CN103019383A - Steady state visual evoked potential brain-computer interface signal identification method - Google Patents
Steady state visual evoked potential brain-computer interface signal identification method Download PDFInfo
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Abstract
本发明公开了一种稳态视觉诱发电位脑-机接口信号识别方法。本方法为：1)将若干不同图片以不同闪烁频率通过一视觉刺激单元同时显示，并采集被测试者注视该视觉刺激单元的脑电信号；2)数据处理单元对脑电信号进行噪声估计和降噪处理，然后进行特征提取和判决，初步确定该被测试者注视的图片；3)打乱显示图片的闪烁频率，采集脑电信号；然后对此次采集的脑电信号进行噪声估计和降噪处理，然后对处理后的脑电信号进行特征提取和判决，确定该被测试者注视的图片，如果此次确定的图片与步骤2相同，则将该图片作为最终确定的识别信息输出；如果不同，则判定该被测试者没有注视该视觉刺激单元显示的任何一幅图片。本发明可有效地提高脑电信号识别的准确性。
The invention discloses a steady-state visual evoked potential brain-computer interface signal recognition method. The method is as follows: 1) Simultaneously display several different pictures with different flickering frequencies through a visual stimulation unit, and collect the EEG signal of the subject gazing at the visual stimulation unit; 2) The data processing unit performs noise estimation and summing on the EEG signal Noise reduction processing, and then perform feature extraction and judgment to preliminarily determine the picture that the subject is looking at; 3) disrupt the flickering frequency of the displayed picture, and collect EEG signals; then perform noise estimation and noise reduction on the EEG signals collected this time. Noise processing, and then feature extraction and judgment are performed on the processed EEG signal to determine the picture that the subject is watching. If the picture determined this time is the same as step 2, then the picture is output as the final identification information; if different, then it is judged that the subject has not watched any picture displayed by the visual stimulation unit. The invention can effectively improve the accuracy of electroencephalogram identification.
Description
Technical field
The invention belongs to neural field of engineering technology, be specifically related to Steady State Visual Evoked Potential and brain-computer interface signal recognition method.
Background technology
Brain-computer interface (Brain-Computer Interface, BCI) is based on the system that EEG signals realizes brain and computing machine or other equipment Direct Communications and control.Brain-computer interface can directly be converted to the information that brain sends can drive the external unit order, and can replace people's limbs etc. to realize exchanging of people and the external world and to the control of external environment condition.Brain that the brain-computer interface system behaves has been opened up one and brand-new has been carried out the approach of communication and control with the external world, so that the idea of utilizing people's brain signal directly to control external unit becomes possibility.
Present brain-computer interface system exists intrusive mood and non-intrusion type two large classes.The signal accuracy that the brain-computer interface of intrusive mood obtains is relatively high, and signal to noise ratio (S/N ratio) is high, is easy to analyzing and processing, but need to carry out operation of opening cranium to the user, be not easy to long signals collecting, and user's brain caused infect or damage easily, danger is larger.Although its brain signal noise that obtains of the brain-computer interface of non-intrusion type is large, the property distinguished of signal characteristic is poor, but its signal relatively easily obtains simultaneously, can user's brain not damaged, and along with the continuous progress of signal processing method and technology, processing to scalp EEG signal (electroencephalogram, EEG) can reach certain level, becomes possibility so that the brain-computer interface system of non-intrusion type enters the real life application.In recent years, the brain-computer interface technical development is very fast, embodies important value in fields such as biomedicine, virtual reality, Entertainment, rehabilitation project and space flight, military affairs.
Event related potential (Event Related Potentials, ERPs) refers to the average rear viewed a series of potential change of EEG signals relevant with the stimulation event and that lock with stimulation in time.It not only depends on the physical attribute of environmental stimuli, and also subjectivity processing and the cognitive state with brain has close relationship.VEP (Visual Evoked Potential, VEP) is a kind of widely used event related potential composition, and it refers to that nervous system accepts the certain electric activity that visual stimulus (for example figure or flash stimulation) produces.When visual stimulus is fixed as some frequencies (generally greater than 6Hz), its VEP that causes overlaps in time, the brain visual cortex can produce Steady State Visual Evoked Potential (Steady State VEP, SSVEP), this brings out electric potential signal and presents the periodicity consistent with visual stimulus, can realize visual stimulus under different frequency and the out of phase condition by the frequency spectrum of analyzing this signal, be a kind of brain-computer interface input signal that has using value.
With Steady State Visual Evoked Potential as the brain-computer interface system of input signal have that rate of information transmission is high, the training time is short, the easy advantage such as extraction of feature; but very unstable owing to EEG signals and often have electromyographic signal interference, thereby often some mistakes can appear when causing classifying.This paper has proposed the mode that a kind of active is confirmed, can effectively reduce false determination ratio.
Summary of the invention
The purpose of this invention is to provide a kind of Steady State Visual Evoked Potential brain-computer interface signal recognition method, be to present a plurality of flicker pictures on the screen, the testee only watches a certain width of cloth picture wherein attentively, when detecting the testee and watch certain width of cloth picture attentively, utilizes the mode of initiatively confirming, the flicker frequency (image content is constant) of upsetting all pictures, again detect, if twice identical, then we think that previous detection is correct, if different, once be error detection before then thinking.
Technical scheme of the present invention is:
A kind of Steady State Visual Evoked Potential brain-computer interface signal recognition method the steps include:
1) sets up a visual stimulus unit, be used for showing the picture of some different contents, when watching attentively for the testee;
2) picture with some different contents shows by this visual stimulus unit simultaneously with different flicker frequencies, and gathers the EEG signals that this testee watches this visual stimulus unit attentively, stores data processing unit into;
3) this data processing unit carries out noise estimation and noise reduction process to the EEG signals that gathers;
4) to step 3) EEG signals after processing carries out feature extraction and judgement, tentatively determines the picture that this testee watches attentively;
5) upset the flicker frequency of this shown picture in visual stimulus unit, and gather the EEG signals that this testee watches this visual stimulus unit attentively, store data processing unit into; Then this data processing unit carries out noise estimation and noise reduction process to the EEG signals of this collection;
6) to step 5) EEG signals after processing carries out feature extraction and judgement, determines the picture that this testee watches attentively, if the picture that picture of this time determining and step 4 are determined is identical, then this picture is exported as final definite identifying information; If not identical, judge that then this testee does not watch any width of cloth picture that this visual stimulus unit shows attentively.
Further, step 2) before, at first the visual stimulus unit is shown as blank screen, gather the testee and watch the EEG signals in this visual stimulus unit blank screen setting duration attentively and store data processing unit into; The average frequency spectrum of EEG signals carried out the noise estimation to the EEG signals of being adopted when then this data processing unit utilized tranquillization.
Further, described setting duration is 10s.
Further, the method that gathers described EEG signals is: at this testee's head occipital lobe O
ZPosition of sound production one EEG electrode, the wide position of sound production reference electrode of picking up the ears, opposite side auricle position of sound production ground-electrode obtains the signal of electrode collection after by differential amplifier, analog to digital converter testee's EEG signals.
Further, determine that tentatively the method for the picture that this testee watches attentively is: with step 3) process rear EEG signals corresponding to the energy of each picture, as the EEG signals feature of corresponding picture; Choose EEG signals eigenwert the maximum and setting threshold and compare, when exceeding this setting threshold, what then the preliminary judgement testee watched attentively is picture corresponding to this EEG signals eigenwert the maximum; Then otherwise judge that the testee does not watch any width of cloth picture, repeating step 2 attentively)～4).
Further, step 3) in, this data processing unit carries out noise estimation and noise reduction process to the signal of EEG electrode collection in the EEG signals that gathers.
This Steady State Visual Evoked Potential brain-computer interface system initiatively confirms device, comprises with lower module (such as Fig. 1):
1. stimulating module: stimulating module mainly is to comprise a large display screen.Purpose is that the picture that will contain certain content utilizes display screen to show, when watching attentively for the testee (such as Fig. 2).
2. signal acquisition module: signal acquisition module mainly comprises electrode, differential amplifier, analog to digital converter (such as Fig. 3).Purpose is the EEG signals that gathers the testee, and utilizes wireless transmission that EEG signals is passed to receiving end.
3. signal processing module: signal processing module comprises that mainly noise estimates and noise reduction process feature extraction and classification (such as Fig. 4).Because EEG signals is unstable, and the interference of the signals such as myoelectricity may be arranged, so in order to promote signal to noise ratio (S/N ratio), make more robust of system, we need to carry out noise and estimate and noise reduction process after receive data, utilize afterwards each frequency energy as feature, classify, thereby obtain the picture that the testee watches attentively.
4. initiatively confirm module: through after the subseries judgement, we initiatively upset the picture flicker frequency, carry out again noise reduction, classification and a judgement, if the result is with front once identical, before then thinking once judgement accurately, thereby reached the purpose of an affirmation.
Compared with prior art, good effect of the present invention is:
The present invention has not only kept the Steady State Visual Evoked Potential advantage of system, and namely rate of information transmission is high, and the training time is short, the easy extraction of feature etc., but also the mode that a kind of active is confirmed is proposed, improve whereby classification accuracy rate.
Description of drawings
Fig. 1 is process flow diagram of the present invention;
Fig. 2 stimulates interface synoptic diagram (stimulating order and the quantity of Image Display to change according to demand);
Fig. 3 is the signal acquisition module process flow diagram;
Fig. 4 is the signal processing module process flow diagram.
Embodiment
Below with reference to accompanying drawing of the present invention, most preferred embodiment of the present invention is described in more detail.
Brain-computer interface method based on the Steady State Visual Evoked Potential of initiatively confirming may further comprise the steps:
Step 1: at testee's head occipital lobe O
ZPosition of sound production electroencephalograph electrode (EEG electrode), the one wide position of sound production reference electrode of picking up the ears, opposite side auricle position of sound production ground-electrode, signal by the electrode collection obtains testee's EEG signals (such as Fig. 2 and Fig. 3) after by differential amplifier, analog to digital converter, and utilize wireless device and computing machine to carry out data transmission, the EEG signals of utilizing computer stored to gather.
Step 2: utilize display screen show to stimulate picture, at first display screen can show the blank screen of 10s, and purpose is the EEG signals when recording tranquillization, estimates and noise reduction process so that noise is carried out in the back.(number can increase and can subtract with 6 afterwards, decide with demand) contain certain content and (for example drink water, have a meal, see TV etc.) the flicker picture of different frequency be presented on simultaneously on the display screen, position of appearing respectively upper left at screen, on, upper right, lower-left, in lower, bottom right, testee's head distance display screen is 50～100 centimetres (such as Fig. 2).
Step 3: the testee watches or the middle section (namely not watching wherein any width of cloth picture attentively) of screen in above-mentioned 6 pictures attentively.
Step 4: the signal that the EEG electrode is adopted carries out noise estimation and noise reduction process, the average frequency spectrum of EEG signals when noise is estimated to adopt tranquillization, and available noise-reduction method is a lot, provides some simple implementation methods here.
Spectrum subtracts (Spectral Subtraction) and is proposed in 1979 by Boll, the ultimate principle that spectrum cuts algorithm is that the hypothesis noise is incoherent, extra to the target EEG signals, the short-time magnitude that deducts noise from noisy EEG signals composes to obtain the short-time magnitude spectrum of echo signal, and the estimation of its noise signal be by when the experimenter is quiet (when front 10s notes blank screen) record.Fundamental formular is as follows
|X(k)|＝|S(k)|+|N(k)|
Wherein X (k) is noisy EEG signals frequency spectrum, and S (k) is target EEG signals frequency spectrum, and N (k) is noise spectrum.Behind the estimation N (k) that obtains the noise amplitude spectrum, just can obtain the estimation of the amplitude spectrum of target EEG signals
Except above-mentioned basic spectrum cuts algorithm, Berouti etc. revise it: (1) is when deducting noise spectrum, regulate the degree that subtracts according to signal to noise ratio (S/N ratio), be multiplied by one greater than the parameter of l to noise spectrum when subtracting by spectrum, make the value that when spectral substraction, deducts than the noise spectrum more (2) of estimating in the larger place of noise amplitude, the spectrum of signal that will be not estimated is set to 0, and introduces the concept of noise floor (Spectral Floor), at these local a little noises that keeps.The estimation of the target EEG signals short-time spectrum after revising becomes
|S(k)|＝max{|X(k)|-α|N(k)|，βN(k)}
Wherein α＞1 is less when signal to noise ratio (S/N ratio) is larger, and 0≤β＜＜1 is a fixed value.
Certainly, available method also has a lot, and for example the short-time magnitude of optimum MMSE spectrum is estimated, MMSE logarithmic spectrum amplitude Estimation, and nonlinear spectral subtracts etc., has only provided two kinds of methods that are simple and easy to realize here and has realized noise estimation and noise reduction process as example.
Step 5: the EEG signals after the denoising is carried out feature extraction and judgement.We obtain first the energy of these corresponding in the denoising brain electricity 6 frequencies, are designated as E
1～E
6Because different people's EEG signals is strong and weak different, so we are respectively with the E that extracts
1～E
6Divided by they and (namely carrying out a normalized process), be designated as e
1～e
6, and with it as feature.The method of judgement also has a lot, only provides a method that is simple and easy to realize here as example.We select first e
1～e
6In the maximum, then compare with threshold value, this threshold value determines by test experiment, in test experiment, gather the EEG signals under the stimulation picture state that a small amount of testee watches respectively 6 kinds of frequencies attentively, this threshold value is that the population mean of the feature of above-mentioned EEG signals multiply by a coefficient of rule of thumb determining, when exceeding this threshold value we just the preliminary judgement testee watches attentively is the picture of that frequency scintillation of its representative; Then we think that the testee does not watch any width of cloth picture attentively and sees if be lower than threshold value, get back to step 3 and begin new round experiment.
Step 6: when preliminary judgement testee stares at a certain width of cloth picture, we need to utilize a process of initiatively confirming again to determine the picture that the testee watches attentively, at this moment the flicker frequency (but not changing its position) of upsetting at random 6 width of cloth figure, it (can be the picture of just now watching attentively that the testee watches a certain width of cloth picture attentively, can not be yet) or center Screen, afterwards the EEG signals that newly collects is carried out step 4 and step 5 (set threshold value is constant in the step 5) again, if twice we judge that the picture that the testee watches attentively is identical, what think then that the testee watches attentively really is the picture that we judge, just can show this width of cloth picture with the animation form on the screen, simultaneity factor can be sent the voice that are consistent with image content, and (for example: you will drink water/have a meal ...) as a result of feed back to the testee, at this moment the testee tells content (certain width of cloth picture or the center Screen) picture whether we judge that we watch attentively for twice, to verify our system's Accuracy of Judgement, get back to afterwards step 3 and begin new round experiment; If twice difference, once may be error detection before then thinking, the testee does not watch any width of cloth picture attentively and sees, simultaneity factor can be sent voice suggestion (for example: you do not watch the picture in the screen attentively), at this moment still allow the testee tell the content that we watch attentively for twice, to verify our system's Accuracy of Judgement, still get back to afterwards step 3 and begin new round experiment.
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| CN113907757B (en) * | 2021-10-18 | 2022-09-30 | 中国民航大学 | A vigilance test method based on attention system theory |
| CN116584957A (en) * | 2023-06-14 | 2023-08-15 | 中国医学科学院生物医学工程研究所 | Data processing method, device, equipment and storage medium of hybrid brain-computer interface |
| CN118873156A (en) * | 2024-09-30 | 2024-11-01 | 中国人民解放军总医院第三医学中心 | A method for measuring critical flicker fusion frequency based on electroencephalogram |
| CN118873156B (en) * | 2024-09-30 | 2024-12-13 | 中国人民解放军总医院第三医学中心 | Method for measuring critical flicker fusion frequency based on electroencephalogram |
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