CN104007823A - Frequency phase hybrid decoding brain-computer interface method based on point-pass filtering and frequency phase hybrid decoding brain-computer interface device based on point-pass filtering - Google Patents

Abstract

The invention discloses a frequency phase hybrid decoding brain-computer interface method based on point-pass filtering and a frequency phase hybrid decoding brain-computer interface device based on point-pass filtering, and relates to the field of digital signal processing. The method comprises the following steps that a coefficient g(n) is used for filtering SSVEP (Steady-State Visual Evoked Potential) signals to obtain y(n); the point-pass filtering output signals y(n) are subjected to FFT (Fast Fourier Transformation) to obtain a final spectral analysis result Y(k); phase values phi1 and phi2 are respectively read from peak spectrums Y(m1) and Y(m2), and in addition, the corrected phase values phi<^>1 and phi<^>2 are respectively and further worked out; f1 and f2 are used as frequency decoded output, and (phi<^>1-phi<^>2)-phic is used as phase decoded output. The device comprises an analog-to-digital converter, a DSP (Digital Signal Processing) device and an output driving and display module, wherein collected signals x(t) are sampled through the analog-to-digital converter to obtain a sample sequence x(n), the sample sequence x(n) enters the DSP device in a parallel digital input form, and through internal processing of the DSP device, parameter estimation of the signals are obtained; then, commands sent by subjects are displayed through the output driving and display module, and finally, external equipment responds the corresponding commands.

Description

Frequency plot hybrid decoding brain-machine interface method and device thereof based on a pass filter

Technical field

The present invention relates to digital processing field, relate in particular to a kind of frequency plot hybrid decoding brain-machine interface method and device thereof based on a pass filter, while being specifically related to adopt in brain-computer interface device the Steady State Visual Evoked Potential of optional frequency to make driving source, by the controlled some bandpass filter of center frequency point, accurately leaching signal and carry out target identification.

Background technology

In order to realize and the exchanging and control of external environment condition ^[1], improve the quality of living, brain-computer interface (Brain-Computer Interface notes by abridging as BCI) ^[2]arise at the historic moment.Brain-computer interface does not rely on the normal output channel of brain, but sets up a kind of direct information interchange control channel between human brain and computing machine or other electronic equipments ^[3].By extracting the feature of EEG signals, and the brain instruction identifying or information are passed to controlled external unit, finally can complete the direct control of brain to external unit.

Research shows, when outer bound pair brain visual cortex is greater than 6Hz flicker excitation, by extracting occipital region EEG signals (EEG), will obtain Steady State Visual Evoked Potential ^[4].Because Steady State Visual Evoked Potential (steady-state visual evoked potential (SSVEP)-based SSVEP) has non-infringement, system configuration is simple, the training time is short and the advantage such as high information conversion ratio, the development of the brain-computer interface based on SSVEP in recent years rapidly.

Obviously, SSVEP signal is containing abundant pathology, physiology and psychographic information, is the important tool of the scientific research activities such as medical diagnosis, brain mind and cognition.But the actual EEG signals very faint (only having microvolt level) gathering with electrode at brain scalp place, also mainly be subject to two kinds of interference: a kind of is multiple artefact composition (as power frequency interference, eye electricity and Muscle artifacts etc.), and this interference is unfavorable for extraction and the analysis of pure EEG signals very much ^[5,6]; Another kind of for watching certain excitation block attentively as experimenter when absent minded, because indicator screen presents the excitation block of a plurality of different frequencies or phase place simultaneously, these disturb excitation blocks to the larger interference of optic nerve generation.Therefore,, for realizing target identification accurately, must introduce the above various interference of pre-service measure filtering.

Yet early stage most SSVEP-BCI system has been ignored the pretreatment operation to disturbing.For example document [7,8] is directly used canonical correlation analysis (Cononical Correlation Analysisi, CCA) to carry out target identification to different SSEVP excitation frequencies, yet CCA needs extra reference signal just can realize; For another example document [9] directly adopts FFT method to SSVEP signal working frequency and phase decoding, although FFT can make a distinction noise and useful pumping signal to a certain extent on frequency axis, but the intrinsic spectrum leakage effect of FFT can reduce discrimination, causes follow-up signal decoding accurate not.

Also there is at present scholar to be studied the noise of removing in EEG signals, as adopted rejection filter ^[10], comb filter ^[11], bandpass filter ^[12], wavelet de-noising ^[13,14]etc. method.Document [10] intention adopts rejection filter to eliminate 50Hz power frequency and disturbs, but when adopting Finite Impulse Response filter to realize, because technology is limit, its wave filter only can be realized the transport property (stopband is made as 45 to 55Hz) of band resistance, and cannot realize the trap that is fine to 50Hz single-frequency point, this inevitably can cause damage to the 50Hz electric composition of requiring mental skill around; Document [11] proposes comb filter to be applied to brain-computer interface Signal Pretreatment link, but comb filter exists the wide shortcoming of passband equally, and can only remove the interference of limited specific frequency, cannot realize the interference of removing optional frequency composition; In document [12], author adopts the logical iir filter of 4 rank Butterworth bands to remove noise, the transmission passband of this wave filter is 29Hz～35Hz, therefore can remove the interference outside this passband, and retain the excitation frequency composition that centre frequency is 32Hz, but clearly, this filter transmission band is still wide, due to iir filter, there is in addition the inherent shortcoming of nonlinear phase, therefore easily cause additive phase distortion, be difficult to guarantee phase extraction precision.For document [13,14] the noise-reduction method based on wavelet decomposition, because wavelet field noise reduction need to be realized in multistage decomposition, step is more loaded down with trivial details, and every grade is decomposed still certain frequency band just observe, rather than certain frequency, and SSVEP adopts single-frequency point excitation, therefore still there is room for promotion in the SSEVP target identification based on wavelet de-noising.

Therefore, for improving the performance of brain machine interface system and the accuracy rate of feature identification, its filter preprocessing process is very important, based on to existing pretreated defect analysis, pure for guaranteeing, to extract fast, without distortion SSVEP coded message, accurately to identify control command, what time following filter preprocessing measure should meet as far as possible: (1) filter bandwidht is narrower, the signal leaching is cleaner, and follow-up decode precision is higher; (2) for the SSVEP-BCIs that comprises phase information coding, pre-service must guarantee that wave filter itself has linear phase, otherwise the phase value recording is inaccurate, badly influences subsequent decoding precision; (3) disturb the pre-removing measure step of should trying one's best simple.

Summary of the invention

The invention provides a kind of frequency plot hybrid decoding brain-machine interface method and device thereof based on a pass filter, the present invention has removed because of various noises and has encouraged the interference that exists frequency deviation to produce, met the demand of pin-point accuracy identification SSVEP order, described below:

A frequency plot hybrid decoding brain-machine interface method based on a pass filter, said method comprising the steps of:

(1) the SSVEP signal of input is done to N point FFT, obtain preliminary analysis of spectrum result X (k), k=0,1 ..., N-1, and search for the two the highest FFT of place spectral lines, record respectively the spectrum peak position k=m of every bunch of spectral line ₁with k=m ₂;

(2) from predefined excitation frequency table f ₁, f ₂to f _nmiddle selecting respectively and m ₁Δ f, m ₂immediate 2 frequencies of Δ f, are assumed to be f ₁with f ₂, determine parameter lambda ₁with λ ₂, Δ f is frequency resolution;

(3) utilize m ₁with λ ₁value, m ₂with λ ₂value and convolution window value w _c(n) the coefficient g (n) of the logical FIR wave filter of point that, configured length is 2N-1;

(4) with coefficient g (n), SSVEP signal is carried out to filtering and obtain y (n);

(5) a pass filter output signal y (n) is done to FFT, obtain final analysis of spectrum result Y (k), k=0,1 ..., N-1;

(6) from peak value spectrum Y (m ₁) and Y (m ₂) on read respectively phase value with and further calculate respectively the phase value after correction with

(7) with f ₁, f ₂as frequency decoding output, as phase decoding output, wherein for presetting.

Described parameter lambda ₁with λ ₂for:

${\mathrm{\&lambda;}}_1=\frac{f_1}{\mathrm{\&Delta;f}}-m_1,{\mathrm{\&lambda;}}_2=\frac{f_2}{\mathrm{\&Delta;f}}-m_2.$

Described coefficient g (n) is:

with

A frequency plot hybrid decoding brain-computer interface device based on a pass filter, comprising: analog-to-digital conversion device, DSP device and output drive and display module.

The signal x (t) collecting is obtained to sample sequence x (n) through described analog-to-digital conversion device sampling, and the form of inputting with Parallel Digital enters described DSP device, through the inter-process of described DSP device, obtains the parameter estimation of signal; By described output driving and display module thereof, show the order that experimenter sends again, last order corresponding to external-device response.

Frequency plot hybrid decoding brain-machine interface method and device thereof based on a pass filter that the present invention proposes, if be applied to Practical Project field and clinical medicine domain, can produce following beneficial effect:

First of the present invention some bandpass filter parameter is adjustable, can meet the filtering demand of various frequencies;

The true frequency known by priori may be positioned at frequency axis any place, or has frequency deviation, and this wave filter is determined some bandpass filter parameter by m, λ are set;

For multifrequency composition, can be by a plurality of m, the existing transition function of λ substitution be set.

By regulating this two parameters, just can go out the some bandpass filter that any dot frequency is passed through by rapid configuration, relaxed the restriction to frequency, also guaranteed the pure property of desired signal.

The second jamming countermeasure of going based on a pass filter of the present invention, can remove because of various noises and encourage the interference that exists frequency deviation to produce, and meets the order demand of pin-point accuracy identification SSVEP;

The 3rd can accurately leach pumping signal in the situation of various frequency shift (FS)s, has objectively relaxed the requirement to excitation frequency, has increased object block quantity.

Accompanying drawing explanation

Fig. 1 is the basic comprising block diagram of brain machine interface system;

Fig. 2 is the process flow diagram of the frequency plot hybrid decoding brain-machine interface method based on a pass filter;

Fig. 3 is without frequency deviation time point bandpass filter filter factor and transmission curve;

Fig. 4 is waveform contrast figure before and after filtering during without frequency deviation;

For there is frequency deviation time point bandpass filter filter factor and transmission curve in Fig. 5;

Fig. 6 waveform contrast figure before and after filtering when there is frequency deviation;

Fig. 7 is two object block excitation display devices;

Fig. 8 is certain experimenter Oz electrode SSVEP signal filtering cross-reference figure;

Fig. 9 is the hardware implementation figure of the frequency plot hybrid decoding brain-computer interface device based on a pass filter;

Figure 10 is the internal processes process flow diagram of DSP device.

Embodiment

For making the object, technical solutions and advantages of the present invention clearer, below embodiment of the present invention is described further in detail.

Based on the problems of the prior art, it is platform that the SSVEP-BCI device of frequency plot hybrid coding is take in the present invention, the controlled some pass filter method of center frequency point is proposed, by SSVEP signal is carried out to a pass filter, leach exactly the signal of excitation frequency, effectively outer other all interference of filtering excitation frequency composition, make the pumping signal of any frequency deviation all can be used as target frequency, finally by crossing, the signal after pre-filtering is done to phase spectrum correction, accurately identify object block.Should relax the requirement to target excitation frequency by the brain-computer interface device based on a pass filter, improve target frequency number, by this pretreatment operation, also improve the off-line accuracy rate of system, there is very high value.

Brain machine interface system shown in Fig. 1 is broadly divided into four parts:

(1) by the external world, produce the various current potentials that bring out that comprise different frequency and first phase information (being coded message) and generate pumping signal;

(2) at cortical electrode or scalp electrode, electroneurographic signal is gathered, and carry out hyperchannel amplification, filtering and A/D conversion, complete pre-service;

(3) feature extraction and control command generate: utilize signal to process and algorithm for pattern recognition, extract the characteristic information that brings out current potential, and classify, decode and change, produce the control command corresponding with nervous activity pattern;

(4) utilize the control command producing to handle external environment condition and equipment.

Need emphasize, pre-service occupies an important position in above step, only in pre-treatment step, remaining with in SSVEP signal, and the various interference of filtering up hill and dale, follow-up feature extraction, decoding and the control command of guarantee is more accurate.And the controlled some bandpass filter of centre frequency that the present invention introduces has played indispensable effect in this link.

101: the SSVEP signal of input is done to N point FFT, and (making sampling rate is f _s, corresponding frequency resolution is Δ f=f _s/ N), obtain preliminary analysis of spectrum result X (k), k=0,1 ..., N-1, and search for the two the highest FFT of place spectral lines, record respectively the spectrum peak position k=m of every bunch of spectral line ₁with k=m ₂;

102: according to predefined excitation frequency table f ₁, f ₂to f _n, from excitation frequency table, select respectively and m ₁Δ f, m ₂immediate 2 frequencies of Δ f, are assumed to be f ₁with f ₂(or be f ₁with f ₃), determine parameter lambda ₁with λ ₂;

${\mathrm{\&lambda;}}_1=\frac{f_1}{\mathrm{\&Delta;f}}-m_1,{\mathrm{\&lambda;}}_2=\frac{f_2}{\mathrm{\&Delta;f}}-m_2---\left(1\right)$

103: utilize m ₁with λ ₁value, m ₂with λ ₂value and predefined convolution window value w _c(n) the coefficient g (n) of the logical FIR wave filter of point that, configured length is 2N-1 is

$g\left(n\right)=\frac{{2w}_c\left(n\right)}{N}\cos \left[\frac{2\mathrm{\&pi;}}{N}(m_1-{\mathrm{\&lambda;}}_1)n\right]+\frac{{2w}_c\left(n\right)}{N}\cos \left[\frac{2\mathrm{\&pi;}}{N}(m_2-{\mathrm{\&lambda;}}_2)n\right],n\&Element;[-N+1,N-1]---\left(2\right)$

104: with the some bandpass filter g (n) configuring, SSVEP signal is carried out to filtering and obtain y (n), complete and eliminate the pre-service of disturbing;

105: a pass filter output signal y (n) is done to FFT, obtain final analysis of spectrum result Y (k), k=0,1 ..., N-1;

106: from peak value spectrum Y (m ₁) and Y (m ₂) on read respectively phase value with and further calculate the phase value after correction with

107: with f ₁, f ₂as frequency decoding output, as phase decoding output (wherein for the fixed value of setting in advance).

Need point out: the some bandpass filter of step 103 design, to optional frequency, all can accurately extract, that is to say when SSVEP excitation frequency exists frequency deviation and also can under the prerequisite that retains excitation composition, remove outside band and disturb, be convenient to follow-up decoding and target identifying.

Experiment

Emulation experiment

In this emulation experiment, burst is { x (n)=acos (ω ₁n+ θ ₁)+bcos (ω ₂n+ θ ₂)+w (n), n=0,1 ..., L-1}, a=1 wherein, θ ₁=50 °, b=1, θ ₂=80 °, L=1200.W (n) is the random additive white noise producing, and is used for simulating the ground unrest in SSVEP signal.In this experiment, frequency resolution Δ ω=2 π/N, N is the length of frequency vector H, at this, makes N=64.For the logical characteristic of the point of deep this wave filter of explanation, below from without frequency deviation with there are two aspects of frequency deviation to verify.

(1) there is not frequency shift (FS) situation

In this situation, be ω ₁, ω ₂the situation that is the integral multiple of frequency resolution Δ ω, might as well make ω ₁=2 Δ ω, ω ₂=7 Δ ω, Fig. 3 (a), Fig. 3 (b) have provided respectively coefficient and the transfer curve of putting bandpass filter in this kind of situation.Fig. 4 (a), Fig. 4 (b) are respectively by the signal waveform before and after this bandpass filter filtering.

As can be seen from Figure 3, some bandpass filter transmission curve passes through ω just ₁=2 Δ ω, ω ₂the frequency of=7 Δ ω.The logical transport property of point that meets expectation.

As can be seen from Figure 4, with after this bandpass filter filtering, the noise of waveform is able to obvious inhibition, and signal is more pure.Waveform to Fig. 4 (a), Fig. 4 (b) is respectively the FFT that N=64 is ordered, and at k=2 and k=7 place, difference reading phase value, as shown in table 1:

Table 1 is phase place read value during without frequency deviation

In table 1, the ideal value of two phase places is respectively 50 ° and 80 °, can find out, the phase error directly producing from FFT result of spectrum analysis is 46.1212 °-50 °=-3.878 °, 88.9883 °-80 °=8.9883 °, and the phase error producing through pre-service of the present invention is 50.4629 °-50 °=0.4629 °, 79.5854 °-80 °=-0.4146 °.This illustrates that filter preprocessing of the present invention has played squelch effect, surveys phase error and is reduced.Because this situation belongs to, there is not frequency deviation, although thereby directly FFT survey phase method and have error, less, still can accept.

(2) there is frequency shift (FS) situation

This situation might as well be established ω ₀=2.3 Δ ω, ω ₁=6.8 Δ ω, the method that adopts the present invention to propose is processed signal.Fig. 5 (a), Fig. 5 (b) have provided respectively coefficient and the transfer curve of putting bandpass filter in this kind of situation.Fig. 6 (a), Fig. 6 (b) are respectively by the signal waveform before and after this bandpass filter filtering.

From Fig. 5 (b), can find out, even if can find to exist the some bandpass filter tradition curve that frequency shift (FS), the present invention configure also can just pass through ω completely ₀=2.3 Δ ω, ω ₁the frequency that the amplitude at=6.8 Δ ω places is 1, and eliminated the outer interference of other bands.

As can be seen from Figure 6, even if signal exists frequency deviation, with after this bandpass filter filtering, still can obtain pure signal, the noise of waveform is able to obvious inhibition equally.Waveform to Fig. 6 (a), Fig. 6 (b) is respectively the FFT that N=64 is ordered, and at k=2 and k=7 place, difference reading phase value, as shown in table 2:

Phase place read value when table 2 exists frequency deviation

By table 2, be not difficult to find, while there is frequency deviation, because spectrum is leaked and the existence of interference noise causes Direct Phase to read can producing very large error (surveying phase error and being respectively 110.5895 °-50 °=60.5895 °,-121.7470 °-80 °=-221.7470 °), can not meet the demand of clinical practice.And through pre-service of the present invention, phase error becomes very little (survey phase error and be respectively 49.0613 °-50 °=-0.9387 °, 79.4096 °-80 °=-0.5904 °), substantially negligible.

Comprehensively, do not have frequency deviation and exist two kinds of conditions of frequency deviation feelings to find out, (1), aspect removal interference, is being all feasible with the wave filter of the present invention's design aspect removal interference, and can completing the accurate extraction to multifrequency frequency content simultaneously.The center frequency point of the wave filter transmission curve of two kinds of situations all drops on the frequency position of prior setting exactly, and attenuation outside a channel is fast.The waveform of signal before and after contrast filtering, can find out filtering after waveform all obviously removed noise, and waveform all becomes pure; (2) aspect phase-measurement accuracy, the present invention with the frequency deviation value of wave filter to phase correction after, survey phase error and all than direct FFT, survey mutually and significantly reduce.

Thereby, based on the present invention, can under any offset frequency situation, complete the advantage of interference and precise phase extraction, for SSVEP-BCIs, just mean and can greatly relax the requirement to excitation frequency, make optional frequency all can be used as excitation frequency, for increasing incentives target number, provide theoretical support.

SSVEP surveys experiment mutually

(1) experimental provision

In this SSVEP-BCI system, survey and test mutually the frequency plot hybrid coding platform of building by University of Macao, and adopt some pass filter and the spectrum means for correcting thereof that the present invention proposes to disturb elimination and phase characteristic to extract.Test set sampling rate f _s=600Hz, the display that one 22 inches of experiment needs, refreshing frequency 120Hz, screen resolution are 1680 * 1050, as shown in Figure 7.

SSVEP excitation is to obtain by the line scan signals of this display is done to frequency division.In experiment, indicator screen is divided into two the incentives target pieces in left and right, and each object block is comprised of two sub-piecemeals; During coding, give these two sub-piecemeals with different flicker frequencies and phase place, during decoding, by detecting every pair of phase differential corresponding to object block, differentiate the target that experimenter watches attentively.

At this, survey in experiment mutually, the SSVEP after filtering is processed and proofreaied and correct measures phase place and (is designated as ) be not that the excitation phase of SSVEP (is designated as ).In fact, between SSVEP excitation and response, there is the reaction time of human brain, the time delay phase place that the corresponding generation of this brain response delay meeting varies with each individual between this three, meet following relation

Because decoding is according to excitation phase rather than measurement phase place therefore need further to determine time delay phase place

The present invention introduces double frequency SSEVP incentive program, by obtaining two excitation frequency f ₁with f ₂correction after measure phase difference identify incentives target.This phase differential is

In formula (5), as excitation frequency f ₁and f ₂while approaching very much, time delay phase difference value can be ignored, thus target when identification available measure phase difference replace actual phase difference, identify easily target excitation piece.But at this SSVEP, survey in experiment mutually two test frequency f that choose ₁and f ₂be respectively 10 frequency divisions and 11 frequency divisions of refreshing frequency 120Hz, be 12Hz (comprising that two first phases are the excitation of 0 ° and 180 °) and 10.9Hz (comprising that two first phases are the excitation of 0 °), its corresponding target block excitation frequency and object block frequency coding table are as shown in table 3

Table 3 half-court mixed excitation phase encoding parameters of display table

Obviously still there is certain intervals in selected two frequencies of table 3.Therefore the time delay phase differential in formula (5) can not ignore, need determine by clinical trial repeatedly.Many experiments discovery, at this moment the time delay phase difference value of two excitation frequencies (be designated as ) be relatively fixed as 36 °, so need on the basis of measure phase difference, deduct 36 ° of ability during practical application and identify more accurately object block.

(2) experimentation and result

Three experimenters (S1, S2, S3) are encouraged to induction, gather 13 electrode positions (PO3, PO5, PO7, POZ, PO4, PO6, PO8, P1, O1, OZ and O2) the SSVEP signal that produces, signal connects USB interface record by Electroencephalo signal amplifier, and sampling rate is 600Hz.This experiment collection EEG signals is divided into 5 and takes turns, and each is taken turns and comprises 10 collections.Each excitation Therapy lasted 8 seconds, in whole process, experimenter requires to focus one's attention on, and wherein encourages first 2 seconds for setup time, within latter 6 seconds, require experimenter to watch as requested corresponding flicker excitation block attentively, watch order attentively and hocket according to ' 1212121212 '.

At this, survey in experiment mutually, be mainly divided into following step:

Step1: SSVEP signal is adopted respectively a pass filter device and without two kinds of different scheme working frequency and phase estimation for filtering, obtains two groups and survey value mutually

Step2: ask for the measure phase difference value estimating in step 1 be used for replacing actual phase difference

Step3: phase difference value is asked in substitution (5)

Step4: differentiate with the phase difference value that the criterion shown in formula (6) is calculated Step3, to determine object block p;

In formula (6), M is number of targets, C _kfor corresponding desirable cluster centre.Therefore only need be from R while judging the classification of this object block ₁to R _min find out maximal value p and (seek R _kvalue is close to 1 o'clock corresponding k value), be identified object block label.

For the correctness that embodies institute of the present invention extracting method directly perceived, provide true SSVEP signal filtering cross-reference figure below.The SSVEP signal of certain experimenter Oz electrode of take is example, and after original signal and filtering, signal is respectively:

By Fig. 8, be not difficult to find, the SSVEP signal before filtering has mixed many undesired signals, and these come from the outside and the interference of self can have influence on follow-up decode procedure undoubtedly; And after a pass filter, it is many that signal is pure, only have useful signal corresponding to excitation frequency (12Hz and 10.9Hz) to be retained, this pre-service measure will inevitably improve the accuracy of decoding.

Experiment uses respectively two schemes (directly FFT method and some pass filter method) to carry out frequency plot identification classification to SSVEP signal, and (classification is two classes (excitation blocks 1 and 2), M=2), table 4 provides different experimenters is added to the target recognition accuracy that different length hanning window obtains.

The long different experimenters' of table 4 different window target recognition accuracy

In table 4, P_FFT represents that, through a bandpass filter bearing calibration, FFT is direct method.Be not difficult to find, the accuracy rate of target identification is not only long relevant with window, and also, with whether a process point pass filter has much relations, accuracy rate is after filtering apparently higher than direct FFT method, and Average Accuracy exceeds more than 10%.

Choosing window length is below 4 seconds, and number of targets M is set to 4, supposes that corresponding phase place is respectively 0 °, 90 °, 180 °, 270 °, again SSVEP signal is processed by two kinds of methods, and the accuracy rate result of contrast target identification is as shown in the table

The different experimenters' of table 5 estimation phase place and feature identification R _k(means standard deviation)

As shown in Table 5, identification incentives target piece only need be found out R ₁, R ₂, R ₃and R ₄in approach 1 value most.

(1) the direct FFT that is 10.9Hz for excitation frequency surveys phase situation, without any pretreated situation, because excitation frequency 10.9Hz is not the integral multiple of frequency resolution, from table 5 the 2nd row data can be clear that and can have very large deviation (desirable first phase value is 0 °, and actual survey is worth mutually deviation maximum and approaches 90 °) to survey phase average that should frequency.

(2) the introducing point pass filter pre-service that is 10.9Hz for the excitation frequency survey phase situation that line phase is proofreaied and correct of going forward side by side, although excitation frequency 10.9Hz is not the integral multiple of frequency resolution, from table 5 the 2nd row data can be clear that survey phase average that should frequency is only existed to less deviation (desirable first phase value is 0 °, and actual survey is worth mutually deviation and is substantially no more than 30 °).

(3) for excitation frequency, be that 12Hz surveys phase situation, because excitation frequency 12Hz be the integral multiple of resolution just, do not have frequency deviation, so error only comes from noise, rather than self frequency deviation factor, thereby be listed as from table 5 the 1st data can see the survey phase effect of two schemes (desirable average is 0 ° and 180 °, the phase place of surveying all distribute among a small circle) about the same near these two desirable averages.

(4) from table 5 the 1st row with the 2nd row also can find out, its standard deviation of surveying phase data all distributes among a small circle near 36 °, has verified to establish in advance the correctness that brain time delay phase is poor.

(5) observe classification results R in table 5 _k(k=1,2,3,4, M=4) corresponding p value (in table 5, the R of every row maximum _kvalue has been done mark with shade, corresponding k value is target p), because in this M=4 imaginary object block, corresponding to the object block of k=2 and the object block of k=4, suppose, only corresponding to the object block (being the object block of corresponding j=1) of k=1 and the object block (being the object block of corresponding j=2) of k=3, be only possible excitation, therefore p value during correct target identification also should be limited to p=1 and two kinds of situations of p=3, and its error probability is wanted greatly during than M=2.As can be seen from Table 5, for direct FFT, surveying phase decoder situation, there is frequency deviation in the excitation that is 10.9Hz due to frequency, add the noise outside band, therefore the result of nearly all target identification is all wrong, p value all detects the result for p=2 or p=4, has lost clinical reference value.And the binding site pass filter of introducing with the present invention and the method for phase correction, p value all detects the correct result for p=1 or p=3, i.e. the corresponding maximum decision value R detecting _kall, more than 0.95, mean that accuracy reaches 100%, therefore than direct FFT method, the inventive method is more reliable.

From above-mentioned emulation experiment and actual SSVEP survey phase study, all can find out, the point pass filter bearing calibration that the present invention proposes can be the in the situation that of any frequency deviation, filtering interference signals well, obtain clean EEG signals, by carrying out calibration accuracy, extract rapidly again the phase information of signal, therefore technology of the present invention has very wide application prospect in the engineering in medicine application of actual brain-computer interface.

Referring to Fig. 9, frequency plot hybrid decoding brain-computer interface device based on a pass filter comprises: analog-to-digital conversion device, DSP device and output drive and display module, by the signal x (t) collecting, through A/D (analog-to-digital conversion device), sampling obtains sample sequence x (n), form with Parallel Digital input enters DSP device, internal algorithm through DSP device is processed, and obtains the parameter estimation of signal; Relend and help output driving and display module thereof to show the order that experimenter sends, last order corresponding to external-device response.

Wherein, the DSP device of Figure 10 is core devices, in signal parameter estimation procedure, completes following major function:

(1) call core algorithm, complete the parameter estimation processing that receives signal;

(2) signal is carried out proofreading and correct after a pass filter, by phase estimation result substitution discriminant, carry out object block identification and send the corresponding command exporting in real time driving and display module to.

Need point out, owing to having adopted digitized method of estimation, thereby determined the complexity of Fig. 8 system, in real time the principal element of degree and degree of stability is not that the periphery of DSP device in Fig. 9 is connected, but the kernel estimation algorithm that DSP internal program memory is stored.

The internal processes flow process of DSP device as shown in figure 10.

The present invention implants proposed " the some pass filter that center frequency point is controlled " this kernel estimation algorithm in DSP device, based on this, completes high precision, low complex degree, incentives target identification efficiently.

Figure 10 flow process is divided into following several step:

(1) first need according to concrete application requirements (as paralytic's free indoor activity etc.), the sampling number N of signalization and the number of times i of duplicate measurements, and setting accuracy requirement according to specific needs.This step is from engineering aspect, to propose real needs, so that follow-up flow process is processed targetedly.

(2) then, CPU primary controller, from I/O port reads sampled data, enters internal RAM.

(3) follow-up " DC processing " is in order to eliminate the impact of the flip-flop in measured signal.Otherwise the existence of flip-flop, can reduce measuring accuracy.Flip-flop is easy to measure, and only needs the mean value that calculates sampling point to obtain.

(4) by Fig. 2 processing procedure of the present invention, carrying out filtering is the most crucial part of DSP algorithm, moves after this algorithm, can obtain clean SSVEP signal, on this basis phase place is made an estimate.

(5) judge whether the inventive method meets clinical demand, if do not meet, program is returned, and again sets as requested sample frequency and carries out next round phase measurement and sort out identification.

(6) until identification target is correct, can send correct control command.Repeat above measuring process i time.

(7) output bus by DSP exports outside display drive device to, and command instruction is passed to external unit.As the switch of controlling TV with adjust platform, control electric fan wind-speed gear, control seesawing of wheelchair etc.

Need point out, owing to having adopted DSP realization, make whole parameter estimation operation become more flexible, the concrete condition of the various components that can comprise according to signal, the inner parameter that changes algorithm by flexible in programming arranges, as sampling number N, sample rate f _sdeng.

List of references

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The embodiment of the present invention to the model of each device except do specified otherwise, the model of other devices does not limit, as long as can complete the device of above-mentioned functions, all can.

It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.

The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (5)

1. the frequency plot hybrid decoding brain-machine interface method based on a pass filter, is characterized in that, said method comprising the steps of:

(1) the SSVEP signal of input is done to N point FFT, obtain preliminary analysis of spectrum result X (k), k=0,1 ..., N-1, and search for the two the highest FFT of place spectral lines, record respectively the spectrum peak position k=m of every bunch of spectral line ₁with k=m ₂;

(2) from predefined excitation frequency table f ₁, f ₂to f _nmiddle selecting respectively and m ₁Δ f, m ₂immediate 2 frequencies of Δ f, are assumed to be f ₁with f ₂, determine parameter lambda ₁with λ ₂, Δ f is frequency resolution;

(3) utilize m ₁with λ ₁value, m ₂with λ ₂value and convolution window value w _c(n) the coefficient g (n) of the logical FIR wave filter of point that, configured length is 2N-1;

(4) with coefficient g (n), SSVEP signal is carried out to filtering and obtain y (n);

(5) a pass filter output signal y (n) is done to FFT, obtain final analysis of spectrum result Y (k), k=0,1 ..., N-1;

(6) from peak value spectrum Y (m ₁) and Y (m ₂) on read respectively phase value with and further calculate respectively the phase value after correction with

(7) with f ₁, f ₂as frequency decoding output, as phase decoding output, wherein for presetting.

2. a kind of frequency plot hybrid decoding brain-machine interface method based on a pass filter according to claim 1, is characterized in that described parameter lambda ₁with λ ₂for:

${\mathrm{\&lambda;}}_1=\frac{f_1}{\mathrm{\&Delta;f}}-m_1,{\mathrm{\&lambda;}}_2=\frac{f_2}{\mathrm{\&Delta;f}}-m_2.$

3. a kind of frequency plot hybrid decoding brain-machine interface method based on a pass filter according to claim 1, is characterized in that, described coefficient g (n) is:

$g\left(n\right)=\frac{{2w}_c\left(n\right)}{N}\cos \left[\frac{2\mathrm{\&pi;}}{N}(m_1-{\mathrm{\&lambda;}}_1)n\right]+\frac{{2w}_c\left(n\right)}{N}\cos \left[\frac{2\mathrm{\&pi;}}{N}(m_2-{\mathrm{\&lambda;}}_2)n\right],n\&Element;[-N+1,N-1].$

4. a kind of frequency plot hybrid decoding brain-machine interface method based on a pass filter according to claim 1, is characterized in that the phase value after described correction with for

with

5. the frequency plot hybrid decoding brain-computer interface device based on a pass filter, comprising: analog-to-digital conversion device, and DSP device and output drive and display module, it is characterized in that,

The signal x (t) collecting is obtained to sample sequence x (n) through described analog-to-digital conversion device sampling, and the form of inputting with Parallel Digital enters described DSP device, through the inter-process of described DSP device, obtains the parameter estimation of signal; By described output driving and display module thereof, show the order that experimenter sends again, last order corresponding to external-device response.