CN106959753A - Unmanned plane dummy control method and system based on Mental imagery brain-computer interface - Google Patents
Unmanned plane dummy control method and system based on Mental imagery brain-computer interface Download PDFInfo
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Abstract
A kind of unmanned plane dummy control method and system based on Mental imagery brain-computer interface, control method include：1) experiment starts, and subject watches virtual unmanned plane interface, imagines different limb motions, gathers EEG signals；2) time filtering, space filtering, feature extraction and feature conversion are sequentially passed through EEG signals is converted into control signal；3) brain electric control signal is delivered to virtual unmanned aerial vehicle (UAV) control program, pass through the virtual virtual unmanned plane during flying of unmanned aerial vehicle (UAV) control programme-control, the unmanned plane during flying state of feedback is presented by virtual unmanned plane interface, subject monitors the control effect of oneself in real time, Mental imagery state is adjusted, until successfully completing virtual unmanned plane during flying task or triggering fail condition.Control system includes virtual unmanned plane interface, brain wave acquisition equipment, signal processing module and interface.The present invention can train subject to control virtual unmanned plane, to control true unmanned plane to prepare.
Description
Technical field
The present invention relates to field of brain-computer interfaces, and in particular to a kind of unmanned plane based on Mental imagery brain-computer interface is virtual
Control method and system, meet the training demand that unmanned plane flies control hand brain electric control unmanned plane.
Background technology
First brain-computer interface (brain computer interface, BCI) international conference defines brain-computer interface
For a kind of brain-machine communication system independent of brain normal neuronal and muscle output channel.Many studies have shown that, the specific imagination
Task can produce specific EEG signals, such as imagine limbs lateral movement, can cause brain contralateral regions EEG signals
Some compositions synchronously weaken or strengthen, this imagination is referred to as that time correlation is synchronous and time correlation is desynchronized.Based on this
Principle, Mental imagery turns into the important normal form that brain-computer interface is controlled.With the development of brain wave acquisition and treatment technology, BCI systems
Function is greatly enhanced, and its application is also more and more extensive.BCI technologies are used for unmanned aerial vehicle (UAV) control field, will improve unmanned plane
Control performance.But control unmanned plane is not easy to, each unmanned plane flies control hand and is required for substantial amounts of remote control distributor to practise, and right
For the unmanned plane that rigid connection touches brain electricity flies control hand, control difficulty is undoubtedly added using brain electric control unmanned plane.For this
Problem, it is necessary to set up Virtual control platform and be trained to the winged control hand of unmanned plane, and according to the utilization of the winged control hand of evaluation of result
The ability of brain electric control unmanned plane.
The content of the invention
It is an object of the invention to connect for above-mentioned the problems of the prior art there is provided one kind based on Mental imagery brain-machine
The unmanned plane dummy control method and system of mouth, simulate the control strategy (scene) of true unmanned plane, meet the demand of training.
To achieve these goals, the unmanned plane dummy control method of the invention based on Mental imagery brain-computer interface includes
Following steps：
1) experiment starts, and subject watches virtual unmanned plane interface, imagines different limb motions, gathers EEG signals；
2) time filtering, space filtering, feature extraction and feature conversion are sequentially passed through EEG signals is converted into control letter
Number；
3) brain electric control signal is delivered to virtual unmanned aerial vehicle (UAV) control program, virtual by virtual unmanned aerial vehicle (UAV) control programme-control
Unmanned plane during flying, the unmanned plane during flying state of feedback is presented by virtual unmanned plane interface, and subject monitors the control of oneself in real time
Effect processed, adjusts Mental imagery state, until successfully completing virtual unmanned plane during flying task or triggering fail condition.
The modeling at virtual unmanned plane interface is carried out by Blender softwares, by Scan4.5 software collection EEG signals,
EEG signals are converted into by control signal by BCI2000 softwares.
The amplifiers of SynAmps 2 and 64 that electroencephalogramsignal signal collection equipment is developed using Neuroscan lead wet electrode brain electricity
Cap, and the configuration of electrodes of the electric cap of brain arranged according to international 10-20 systems.
The space filtering of the EEG signals utilizes Laplacian space wave filter, is reduced in each time point t, center
The point position of motor subtracts the weighted sum of 4 vectorial electrodes：
Weight w in formulah,iIt is apart from d between target electrode h and its neighbouring electrode ih,iFunction.
The feature extraction of the EEG signals is believed the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things from time domain using autoregression Maximum Entropy Spectrum Method
Frequency domain character number is converted into, the described Mu rhythm and pace of moving things is 8Hz-12Hz, and the Beta rhythm and pace of moving things is 18Hz-25Hz.
The feature conversion of the EEG signals includes Classification and Identification and normalized；Classification and Identification introduces Fisher and differentiated
Criterion expression formula, makes the sample after projection have maximum inter _ class relationship and minimum within-cluster variance；Normalized is led to
Cross and linear transformation is carried out to control signal, the control signal of output is in particular range.
Brain electric control signal is sent in signal transfer software by the connectionless host-host protocols of UDP, signal transfer software
The brain electric control signal of local port is read, is then delivered to by internal memory mapping among Blender virtual environments.
Unmanned plane virtual control system of the invention based on Mental imagery brain-computer interface includes：Virtual unmanned plane interface with
And for gathering the brain wave acquisition equipment of produced EEG signals when subject imagines different limb motions；For by EEG signals
It is converted into the signal processing module of control signal；And for brain electric control signal to be delivered into virtual unmanned plane virtual environment
Interface.
The EEG signals that brain wave acquisition equipment is collected are transmitted to signal processing module by Electroencephalo signal amplifier.
Compared with prior art, unmanned plane dummy control method and system of the invention based on Mental imagery brain-computer interface
With following beneficial effect：Virtual unmanned plane interface is watched by subject, Mental imagery flow is familiar with, starts in experiment
Before, subject works as the imagination it should be understood that assume virtual unmanned plane at the uniform velocity flight forward in nobody virtual control rule, this programme
Left hand is moved, and unmanned plane is to left movement, and imagination right hand motion, unmanned plane is moved right, and imagination both hands are moved simultaneously, unmanned plane to
Upper motion, imagination both legs motion, unmanned plane is moved downward.The EEG signals of generation, brain electricity are gathered using brain wave acquisition equipment
Signal is converted into control signal and is delivered to virtual unmanned aerial vehicle (UAV) control program, by virtual unmanned aerial vehicle (UAV) control programme-control virtually nobody
Machine flies.The present invention can train subject to control virtual unmanned plane, so as to control true unmanned plane to prepare, can be used in
The fields such as electronic entertainment, Industry Control, and the brain-computer interface system that can be improved, are expected to obtain objective social economy
Benefit.
Brief description of the drawings
The structural representation of Fig. 1 brain-computer interfaces of the present invention；
The software connection diagram of Fig. 2 brain-computer interfaces of the present invention；
The signal processing flow and control mode schematic diagram of Fig. 3 brain-computer interfaces of the present invention；
The virtual unmanned plane interface schematic diagram of Fig. 4 present invention.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
Referring to Fig. 1, the unmanned plane virtual control system of the invention based on Mental imagery brain-computer interface includes virtual unmanned plane
Interface and the brain wave acquisition equipment for gathering produced EEG signals when subject imagines different limb motions；For by brain
Electric signal is converted into the signal processing module of control signal；And it is virtual for brain electric control signal to be delivered into virtual unmanned plane
The interface of environment.In view of the complexity of unmanned aerial vehicle (UAV) control, subject needs to carry out necessarily one-dimensional before the inventive method is carried out
Mental imagery is trained, and is familiar with Mental imagery flow.Before experiment starts, subject should be appreciated that nobody virtual control method (rule
Then)：Assuming that virtual unmanned plane at the uniform velocity flight forward, when imagination left hand motion, unmanned plane is to left movement；Imagine right hand motion, nothing
It is man-machine to move right；Imagination both hands are moved simultaneously, and unmanned plane is moved upwards；Imagine both legs motion, unmanned plane is moved downward.
Referring to Fig. 2, brain wave acquisition software of the present invention is Scan4.5, and brain electric treatment software is BCI2000, virtual unmanned plane
Interface (equivalent to simple game) modeling software is Blender.The virtual unmanned plane model wherein set up be four rotors without
It is man-machine, and the game engine carried using Blender writes control program.Virtual unmanned plane at the uniform velocity flies forward when experiment is carried out
OK, and according to control signal turn to left and right, rise or decline.Brain wave acquisition equipment is used to be developed by Neuroscan
The amplifiers of SynAmps 2,64 lead the electric cap of wet electrode brain, and the configuration of electrodes of brain electricity cap is carried out according to international 10-20 systems
Arrangement.When testing beginning, subject watches virtual unmanned plane interface, imagines different limb motions.Put by EEG signals
The big electric cap of device and brain collects EEG signals, and Scan4.5 reads EEG signals, given signal transmission by ICP/IP protocol
BCI2000 softwares, BCI2000 softwares carry out a series of processing to signal.
1) BCI2000 receives all EEG signals for passing over of Scan4.5, but we are actual only focuses on and Mental imagery
13 related electrodes C3, C4, C5, CZ etc. signal.Therefore the signal for this 13 passages of signal transacting is carried out.Additionally
The attribute for needing concern is data block (sample block) size and sample frequency (sample Rate).In BCI2000 softwares
Data and non real-time transmission between each module in portion, but transmitted according to data block (sample Block) size.
2) space filtering is carried out, the purpose of space filtering is to reduce the influence of ambiguity of space angle, improve the fidelity of signal
Degree.The present invention utilizes Laplacian space wave filter, and the algorithm after simplifying is that the point position of central motor subtracts in each time point t
Go the weighted sum of 4 vectorial electrodes：
Weight w in formulah,iIt is apart from d between target electrode h and its neighbouring electrode ih,iFunction.In this experiment, use
Simpler implementation, i.e., subtract the average value of four nearest electrodes with centre potential.
3) and then feature extraction, theoretical research shows that Mental imagery can be Mu (8Hz-12Hz) or the Beta rhythm and pace of moving things (18Hz-
25Hz) frequency band produces change.The general purpose of feature extraction is exactly that the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things are converted into frequency from time-domain signal
Characteristic of field.BCI requires that system can provide repeatedly feedback at one second, and the maximum entropy method (MEM) MEM based on autoregression model exists here
Given frequency resolution is than FFT and wavelet transformation has preferably temporal resolution, is more suitable for BCI systems, therefore use here
Be autoregression maximum entropy spectrum carry out feature extraction.The method of maximum entropy extrapolation auto-correlation function is of equal value with auto-regressive analysis method
's.Under
Introduce the use of auto-regressive analysis method in face：
By time series analysis, the steady random restricting the number sequence { x that average is zerokIts N rank autoregressive signal model [AR
(N)] it is：
In formula, wnFor with zero-mean and unit varianceWhite noise, G be its gain coefficient, ak(k=1,
2 ..., N) it is autoregressive coefficient,For xnOne-step prediction value.It is one in AR Power estimations to select suitable AR model orders
Major issue, order selection is too low to produce relatively large deviation, and too Gao Zehui causes false spectral peak, and causes Power estimation variance performance
Decline, according to EEG Processing experience, preference pattern order is 16.
Therefore GwnEquivalent to predicated error, regression coefficient can be tried to achieve with the Minimum Mean Square Error method of error, even
To akDerivative be 0, Yule-Walker equations can be obtained：
In formula, r (0), r (1) ..., r (N) is the auto-correlation function of signal, coefficient ak(k=1,2 ..., N) can be by solving
This equation is drawn.From precedingThis equation is write as to the form of transform, obtainedTherefore transmission function is
Because H (z) rational expression is full limit, therefore this signal model is turned into full pole type.Therefore autoregression
Signal model is full pole type.Its power spectrum is：
In formula, also G2It is unknown, derived now.
Current auto-regressive analysis method and maximum entropy spectrum analysis all apply with predicated error filtering method, this method is straight
Connect from gained signal sequence data, estimate a minimum fragrance prediction error filter, it is output as whitening sequence.
Predicated error is：
Its transmission function is
Because prediction error filter is output as whitening sequence, therefore E { enxn-k}=0 (k ＞ 0).
Its power output, which can be obtained, is：
R (0), r (1) ..., r (N) are the auto-correlation function of signal, coefficient ak(k=1,2 ..., N) by solving Yule-
Walker equation equations are drawn.
The frequency characteristic of prediction error filter：
Its power spectrum is S (w) | A (w) |2=P；
I.e.
If prediction five or filter input signal are identical with autoregressive signal power spectrum, G2=P；
This addresses the problem G2The problem of.Thus the power spectrum of signal can be calculated.
4) and then it is accomplished by carrying out Classification and Identification to the feature of extraction after feature extraction, its main process is a series of
EEG signals feature be converted into a series of control signal.Traditional classification/homing method can realize this conversion.
Linear discriminent analysis (Linear Discriminant Analysis, LDA) is described below.
The basic thought of this method is that the pattern sample of higher-dimension is projected into best discriminant technique vector space, to reach extraction
Assured Mode sample has the class spacing of maximum in new subspace after the effect of classification information and compressive features space dimensionality, projection
From the inter- object distance with minimum, i.e. pattern has optimal separability within this space.
Comprise the following steps that：One N*1 column vector during data after feature extraction, it is assumed that obtained m sample,
And sample belongs to C classification altogether.Class i sample average is calculated first；
Similarly obtain population sample average；
Define inter _ class relationship matrix and within class scatter matrix：
LDA is as the algorithm of a classification, and we are it is of course desirable that the degree of coupling is low between the class that it divides, the degree of polymerization in class
Numerical value in height, i.e. within class scatter matrix is small, and the numerical value in inter _ class relationship matrix is big, the effect of such classification
Fruit is better.Here we introduce Fisher discriminating criterion expression formulas：
Wherein,Column vector is tieed up for any n.
Fisher linear discriminant analysis is exactly to choose to causeReach the vector of maximumAs projecting direction,
Its physical significance is exactly that the sample after projection has maximum inter _ class relationship and minimum within-cluster variance.It is final by deriving
Obtain, meet Fisher conditionsColumn vector is, matrix Sw -1SbCharacteristic vector corresponding to maximum characteristic value.
5) output signal range after feature conversion is not known, for the stability of control, in addition it is also necessary to which output signal is entered
Row normalized, normalization carries out linear transformation to control signal, makes the control signal of output in particular range.
Following conversion is used during BCI2000 on-line operations：Usage data buffer area preserves the input letter before normalization in BCI2000
Number, for each passage, normalization is exactly that channel data is subtracted into an offset and a yield value is multiplied by.Offset as data
Average value, gain then for data standard deviation it is inverse.Assuming that i-th of passage has buffered N number of data：
outputi=(inputi-NormalizerOffseti)×NormalizerGaini；
The output signal finally given has zero-mean and unit variance.
6) EEG Processing part is so far completed.Next control signal is output in unmanned plane virtual environment.
On-line Control requires that the design of external application interface should be succinct efficient as far as possible first, therefore uses UDP
Connectionless host-host protocol, the signal transmission after brain electric treatment is come out.Here being by the way of will by udp protocol
EEG signals after BCI2000 processing are sent in signal transfer software, and signal transfer software easily reads local port
Control signal, is then delivered among Blender virtual environments, as shown in Figure 2 by internal memory mapping.
Unmanned plane model in Blender is flown according to set strategy, as shown in Figure 3, Figure 4.
Claims (9)
1. a kind of unmanned plane dummy control method based on Mental imagery brain-computer interface, it is characterised in that comprise the following steps：
1) experiment starts, and subject watches virtual unmanned plane interface, imagines different limb motions, gathers EEG signals；
2) time filtering, space filtering, feature extraction and feature conversion are sequentially passed through EEG signals is converted into control signal；
3) brain electric control signal is delivered to virtual unmanned aerial vehicle (UAV) control program, by virtual unmanned aerial vehicle (UAV) control programme-control virtually nobody
Machine flies；The unmanned plane during flying state fed back according to virtual unmanned plane interface, subject monitors the control effect of oneself in real time, adjusts
Whole Mental imagery state, until successfully completing virtual unmanned plane during flying task or triggering fail condition.
2. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
The modeling at virtual unmanned plane interface is carried out by Blender softwares, by Scan4.5 software collection EEG signals, is passed through
EEG signals are converted into control signal by BCI2000 softwares.
3. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
The amplifiers of SynAmps 2 and 64 that electroencephalogramsignal signal collection equipment is developed using Neuroscan lead the electric cap of wet electrode brain, and brain electricity
The configuration of electrodes of cap is arranged according to international 10-20 systems.
4. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
The space filtering of the EEG signals utilizes Laplacian space wave filter, is reduced in each time point t, the point of central motor
Position subtracts the weighted sum of 4 vectorial electrodes：
Weight w in formulah,iIt is apart from d between target electrode h and its neighbouring electrode ih,iFunction.
5. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
The Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things are converted into by the feature extraction of the EEG signals using autoregression Maximum Entropy Spectrum Method from time-domain signal
Frequency domain character, the described Mu rhythm and pace of moving things is 8Hz-12Hz, and the Beta rhythm and pace of moving things is 18Hz-25Hz.
6. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
The feature conversion of the EEG signals includes Classification and Identification and normalized；Classification and Identification introduces Fisher and differentiates criterion expression
Formula, makes the sample after projection have maximum inter _ class relationship and minimum within-cluster variance；Normalized passes through to control
Signal carries out linear transformation, the control signal of output is in particular range.
7. the unmanned plane dummy control method based on Mental imagery brain-computer interface according to claim 1, it is characterised in that：
Brain electric control signal is sent in signal transfer software by the connectionless host-host protocols of UDP, and signal transfer software reads local
The brain electric control signal of port, is then delivered among Blender virtual environments by internal memory mapping.
8. it is a kind of realize the unmanned plane dummy control method based on Mental imagery brain-computer interface as claimed in claim 1 nobody
Machine virtual control system, it is characterised in that including：Virtual unmanned plane interface and imagine different limbs fortune for gathering subject
The brain wave acquisition equipment of produced EEG signals when dynamic；Signal processing module for EEG signals to be converted into control signal；
And for brain electric control signal to be delivered to the interface of virtual unmanned plane virtual environment.
9. unmanned plane virtual control system according to claim 8, it is characterised in that：The brain that brain wave acquisition equipment is collected
Electric signal is transmitted to signal processing module by Electroencephalo signal amplifier.
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| CN107544675A (en) * | 2017-09-08 | 2018-01-05 | 天津大学 | Brain control formula virtual reality method |
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