CN103019382A - Brain-computer interaction method for reflecting subjective motive signals of brain through induced potentials - Google Patents
Brain-computer interaction method for reflecting subjective motive signals of brain through induced potentials Download PDFInfo
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Abstract
The invention provides a brain-computer interaction method for reflecting brain signals of the brain on the basis of P2 and VPPs (vertex positive potentials). The method mainly comprises the following steps that the visual stimulation is displayed through a picture stimulator; brain-computer signals induced by a visual event are collected and processed; P2 and VPP combined signals are analyzed to obtain brain-computer interaction control signals; and signals are output through a signal output device. When the brain-computer interaction method for reflecting brain signals of the brain on the basis of P2 and VPPs provided by the invention is utilized, the target brain-computer component range of the brain-computer interaction is expanded, the signal judgment accuracy is improved, effective signals can be obtained without the basis on the action of any peripheral system of users, no injury is caused on the users, and the brain-computer interaction method can be used for a support auxiliary system of disabled people.
Description
Technical field
The invention belongs to the brain-computer interface field, be specifically related to a kind of take event related potential as gordian technique the brain electricity analytical method of reflection brain brain signal, comprise and utilize pixel randomization blurred picture as stimulus material and event related potential VPP and the P2 composition correlation technique as key signal.
Background technology
Brain-computer interface (Brain Computer Interaction, BCI) is that a kind of peripheric movement nerve or muscle of not relying on participates in, and only relies on cerebral cortex to external transport instruction, thereby sets up the technology of human brain and extraneous communication channel.Core technology wherein is the deriving means collection EEG signals (Electroencephalography by non-intrusion type, EEG), utilize pattern and the information hidden in Computer Processing and the identification signal, be converted into the output of computing machine, finish the direct control to external unit.
Event related potential (Event Related Potentials, ERPs) refer to the stimulation event relevant, and in time with viewed a series of potential change after the EEG signal averaging that stimulates locking.It not only depends on the physical attribute of environmental stimuli, and also subjectivity processing and the cognitive state with brain has close relationship.In all kinds of event related potential compositions, P300 is a kind of widely used composition.It refers to the larger positive wave that occurs in 300 milliseconds of left and right sides brain electricity after stimulation presents.The patent relevant with P300 has " based on visual P 300-Speller brain-computer interface method " (201110325897.1) at present " and " based on the input in Chinese BCI system of P300 brain electric potential " (200710164418.6).The content of above-mentioned patent is introduced in this instructions, for reference in full.
Traditional BCI system based on P300 mainly uses 6 * 6 designed stimulation interface of Farwell and Donchin, presents a letter or number in each unit lattice, by random row flicker or row flicker, makes the experimenter constantly accept visual stimulus.At this moment, when row or column glimmers alphabetical that the user thinks in the heart, small probability event namely occurs, will occur the larger P300 of wave amplitude in the ERP signal.By feature extraction and sorting algorithm, obtain the row and column that target stimulation occurs, just can calculate the target letter or number that the user thinks in the heart.Then, can by the successively combination of letter and number, reflect that the experimenter wants the heart activity of expressing again.
Except P300, also have at present another kind of people's face specific recognition ripple N170 also to can be used as the signal index of brain-computer interface." based on the brain machine interface system of human face recognition specific wave N 170 component " (application number: 200810147312.X, publication number: in patented invention CN 101339455A), N170 can reflect the variation of brain signal more significantly as people's face is stimulated responsive specific component.Can overcome based on the brain machine interface system of N170 other type brain machine interface system ubiquitous weakness: catch in the process of very faint EEG signals, namely to guarantee accuracy and transmission speed, guarantee that again real-time control has very large difficulty.Yet but there is a very large defective in this system, namely can only as the operation control of picture classification, can not reflect subjective intention how abundant in the brain and motivation.This defective is relevant with the attribute of N170 itself: N170 mainly depends on the processing from bottom to top that the physical attribute of stimulation causes, but not top-down Cognitive Processing and instruction that the brain signal causes.Equally, the content of above-mentioned patent is introduced in this instructions, for reference in full.
Summary of the invention
In view of foregoing, the inventor is through studying intensively with great concentration event related potential, utilizes the specificity of people's face processing and brain to the top-down modulation of this process, reflects the brain signal of brain, and is not limited to the classification of people's face picture.Wherein, the brain electricity composition relevant with the processing of people's face that can be modulated from top to bottom is P2 and top positive potential.Wherein, P2 refers to occur 200 milliseconds (wherein in visual stimulus, " P " represents Positive, 200 milliseconds of " 2 " expressions) positive wave that occurs in the hindbrain electricity, top positive potential (Vertex Positive Potential, VPP) refers to about the 170 milliseconds positive waves that occur at brain central authorities top after visual stimulus occurs.
The purpose of this invention is to provide the brain-machine interface method of a kind of P2 of utilization and VPP reflection brain brain signal, and can reach reliable judgment accuracy.The method can be expanded the class scope of employed event related potential composition in the brain machine interface system, so that target brain electricity composition is not limited to P300 and N170.
Utilize the brain-machine interface method based on P2 and VPP reflection brain brain signal provided by the invention, have the following advantages: (1) utilizes VPP and P2 can be subject to the characteristic that the brain motivation is regulated from top to bottom, use first VPP and P2 as the crucial brain electricity composition of brain-computer interface technology, expanded the scope of brain-computer interface target brain electricity composition; (2) VPP and P2 lay respectively at quader just in and the temporal lobe both sides, adopt the brain electricity composition allied signal of two Different brain regions as judging criteria for classification, improve the signal Accuracy of Judgement; (3) the present invention need to not comprise the movement of blinkpunkt based on the action of any peripheral-system of user, only need to carry out people's face or the non-face imagination in brain, can obtain useful signal.And this point is not accomplished by other system: based on the system of SSVEP (referring to: based on the stable state vision inducting brain-machine interface method of two frequency stimulation of left and right view field, application number: 200910076209, publication number: CN101477405A.The document is introduced in this instructions, for reference in full.) need to obtain by the movement of blinkpunkt the vision homeostatic reaction under the different frequency, perhaps need to pay close attention to by the movement of blinkpunkt the variation of different row and columns based on P300; (4) the outer eeg recording of cranium is non-intrusive mood recording method, to not infringement of user, can be used for individuals with disabilities's support backup system.
According to an aspect of the present invention, provide a kind of brain-machine interface method based on P2 and top positive potential reflection brain signal, comprising: step (1): play the visual stimulus signal; Step (2): gather the EEG signals of being brought out by described visual stimulus signal; Step (3): the described EEG signals that collects is processed, obtained described P2 and top positive potential waveform; Step (4): analyze according to the described P2 that obtains and top positive potential waveform signal, obtain the brain-computer interface control signal; Step (5): control output unit to produce output signal by described brain machine control signal.
According to an aspect of the present invention, in step (1), adopt the picture stimulator that clear picture is converted into blurred picture, thereby produce described visual stimulus signal.
According to an aspect of the present invention, in step (2), also comprise: with the target electrode Cz of top and temporo occipital lobe position, FCz, P5/6, P7/8, PO5/6, the EEG signals that the PO7/8 place obtains is amplified, is carried out digital-to-analog conversion through amplifier, is stored in the computing machine in digital form.
According to an aspect of the present invention, in step (3), EEG signals is processed, also comprised pre-service: go at least a of the processing such as eye electricity, Baseline wander, linear rectification and filtering.
According to an aspect of the present invention, in step (4), specifically comprise:
Extract wave amplitude and the latent period of P2 and top positive potential, detect the significant difference that people's face brings out the ERP composition that brings out with non-face object;
The allied signal of P2 and top positive potential is carried out classification and Detection in characteristic frequency section and the special time window;
Classification and Detection result according to P2 and top positive potential judges, obtains the brain-computer interface control signal;
According to an aspect of the present invention, also comprise in the step (5): carry out described output signal visual.
Description of drawings
Fig. 1 is that system of the present invention consists of synoptic diagram;
Fig. 2 is that the blurred picture that the present invention uses stimulates exemplary plot;
Fig. 3 is that put forward the methods realizes leading the selection synoptic diagram according to the present invention;
Fig. 4 is the different wave shape of VPP and P2 under the imagination people's face and non-face condition figure as a result;
Fig. 5 is P2 and VPP signal classification and Detection synoptic diagram.
Embodiment
Referring to accompanying drawing of the present invention, by specific embodiment technical scheme of the present invention is further described.
Be understood that detection method provided by the invention can have at different examples various variations, based on neither the departing from the scope of the present invention of example of the various variations of inventive concept; And the accompanying drawing among the present invention is as illustrative purposes in itself, and for example drawing element might not be to draw in proportion the specific equipment that also do not limit, and describes particular order or behavior and does not also require in the such specificity of order.Be that accompanying drawing among the present invention only is the method and system of the present invention of explaining, but not in order to limit the present invention.
According to one embodiment of present invention, provide a kind of brain-machine interface method based on P2 and VPP reflection brain brain signal, comprising:
Play visual stimulus by the picture stimulator;
Gather the EEG signals that visual event brings out and also process, with the electrode arrangement of eeg signal acquisition system in the target location, i.e. Cz, FCz, P5/6, P7/8, PO5/6, PO7/8.Reference electrode places nose, ground-electrode ground connection.Gather the EEG signals that visual event brings out; The EEG signals that each electrode obtains is amplified, carried out digital-to-analog conversion through amplifier, be stored in digital form in the computing machine; EEG signals is carried out Treatment Analysis, and pre-treatment step comprises eye electricity, Baseline wander, the linear rectification and filtering; The core technology of utilizing event related potential is the superposed average principle, obtains P2 and VPP;
Allied signal analysis to P2 and VPP obtains the brain-computer interface control signal.Extract wave amplitude and the latent period of P2 and VPP, detect the significant difference that people's face brings out the ERP composition that brings out with non-face object; According to the allied signal of P2 and VPP, utilize sorting algorithm that user's motivation is carried out the judgement of "Yes" or "No", obtain the brain-computer interface control signal;
By the signal output apparatus output signal.
As shown in Figure 1, system of the present invention comprises picture stimulator, signal pickup assembly, signal processing apparatus, signal output apparatus.
The picture stimulator
Carry out the more lively imagination in order to allow the user that the picture of playing is stimulated, the present invention adopts the randomized blurred picture of pixel as material.Choose GTG people face and house picture from existing picture library, people's face and house picture are carried out the pixel randomization, soon all pixels are chosen and are upset at random and form a new picture.A among Fig. 2 partly is original people's face and house picture, and the b among Fig. 2 partly is by the used blurred picture of the experiment that obtains after the pixel randomization.Adopt refreshing frequency 60Hz in this example, resolution is the display of 1024 * 768 pixels, and the visual angle that picture stimulates is 10.5 °.
Signals collecting
64 conductive caps (Fig. 3) of selection standard system, material are silver/silver chloride (Ag/AgCl) alloy, and the polarizing voltage of this alloy is minimum, is suitable for gathering faint EEG signals.Wear electrode cap according to brain electricity experimental standard flow process for the user, ground-electrode places the crown, and reference electrode places nose.With reference to mode, nose especially increases the signal intensity of pillow temporo both sides with reference to reflecting better EEG signals compared to average reference and bilateral mastoid process.In addition, in order to ensure the high-quality of signal, also recording level eye electricity and vertical eye electricity are used for carrying out artefact and get rid of.
In this example, the electrode position of collection signal marks part at P5/6, P7/8, PO5/6, PO7/8, Cz, FCz(Fig. 3).Wherein, P5/6, P7/8, PO5/6, PO7/8 are used for gathering the signal of P2 composition, and Cz and FCz are used for gathering the signal of VPP composition.Guarantee in the data acquisition that the scalp resistance at all electrode places is less than 4.3k Ω.The systematic sampling rate is 1000Hz, bandwidth 0.05-40Hz.The signal that obtains amplifies by amplifier, and gain amplifier reaches more than 3000 times.Signal after amplifying is sent in the data acquisition unit, carried out digital-to-analog conversion, A/D converter is chosen 16, and according to embodiments of the invention, last signal is stored in the computing machine with the digital document of the CNT form of Neuroscan system.
EEG Processing
The pre-service of EEG signals is in order to reduce the artefact in the signal, to improve the quality of signal.At first, the signal that baseline wander is surpassed ± 100 μ V is corrected, and gets rid of the serious signal segment of artefact nictation.According to one embodiment of present invention, can adopt following eye electricity to correct concrete grammar:
1) calculates the ratio that each EEG leads the eye electricity that contained eye is electric and the eye electrode is contained, the propagation coefficient By that namely respectively leads.
2) according to propagation coefficient By, from the EEG signal of respectively leading, deduct electro-ocular signal.Wherein, according to one embodiment of present invention, it is following [with reference to Gratton et al. (1983) to propagate the computing method that return coefficient B y, Anew me thod for off-line removal of ocular artifact, Electroencepgalogr Clin Neuropgysiol, 55:468-484]
Wherein, X refers to electric each sampled point and each time with the difference of putting mean value, and Y refers to each sampled point of EEG and each time with the difference of putting mean value, and N is sampling number.
Then, the EEG signal of time domain is converted into the frequency domain sign by Fourier transform, and the frequency range during frequency is characterized more than the 20Hz is made as zero, again it is converted to time domain by inverse Fourier transform and characterizes, by this operation, be the low-pass filtering of 20Hz as cutoff frequency with signal.
Utilize the superposed average technology in the event related potential, with the EEG signal of gained according to stimulating the time point that occurs to carry out the repeatedly superposed average of time lock, thereby obtain event related potential, i.e. ERP waveform.As shown in Figure 4, when the user carried out people's face imagination to blurred picture, it was the imagination in house that resulting VPP and P2 waveform are significantly higher than non-face.Wherein, the object time window that crest appears in the VPP composition is 160-220ms, and the object time window that crest appears in the P2 composition is 200-240ms.
For the energy of verifying VPP and P2 reflects that as echo signal the feasibility of brain signal, inventor have adopted said method to carry out experiment based on VPP and P2 reflection brain signal.Recruit 16 university student experimenters in the experiment, adopted Neuroscan system and Neuroscan amplifier (described Neuroscan system and amplifier belong to the state of the art).The signals collecting parameter is set under the Neuroscan system interface, and EEG signals is after the Neuroscan amplifier amplifies in digital form in storage and the computing machine.Adopt the system off-line operating function to carry out the processing of eeg data.The experimenter accepts the blurred picture that the picture stimulator produces, and the imagination of two kinds of different modes, and namely the imagination is people's face or is envisioned as the house.Test experiment is the result show, only needs the average stack of 30 left and right sides visual stimulus, just can produce the VPP of accurate reflection imagination mode, only needs the average stack of 60 left and right sides visual stimulus, just can produce the P2 signal of accurate reflection imagination mode.At this moment, the difference of 16 experimenters' VPP and P2 amplitude can reach statistical conspicuousness (p＜0.05), illustrates that VPP and P2 can distinguish well the imagination and be the subjective process (such as Fig. 5) in people's face and house.In actual applications, as long as give people's face imagination the implication different with the non-face imagination, for example "Yes" and "No", VPP and P2 signal just can reflect the instruction of " being/no " that the experimenter sends well so.
The sorting algorithm of VPP and P2 allied signal
When the user carried out people's face imagination to blurred picture, resulting VPP and P2 waveform were significantly higher than the imagination in non-face (house).Therefore, utilize respectively through low-pass filtering (the energy frequency range: in the corresponding time window 0.0520Hz) energy of (VPP:160-220 ms, P2:200-240 ms) VPP and P2 as a two dimensional character, with some experimental data as training set.
Support vector machine (SVM, Support Vector Machine) algorithm is adopted in the classification of EEG signals, trains a straight line y=ax+b as classifying face through the svm classifier device, and wherein, x is the energy value of VPP, and y is the energy value (such as Fig. 5) of P2.The energy that just represents the two above classifying face is all very large, and namely when y＞ax+b, the Output rusults characterization value is " 1 ", and it just is judged to "Yes"; Below classifying face, namely when y＜ax+b, the Output rusults characterization value is " 0 ", and we just are judged to "No" with it, so just can realize the signal classification of two classes (" being/no ").
Signal output apparatus
This interface imagines that with difference the motive difference that mode reflects is output as visualization result as signal output part, the decision instruction after VPP and the P2 detection is output as the printed words of "Yes" and "No" at display interfaces.Testing result is " 1 ", shows to have detected strong P2 and VPP signal in the EEG signals, illustrates that the experimenter is carrying out people's face imagination, when this signal is judged as "Yes", and the printed words of display output "Yes"; Testing result is " 0 ", shows to have detected weak P2 and VPP signal in the EEG signals, illustrates that the experimenter is carrying out the house imagination, and this signal is judged as "No", the printed words of display output "No".Except output reflects the "Yes" and "No" printed words of motivation as visualization interface with display, the signal output interface can also comprise connection and the operation of miscellaneous equipment, thereby be applied to widely in the brain-computer interface equipment such as the switch of display, the switch of light fixture etc.
In sum, the present invention proposes the brain-machine interface method of a kind of P2 of utilization and VPP reflection brain signal.The method can be expanded the class scope of employed event related potential composition in the brain machine interface system, so that target brain electricity composition is not limited to P 300 and N170, and can reach reliable judgment accuracy.
Although the present invention with preferred embodiment openly as above, yet disclosed example is not to limit the scope of the invention.Be understood that in the situation that does not break away from spirit of the present invention, this can produce various additional, revise and replace.It will be apparent to those of skill in the art that in the situation that does not break away from spirit of the present invention or intrinsic propesties, can and utilize other elements, material and parts to realize the present invention with other special shapes, structure, layout, ratio.Those skilled in the art will recognize: the present invention can use the structure, layout, ratio, material and the parts that use in the invention reality and other many modifications, and these are modified in and are adapted to especially particular surroundings and operational requirements in the situation that does not break away from principle of the present invention.Therefore, current disclosed embodiment should be understood to illustrative but not to the restriction of its scope of asking for protection in all respects.
Claims (6)
1. brain-machine interface method based on P2 and top positive potential reflection brain signal comprises:
Step (1): play the visual stimulus signal;
Step (2): gather the EEG signals of being brought out by described visual stimulus signal;
Step (3): the described EEG signals that collects is processed, obtained described P2 and top positive potential waveform;
Step (4): analyze according to the described P2 that obtains and top positive potential waveform signal, obtain the brain-computer interface control signal;
Step (5): control output unit to produce output signal by described brain machine control signal.
2. method according to claim 1 is characterized in that, in step (1), adopts the picture stimulator that clear picture is converted into blurred picture, thereby produces described visual stimulus signal.
3. method according to claim 1, it is characterized in that, in step (2), also comprise: with the target electrode Cz of top and temporo occipital lobe position, FCz, P5/6, P7/8, PO5/6, the EEG signals that the PO7/8 place obtains is amplified, is carried out digital-to-analog conversion through amplifier, is stored in the computing machine in digital form.
4. method according to claim 1 is characterized in that, in step (3) EEG signals is processed, and also comprises pre-service: go at least a of the processing such as eye electricity, Baseline wander, linear rectification and filtering.
5. method according to claim 1 is characterized in that, specifically comprises in step (4):
Extract wave amplitude and the latent period of P2 and top positive potential, detect the significant difference that people's face brings out the ERP composition that brings out with non-face object;
The allied signal of P2 and top positive potential is carried out classification and Detection in characteristic frequency section and the special time window;
Classification and Detection result according to P2 and top positive potential judges, obtains the brain-computer interface control signal.
6. method according to claim 1 is characterized in that, step also comprises in (5): carry out described output signal visual.
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| CN105868562A (en) * | 2016-03-31 | 2016-08-17 | 天津大学 | Nerve work efficiency enhancing method and device based on steady state visual evoked potential modulation |
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| CN107468260A (en) * | 2017-10-12 | 2017-12-15 | 公安部南昌警犬基地 | A kind of brain electricity analytical device and analysis method for judging ANIMAL PSYCHE state |
| CN109002798A (en) * | 2018-07-19 | 2018-12-14 | 大连理工大学 | It is a kind of singly to lead visual evoked potential extracting method based on convolutional neural networks |
| CN109508094A (en) * | 2018-12-11 | 2019-03-22 | 西安交通大学 | A kind of vision inducting brain-machine interface method of the asynchronous eye movement switch of combination |
| CN109508094B (en) * | 2018-12-11 | 2020-03-31 | 西安交通大学 | Visual induction brain-computer interface method combined with asynchronous eye movement switch |
| CN110811558A (en) * | 2019-11-18 | 2020-02-21 | 郑州大学 | Sleep arousal analysis method based on deep learning |
| CN114343640A (en) * | 2022-01-07 | 2022-04-15 | 北京师范大学 | Attention assessment method and electronic equipment |
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