signals basic feature, can be judged as the interference that limb motion etc. causes.
Signal collecting device of the present invention is an independently difference mode signal pre-process circuit of a four-way, each passage comprises Ag-AgCl electrode, amplifying circuit, filter circuit, photoelectric isolating circuit, the Ag-AgCl electrode is connected to amplifying circuit, imports photoelectric isolating circuit behind the wave circuit after filtration again.Its end links to each other with the Ag-AgCl electrode that places scalp by shielded cable, and another side is connected with serial port by USB interface with portable computer.
Beneficial effect of the present invention: this system adopts multichannel eeg data obtain manner, modular configuration, wireless connections mode flexibly, safe and reliable design can effectively help the people with disability to control mechanical prosthetic hand, and can control other equipment by expansion interface, such as light switch, wheelchair moves or the like, improves people with disability's living environment, increase its self-care ability, improve the quality of its life.Wireless mode can better address this problem, and makes things convenient for user to wear this equipment.Further,, can on electric, keep apart mechanical hand control unit and brain wave acquisition analytic unit completely, eliminate the two and influence each other by the controlled in wireless mode.As seen, the introducing of wireless communication mode can well address the above problem.To this, the present invention is based on the modulation system of FSK, adopt efficient forward error correction channel technology, improved the ability of anti-bursty interference of data and random disturbances, its transmitting power 20mW, carrier frequency 433MHz.
Description of drawings
Fig. 1 human brain-mechanical hand wireless control system block diagram;
Fig. 2 pretreatment analog circuit block diagram;
Fig. 3 (a) pretreatment amplifying circuit;
Fig. 3 (b) pretreatment filter circuit figure;
Fig. 3 (c) pretreatment photoelectric isolating circuit figure;
Fig. 3 (d) pretreatment wireless communication line;
Fig. 4 (a) sampling parameter management process;
Fig. 4 (b) data source is chosen flow process;
Fig. 4 (c) EEG feature extraction and analysis process;
Fig. 4 (d) control model flow chart.
The specific embodiment
In conjunction with the accompanying drawings the present invention is described further:
As shown in Figure 1, human brain-mechanical hand wireless control system block diagram, the signal analyse block and the wireless transport module that comprise signal collecting device, finish by computer, wherein signal collecting device is gathered EEG signals, EEG signals enters computer, finishes analytical judgment to signal, the analysis of module by analysis by signal analyse block then, the characteristic information of different brain electricity is formed different command modes, send to mechanical hand by wireless communication module then.
Native system adopts box-packed mode, and the control box front panel has on and off switch, power supply indication, four passage input terminals and a common-mode signal feedback terminal.Rear board has serial line interface, USB interface, wireless control switch and an expansion interface.There are the pretreatment analog circuit of four passages, wireless transmission circuit, analog-to-digital conversion module etc. in control box inside.Pre-process circuit can be divided into amplifying circuit, three parts of filter circuit and photoelectric isolating circuit, and block diagram is seen accompanying drawing 2.The each several part circuit theory see accompanying drawing 3 (a, b, c).Wireless transmission is to adopt lower powered FSK modulation circuit to realize, can realize the operating characteristic of low-power consumption by its mode of operation of reasonable disposition, and circuit theory is seen accompanying drawing 3 (d).In use, the each processing unit among Fig. 3 can be as required, by the jumper annexation of optimizing hardware.Such as disturbing under the very little situation, can simplify multi-stage filter circuit in the accompanying drawing 3 (b).
The realization of under the LabVIEW framework, programming of human brain-mechanical hand control software, can be divided into modules such as sampling parameter setting, data source selection and management, EEG feature extraction, control model configuration and mechanical hand communication mode according to its function, the flow process of each module is seen accompanying drawing 4.Based on above module, management and control have been realized to hardware system.Select and administration module by data source, this system not only can gather in real time and analyze EEG, but also can do the off-line data analysis, has improved the utilization rate of software greatly.The EEG feature extraction module can be selected different analysis modes for use, as wavelet transformation mode and power spectrum mode etc., after having extracted brain electrical feature signal, compares with feature samples, if meet given feature, promptly selects the control mode of current sample for use.
EEG signals is the extremely faint signal of telecommunication of a kind of human body, its amplitude at several microvolts between tens microvolts.Place the Ag-AgCl electrode in scalp surface, closely contact, can pick up EEG signals with scalp surface.The front end input terminal of signals collecting control appliance is connected by shielded conductor with the Ag-AgCl electrode, faint EEG signals is delivered in the acquisition controlling equipment handles.On the other hand,, reduce distorted signals, the EEG signals common-mode voltage is fed back to human body through the common mode terminal of front panel, effectively improve the effect of signal processing in order effectively to suppress of the influence of human body common-mode signal to signal processing.Pretreatment front-end amplifier in the acquisition controlling equipment directly is connected with corresponding input terminal.By amplifying circuit to signal is preliminary amplify after, EEG signals is delivered to second level filter circuit.Because the influence of many factors such as environment is being mingled with power frequency component by a relatively large margin usually in the EEG signals, can interference band effectively be weakened by filter circuit, to reduce the influence of back level amplification to signal.Signal after the processing to guarantee the electrical safety characteristic of equipment, is imported analog-to-digital conversion module via photoelectric isolating circuit again.The parameter of Acquisition Circuit is shown in subordinate list 1.
Table 1 control system parameter
| Parameter | Numerical value | Unit | Remarks |
| Input impedance | ?10 10 | Ω | The prime chip |
| Common mode rejection ratio | ?120 | dB | The prime chip |
| Preceding stage gain | ?20 | dB | |
| Filter range | ?0.1-30 | Hz | Optional |
| Two stage gains | ?46+20 | dB | Adjustable |
| Bandreject filtering | ?50 | Hz | Optional |
| The photoelectricity isolation parameters | ?10 13?5 | Ω ％ | The isolation resistance gain error |
| Sample frequency | ?200-1000 | Hz | Adjustable |
| Wireless telecommunications speed | ?2400-9600 | bps | Adjustable |
Human brain-do evil through another person control software is gathered EEG signals after the pretreatment by the USB mouth, enters the software processes flow process.The operation interface of control software mainly comprises the demonstration of EEG waveform, frequency spectrum and zones such as feature demonstration, parameter setting and control as shown in Figure 5.Under the data analysis pattern, at first spectrum analysis is done in measured signal, the graphical demonstration.According to the frequency domain character that obtains, signal is carried out Filtering Processing, remove noise.According to the configuration difference and the actual interference situation of hardware filtering, filtering parameter is carried out choose reasonable.Then,, do fast Fourier analysis, WAVELET PACKET DECOMPOSITION, power spectral density calculating etc., obtain the characteristic parameter of EEG signals, compare, choose the control mode of suitable characteristics value correspondence with eigenvalue at different analytical methods.By wireless terminal, send the control instruction of human thinking.Outside above analytic process, other has a successive NAP noise analysis program, detects parameters such as artefact and power frequency interference, if exceed given range, then cancels current control command.By above software analysis process, this control system can obtain the thinking control command accurately, and can suppress the disturbance factor that operator and environment bring, and reaches mechanical hand is effectively controlled.
The control mechanical hand grasps example
At first start soft of machinery, put measurement electrode and brain top, connect control box and scalp electrode, and control box and computer USB connecting line, use human brain-manipulator control system in the example, control a five-finger type apery mechanical hand, this mechanical hand is by five finger motions of one 8051 chip controls mechanical hand, can finish different actions, adopt the mode of wireless telecommunications between mechanical hand and the control appliance, receive next instruction to control system by wireless terminal.Can be corresponding with the concrete action of mechanical hand enforcement in different application the different command pattern that brain electricity analytical is obtained.In the present embodiment, EEG Processing is selected power spectrum analysis method for use, and Frequency point detects and to be respectively 5Hz, 10Hz and 22Hz, and this method pattern 1 of issuing orders, pattern 2 respectively with mechanical hand open and extracting is associated.The subjects at first produces the EEG signals that mechanical hand is opened in control with thinking, and mechanical hand opens, and puts mechanical hand and mineral water bottle then behind correct position, and the thinking of subjects's reuse produces the EEG signals that control mechanical hand work grasps, and mechanical hand has grasped mineral water bottle.The subjects has well finished the action that mechanical hand grasps mineral water bottle with the EEG signals that thinking produces, and meets practical needs.
Claims (5)
1. human brain-mechanical hand interface system under the micro-power wireless communication mode, the signal analyse block and the wireless communication module that it is characterized in that comprising signal collecting device, finish by computer, wherein signal collecting device is gathered EEG signals, EEG signals enters computer, signal analyse block adopts power spectral-density analysis, detect EEG signals A second every t, B, three Frequency point power spectral densities of C, A, B, C three dot frequencies are at 2～30HZ, when the power spectral density of Frequency point between the 2-50 microvolt, getting this Frequency point characteristic information is 1, otherwise is 0; EEG signals forms the characteristic information of being made up of three binary numerals, and characteristic information sends to mechanical hand by wireless communication module.
2. according to human brain-mechanical hand interface system under the described micro-power wireless communication mode of claim 1, it is characterized in that detecting EEG signals 5Hz, 10Hz and 22Hz Frequency point power spectral density every 0.2～1 second.
3. according to human brain-mechanical hand interface system under the described micro-power wireless communication mode of claim 1, it is characterized in that characteristic information 010,101 is pattern 1, characteristic information 010,000 is a pattern 2, and characteristic information 110,010,000 is a mode 3.
4. according to human brain-mechanical hand interface system under the described micro-power wireless communication mode of claim 1, it is characterized in that described signal analyse block adopts the threshold decision analysis, process is as follows: choose amplitude range 0-100 microvolt, the EEG signals of average-30-30 microvolt, utilize the spectrum analysis instrument the α ripple then, the amplitude of β ripple frequency band correspondence and root-mean-square information compare with preset threshold respectively, wherein α frequency band mean square deviation exceeds between threshold amplitude 5-50 microvolt, be defined as pattern 4, the mean square deviation of β frequency band exceeded threshold range 10-30 microvolt be defined as pattern 5.
5. according to human brain-mechanical hand interface system under the described micro-power wireless communication mode of claim 1, it is characterized in that described signal analyse block adopts wavelet transformation analysis, process is as follows: EEG signals is resolved into time and frequency signal independently, and two yardstick equations of wavelet analysis are as follows:
In the formula
Be scaling function, Ψ is a wavelet function, h
kBe one group of low-pass filtering coefficient, g
kBe the high-pass filtering coefficient, x and k are the ordinal number of data point in sequence;
EEG signals is handled through multiresolution analysis, utilizes the h of two yardstick equations
kAnd g
kSignal decomposition is become low frequency and two parts of high frequency, if 6.25Hz, 12.5Hz the frequency band signals amplitude is in 20-50 microvolt scope, and the root-mean-square of relative wavelet coefficient is between 0.1-0.5, be defined as pattern 6, for the EEG signals between the 18.75Hz-25Hz, if its amplitude is between the 20-30 microvolt, and coefficient root-mean-square is defined as mode 7 between 0.1-0.5.
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| CN102198660A (en) * | 2011-05-04 | 2011-09-28 | 上海海事大学 | Robotic arm control system and action command control scheme based on brain-computer interface |
| CN105528811A (en) * | 2015-12-30 | 2016-04-27 | 广州快速交通建设有限公司 | Multifunctional lane toll control system |
| CN108594993B (en) * | 2018-03-30 | 2021-08-06 | Oppo广东移动通信有限公司 | Electronic device, audio playing method and related product |
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