CN104951082A - Brain-computer interface method for intensifying EEG (electroencephalogram) signals through stochastic resonance - Google Patents
Brain-computer interface method for intensifying EEG (electroencephalogram) signals through stochastic resonance Download PDFInfo
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Abstract
The invention discloses a brain-computer interface method for intensifying EEG (electroencephalogram) signals through stochastic resonance. According to the method, the stochastic resonance phenomenon discovered in the human brain perception process is used, a method for regulating cortex EEG synchronization through stochastic resonance is used, the improvement on the signal-to-noise ratio of the EEG signals, reduction of the difference between subjects and improvement on the stability of signal sources are realized from the source, and a new way for intensifying EEG signal sources is provided. The relation among the electrophysiological activity rule of a sensorimotor brain area, sensory perception and stochastic noise is modeled, an EEG feedback regulation mode based on a classic brain-computer interface technology is constructed, and the effect of the brain-computer interface technology characterized by EEG rhythm signals of scalp EEG is enhanced.
Description
Technical field
The invention belongs to biomedical engineering and medical Instrument field, relate to a kind of brain-machine interface method, particularly relate to a kind of method that accidental resonance strengthens brain-computer interface brain wave rhythm signal.
Background technology
Brain-computer interface technology is the information transfer channel not relying on peripheral nerve and muscle, and directly realizes the technology with external environment communication by obtaining brain signal.Clinically, the brain wave rhythm signal in conventional sensorimotor cortex district is as control signal source, its application may be summarized to be two kinds: a kind of is help disabled person to realize and the communication of external environment, as artificial limb and wheelchair motion control, use domestic environment controller to operate various electrical equipment etc.; Another kind is that this rehabilitation maneuver not only can help patient's oneself's assistant massaging and motion, also can strengthen the function of corresponding nerve pathway, contributes to the real motion function recovering limbs for the personnel of kinesitherapy nerve pathway injury provide initiative rehabilitation to assist.Except these two kinds of typical apply, the brain machine interface system based on scalp EEG signals also have cost low, easy to operate, to advantages such as brain not damageds, be one of Main way of current brain-computer interface research.
Due to the complicacy of brain, make the brain-computer interface signal source based on scalp EEG signals faint, and poor stability.At present, the brain-computer interface technology based on the independent rhythm of sensorimotor cortex mainly overcomes this problem by feedback training, the method such as signal transacting and method for classifying modes, change experiment model and Improving Equipment coupling targetedly of introducing.Although these methods improve to system performance, still have some shortcomings: as long in the tested training time, even invalid; Input is limited to the shortcomings such as brain wave rhythm signal to noise ratio (S/N ratio) is low.Which greatly limits the process of brain-computer interface technical application.
Stochastic Resonance Phenomenon is a kind ofly usually familiar with upper contrary, the mechanism of eliminating random (noise) with people.It is present in nonlinear system, and characteristic feature is when random perturbation (noise) adds nonlinear system, can occur the phenomenon that system output signal-to-noise ratio goes up not down.Nervous system is typical nonlinear system, and nervous system, sensory perception, cognition and cortex synchronization etc. all exist the operative condition of accidental resonance to have large quantity research to confirm.By the inspiration of these Research Thinkings, the present invention regulates the synchronized phenomenon of brain wave rhythm to start with from accidental resonance, the Stochastic Resonance Phenomenon introduced in sensation cognitive process carrys out the intensity of the brain wave rhythm in wild phase Ying Nao district, improves from the direction exploitation improving EEG signals source with the technology of the practicality of brain wave rhythm signal brain-computer interface control signal.Patent [publication No., CN 103970273.A] have employed accidental resonance to strengthen the response intensity of VEP, reaches and promotes the precision of existing brain-computer interface and the object of efficiency.Evoked ptential is a kind of important signal source in brain-computer interface technology, has by force anti-interference, and user is without the need to the too much advantage such as training.The brain-computer interface technology being signal source with scalp brain electricity spontaneous rhythm (as SCP (SCP) and μ or beta response) does not rely on the input of extra stimulation, visual fatigue can not be produced, there is no adaptive problem, there is more wide and user demand flexibly.But spontaneous rhythm is easily interfered, often needs to carry out training for a long time by user and reach the stability of performance.
Summary of the invention
The object of the invention is for the brain-computer interface technology of sensorimotor cortex district brain wave rhythm as signal source, utilize the Stochastic Resonance Phenomenon found in human brain perception, by the synchronized method of accidental resonance regulation and control Cortical ECoG, attempt improving from source the signal to noise ratio (S/N ratio) of EEG signals, reduce subject's otherness and improve the stability of signal source, to utilize in nonlinear system Stochastic Resonance Theory to build a kind of brain electricity regulate and control method of practicality.
Described Stochastic Resonance Phenomenon is defined as: a kind of mechanism being usually familiar with upper contrary elimination random (noise) with people.It is present in nonlinear system, and characteristic feature is when random perturbation (noise) adds some nonlinear system, can occur the phenomenon that output signal-to-noise ratio goes up not down.Wide in rangely to be defined as: add the phenomenon that random perturbation (or noise) is conducive to system information process or strengthening target signature.The ultimate principle that the present invention utilizes for utilizing external random noise, by regulating interior raw noise source and then regulating neural information processing capability.
For reaching this object, the technology contents that the present invention adopts is as follows:
The inventive method is applied to the brain machine interface system of following motorsensory cortex district EEG signals, and this system comprises electroencephalogramsignal signal acquisition module, electroencephalogramsignal signal analyzing module, pattern recognition module, accidental resonance inverting module, human-computer interaction module, feedback regulation module; The output terminal of electroencephalogramsignal signal acquisition module is connected with the input end signal of electroencephalogramsignal signal analyzing module; An output terminal of electroencephalogramsignal signal analyzing module is connected with the input end signal of pattern recognition module, and another output terminal is connected with the input end signal of accidental resonance inverting module; The output terminal of pattern recognition module is connected with the input end signal of human-computer interaction module; The output terminal of accidental resonance inverting module is connected with the input end signal of feedback regulation module; The output stimulus signal of feedback regulation module acts on user;
Described electroencephalogramsignal signal acquisition module is by electrode, impedance matching and protection circuit, signal amplifier, low pass and rejection filter, simulating signal turn digital efm signal and control and communication unit etc. form, for gathering EEG signals.
Described electroencephalogramsignal signal analyzing module is the digital brain electrical signal being received electroencephalogramsignal signal acquisition module output by USB interface or serial line interface RS-232, for analyzing the brain wave rhythm change of sensorimotor cortex district.
Described pattern recognition module carries out digital filtering process, feature extraction and pattern discrimination to the EEG signals that electroencephalogramsignal signal analyzing module exports.
Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, this module can run in electroencephalogramsignal signal analyzing module in a software form, also the device of a software and hardware integration can be become separately, the anti-release of EEG signals prosodic feature that this module can export according to electroencephalogramsignal signal analyzing module is that brain causes the optimization model of accidental resonance by outside stimulus, and refutation process can be realized by the method such as fuzzy reasoning, Bayesian decision.
Described human-computer interaction module is by the Composition of contents of computer display and upper display thereof, and it is used to refer to user and completes the motion of right-hand man's phenomenon, and presents feedback result with icon etc. with the mark of indicative character.
Described feedback regulation module by sound generation unit, transcutaneous electrostimulation and vibratory stimulation etc. one of them noise energy driving source and analog line driver form, this module, after the output information receiving accidental resonance inverting module, is various output signal and then the corresponding sensory channel of stimulation user convert information.
The present invention proposes a kind of accidental resonance that utilizes based on said system and strengthens the brain-machine interface method of EEG signals, and the method includes the steps of:
Step (1), according to 10-20 electrode for encephalograms wearing mode, choice for use person left and right brain district C3, C4 are as posting field; Potential electrode is laid in posting field, and lay reference electrode in one-sided ear-lobe position, lay ground electrode at Fpz place of head forehead place, the EEG signals then recorded by above-mentioned electrode sends into electroencephalogramsignal signal acquisition module, through amplifying and being sent to electroencephalogramsignal signal analyzing module after analog to digital conversion;
C3, C4 are motor cortex region, scalp both sides for described left and right brain district.
Step (2), by imagination left hand and the motion of the imagination right hand, can there is at left and right brain district C3, C4 the electrical energy of brain change that desynchronization causes; The brain wave rhythm change of electroencephalogramsignal signal analyzing module analysis sensorimotor cortex district, wherein said brain wave rhythm is the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things, specifically:
First 2.1 set a sliding time window, electroencephalogramsignal signal analyzing module carries out filtering pre-treatment for the EEG signals in same time window potential electrode, wherein filtering pre-treatment adopts the Laplace transformation method with spa-tial filter properties, specifically with the brain electric strength value (E being positioned at centre position electrode
1) the average brain electric strength value that deducts surround electrode obtains the brain electric strength value E ' of centre position electrode
1, namely
this computing can give prominence to the signal intensity that centre position electrode obtains, the interference of filtering ambient signals.
2.2 for a certain potential electrode, brain wave rhythm signal energy after task starting point is quantized, specifically get first time window rhythm and pace of moving things energy after task starting point as benchmark, then with fixed step size sliding time window, the relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window asking for this potential electrode.
Step (3), build the accidental resonance parameter model of user in advance:
Described accidental resonance parameter model adopts classical Graz-BCI Mental imagery experimental paradigm to build, wherein after determining accidental resonance pattern (i.e. input stimulus source), by multidimensional matching function method, form according to the changing value curve of the brain wave rhythm signal obtained described in noise figure and above-mentioned steps 2.2 and set up the accidental resonance multi-parameters model of individuation, using the method asking for extreme value to ask for optimal stimulus parameter to its fitting function.
The Graz-BCI Mental imagery experimental paradigm task of described classics is divided into right-hand man's action/imagination right-hand man action, finger movement/imagination finger movement, grasping/imagination to grasp, selected irritating noise source is gaussian shaped profile white noise, frequency band limits is at 0-100 hertz, and concrete operations are:
After selected wherein a kind of accidental resonance pattern (i.e. irritating noise source), by regulating the threshold of feelings measuring method determination noise threshold of size according to psychophysics experiments of noise energy; According to the number percent of noise threshold intensity, this noise is divided into several intensity levels; Stochastic inputs certain noise level value above-mentioned in test process, then user performs corresponding task and by brain wave acquisition module record brain wave rhythm signal, simultaneously by electroencephalogramsignal signal analyzing module analysis brain wave rhythm signal acquisition noise figure, set up the change curve of noise figure and brain wave rhythm signal, and obtained the optimum noise figure causing the strongest brain wave rhythm signal by extremum method;
All the time noise is there is during the Classic Experiments normal form of described accidental resonance is task, in the present invention in order to the identification problem of thinking idle condition in right-hand man's Mental imagery, the applying mode of random noise except classics allow noise be present in various task all the time during, too increase synchronous accidental resonance pattern, its step is that the applying process of noise is synchronous with tasks carrying simultaneously.
Step (4), two classification task by imagination motion control one dimension cursor movement, imagination motion can cause the brain wave rhythm in above-mentioned C3, C4 brain district to change, in task implementation, there are two class feedbacks: a class is visual feedback, carry out imagination motion in order to instruct user and control cursor moving; Another kind of is based on accidental resonance parameter model, is stimulated the online feedback regulated by random external; Concrete operations are:
4.1 visual feedback tasks carrying processes:
User watches the visual feedback prompting on human-computer interaction interface attentively, can imagine motion accordingly according to prompting user, controls two classification task of one dimension cursor movement on human-computer interaction interface by comparing the EEG signals energy difference led in C3, C4 brain district;
The online feedback that 4.2 random stimulus regulate:
From random electro photoluminescence/mechanical vibration as noise source, across sensation path audile noise, through the electro photoluminescence of skin electric current vestibular, choose a kind of accidental resonance pattern as input stimulus amount, input in the accidental resonance parameter model of the identical accidental resonance pattern built in advance, in order to regulate and control the brain wave rhythm of human cortical brain; Wherein said random electro photoluminescence/mechanical vibration are act on inner noise circumstance by sense of touch and sense of hearing sensation path as noise source, across the audile noise of sensation path, and then have influence on cranial nerve maincenter; Described can directly act on human cortical brain through the electro photoluminescence of skin electric current vestibular; Three kinds of above-mentioned irritating noises are gaussian shaped profile white noise, and frequency band limits is at 0 ~ 100 hertz.
4.3 by accidental resonance parameter model, define the fitting function of different noise level value and brain wave rhythm signal intensity curve, accidental resonance inverting module is in conjunction with this fitting function, by the method such as fuzzy reasoning, Bayesian decision, the optimum inverting stimulated is realized to the corresponding relation of noise figure and brain wave rhythm signal, obtains the optimal stimulus parameter of corresponding accidental resonance pattern.
The optimal stimulus parameter of 4.4 corresponding accidental resonance patterns step 4.3 obtained is input to feedback regulation module, physical characteristics according to this accidental resonance pattern stimulates user's human body, produce a kind of noise stimulation energy to realize stimulating the feedback of user, and then regulation and control brain accidental resonance occurrence condition, change brain wave rhythm signal, thus reach the object that EEG signals source is strengthened.
Step (5), user are under the feedback regulation obtaining brain power supply signal, according to the EEG signals that electroencephalogramsignal signal acquisition module collects, and obtain brain wave rhythm feature by brain electricity analytical module, linear discriminant analysis (as Fisher linear discriminant analysis) is adopted to obtain recognition result finally by pattern recognition module, output to human-computer interaction interface, described human-computer interaction interface is graphoscope.
The invention has the beneficial effects as follows:
1, the present invention uses exogenous signals to regulate and control brain wave rhythm, provides a kind of technical method, for brain-computer interface technology provides the signal of high s/n ratio for strengthening brain source signal.
2, the present invention can be the brain-computer interface user of brain wave rhythm as BCI system control signal in use scalp motorsensory cortex district, reduces the time of feedback training, improves the applicability of user.
3, the invention provides the applying method of multiple external random noise, utilize random electro photoluminescence/mechanical vibration as noise source, mutual and through the source of skin electric current vestibular stimulation as outside stimulus across sensory channel sensation.
4, the invention provides a kind of defining method of off-line accidental resonance regulating parameter, further, provide the online brain-computer interface technology regulated based on accidental resonance.
5, the off-line that the present invention realizes simply, stability is high or online brain wave rhythm brain-computer interface control technology, can expand the application crowd scope of similar brain-computer interface technology.
Accompanying drawing explanation
Fig. 1 is motorsensory cortex district schematic diagram, and wherein a is that electrode for encephalograms lays schematic diagram, and b is brain electricity spatial filtering schematic diagram;
Fig. 2 is the applying mode schematic diagram of three kinds of accidental resonance patterns;
Fig. 3 is the applying mode schematic diagram of accidental resonance;
Fig. 4 is the online brain-computer interface example regulated based on the brain wave rhythm of accidental resonance.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
The inventive method is applied to the brain machine interface system of following motorsensory cortex district EEG signals, and this system comprises electroencephalogramsignal signal acquisition module, electroencephalogramsignal signal analyzing module, pattern recognition module, accidental resonance inverting module, human-computer interaction module, feedback regulation module; The output terminal of electroencephalogramsignal signal acquisition module is connected with the input end signal of electroencephalogramsignal signal analyzing module; An output terminal of electroencephalogramsignal signal analyzing module is connected with the input end signal of pattern recognition module, and another output terminal is connected with the input end signal of accidental resonance inverting module; The output terminal of pattern recognition module is connected with the input end signal of human-computer interaction module; The output terminal of accidental resonance inverting module is connected with the input end signal of feedback regulation module; The output stimulus signal of feedback regulation module acts on user;
Described electroencephalogramsignal signal acquisition module is by electrode, impedance matching and protection circuit, signal amplifier, low pass and rejection filter, simulating signal turn digital efm signal and control and communication unit etc. form, for gathering EEG signals.
Described electroencephalogramsignal signal analyzing module is the digital brain electrical signal being received electroencephalogramsignal signal acquisition module output by USB interface or serial line interface RS-232, for analyzing the brain wave rhythm change of sensorimotor cortex district.
Described pattern recognition module carries out digital filtering process, feature extraction and pattern discrimination to the EEG signals that electroencephalogramsignal signal analyzing module exports.
Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, this module can run in electroencephalogramsignal signal analyzing module in a software form, also the device of a software and hardware integration can be become separately, the anti-release of EEG signals prosodic feature that this module can export according to electroencephalogramsignal signal analyzing module is that brain causes the optimization model of accidental resonance by outside stimulus, and refutation process can be realized by the method such as fuzzy reasoning, Bayesian decision.
Described human-computer interaction module is by the Composition of contents of computer display and upper display thereof, and it is used to refer to user and completes the motion of right-hand man's phenomenon, and presents feedback result with icon etc. with the mark of indicative character.
Described feedback regulation module by sound generation unit, transcutaneous electrostimulation and vibratory stimulation etc. one of them noise energy driving source and analog line driver form, this module, after the output information receiving accidental resonance inverting module, is various output signal and then the corresponding sensory channel of stimulation user convert information.
Utilize the accidental resonance of said system to strengthen the method for brain-computer interface brain wave rhythm signal, comprise following steps, see Fig. 4:
Step (1), according to 10-20 electrode for encephalograms wearing mode, choice for use person left and right brain district C3, C4 are as posting field (see Fig. 1 (a), dotted line frame in); Potential electrode is laid in posting field, and lay reference electrode in one-sided ear-lobe position, lay ground electrode at Fpz place of head forehead place, the EEG signals then recorded by above-mentioned electrode sends into electroencephalogramsignal signal acquisition module, through amplifying and being sent to electroencephalogramsignal signal analyzing module after analog to digital conversion;
C3, C4 are motor cortex region, scalp both sides for described left and right brain district.
Step (2), by imagination left hand and the motion of the imagination right hand, can there is at left and right brain district C3, C4 the electrical energy of brain change that desynchronization causes; The brain wave rhythm change of electroencephalogramsignal signal analyzing module analysis sensorimotor cortex district, wherein said brain wave rhythm is the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things, specifically
First 2.1 set a sliding time window, electroencephalogramsignal signal analyzing module carries out filtering pre-treatment for the EEG signals in same time window potential electrode, wherein filtering pre-treatment adopts the Laplace transformation method with spa-tial filter properties, as Fig. 1 (b), specifically with the brain electric strength value (E being positioned at centre position electrode
1) the average brain electric strength value that deducts surround electrode obtains the brain electric strength value E ' of centre position electrode
1, namely
this computing can give prominence to the signal intensity that centre position electrode obtains, the interference of filtering ambient signals.
2.2 for a certain potential electrode, brain wave rhythm signal energy after task starting point is quantized, specifically getting first time window rhythm and pace of moving things energy after task starting point is benchmark, then with fixed step size sliding time window, the relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window asking for this potential electrode.
Step (3), build the accidental resonance parameter model of user in advance:
Described accidental resonance parameter model adopts classical Graz-BCI Mental imagery experimental paradigm to build, wherein after determining accidental resonance pattern (i.e. input stimulus source), by your sum function method of multidimensional, change curve matching according to noise figure and brain wave rhythm signal forms the accidental resonance multi-parameters model that fitting function sets up individuation, uses the method asking for extreme value to ask for optimized parameter to its fitting function.
The Graz-BCI Mental imagery experimental paradigm task of described classics is divided into right-hand man's action/imagination right-hand man action, finger movement/imagination finger movement, grasping/imagination to grasp, selected irritating noise source is gaussian shaped profile white noise, frequency band limits is at 0-100 hertz, and concrete operations are:
After selected wherein a kind of accidental resonance pattern (i.e. irritating noise source), by regulating the threshold of feelings measuring method determination noise threshold of size according to psychophysics experiments of noise energy; According to the number percent of noise threshold intensity, this noise is divided into several intensity levels, as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% etc. of threshold intensity.Stochastic inputs noise level value in test process.Then user performs corresponding task and by brain wave acquisition module record brain wave rhythm signal, simultaneously by electroencephalogramsignal signal analyzing module analysis brain wave rhythm signal acquisition noise figure, sets up the change curve of noise figure and brain wave rhythm signal.The optimum noise figure causing the strongest brain wave rhythm signal is obtained by extremum method;
The Classic Experiments normal form of described accidental resonance stimulates with noise for omnidistance term of execution of test.In the present invention in order to the identification problem of thinking idle condition in right-hand man's Mental imagery, the applying mode of random noise is except classical noise applying mode, too increase synchronous accidental resonance pattern simultaneously, its step is that the applying process that noise stimulates is synchronous with tasks carrying, namely stimulating with random noise appears in task, task interval stops applying noise to stimulate, and sees Fig. 3.
The described threshold of feelings measuring method determination noise threshold of size according to psychophysics experiments by regulating noise energy, experiment adopts the interval that " can tolerate " to experimenter from zero, according to step change, each stimulus intensity presents at random, its tolerance value is obtained by the threshold of feelings method of psychophysics experiments, and this tolerance value is noise threshold.
Step (4), two classification task by imagination motion control one dimension cursor movement, imagination motion can cause the brain wave rhythm in above-mentioned C3, C4 brain district to change.There are two class feedbacks in tasks carrying process: a class is visual feedback, carry out imagination motion in order to instruct user and control cursor moving; Another kind of is based on accidental resonance parameter model, and stimulated the online feedback regulated by random external, concrete operations are:
4.1 visual feedback tasks carrying processes:
User watches the visual feedback prompting on human-computer interaction interface attentively, can imagine motion accordingly according to prompting user, controls two classification task of one dimension cursor movement on human-computer interaction interface by comparing the EEG signals energy difference led in C3, C4 brain district;
User is sitting on armed chair, the visual feedback on eye gaze human-computer interaction interface 500.The single experiment time is 8 seconds, and first 2 seconds is relaxation state, and when the 3rd second starts, the briefing represented by different directions arrow appears in screen, and experimenter's start time is that the imagination of 6 seconds is moved, and wherein within latter 5 seconds, has visual feedback.The electrical energy of brain difference of leading by comparing C3 and C4 controls the motion in one dimension of cursor, and the corresponding cursor that controls of imagination left hand motion moves upward, and imagines that right hand motion then controls cursor and moves downward.
The online feedback that 4.2 random stimulus regulate:
From random electro photoluminescence/mechanical vibration as noise source, across sensation path audile noise, through the electro photoluminescence of skin electric current vestibular, choose any one accidental resonance pattern as input stimulus amount, input in the accidental resonance parameter model built in advance, in order to regulate and control the brain wave rhythm of human cortical brain; Wherein said random electro photoluminescence/mechanical vibration are act on inner noise circumstance by sense of touch and sense of hearing sensation path as noise source, across the audile noise of sensation path, and then have influence on cranial nerve maincenter; Described can directly act on human cortical brain through the electro photoluminescence of skin electric current vestibular, sees Fig. 2; Three kinds of above-mentioned irritating noises are gaussian shaped profile white noise, and frequency band limits is at 0-100 hertz.
Described random electro photoluminescence/mechanical vibration are as noise source, and stimulation location is subject hand/foot, and applying method is homonymy and heteropleural two kinds of patterns.Can form accordingly: four kinds of patterns such as hand exercise/imagination motion+homonymy hand random stimulus, hand exercise/imagination motion+offside hand random stimulus, hand exercise/imagination motion+homonymy vola random stimulus, hand exercise/imagination motion+offside vola random stimulus.
Owing to utilizing the accidental resonance effect phenomenon across sensory channel, it is a kind of regulatory pathway by feeling the sensorimotor cortex district brain wave rhythm change caused alternately; And audile noise has larger convenience in brain-computer interface technical process, the audile noise across sensation path is therefore selected to stimulate, experiment employing two kinds of stimulation modes: the first, produce audile noise source by high performance earphone; The second, the music that selection cycle is strong or sound, in contrast condition.Applying mode is: bilateral, one-sided and offside etc.
Through skin electric current vestibular electro photoluminescence 101, described verifies that outer source noise stimulus sequence is to the synchronized regulating action of brain wave rhythm, this kind of method can the change of direct regulation and control encephalomere rule.In experimentation, according to above-mentioned experimental paradigm, use the electrical noise of varying strength to stimulate to sensorimotor cortex district, record its EEG signals, and analyze the regulating action of quantitative electrical noise to brain wave rhythm.
The 4.3 accidental resonance parameter models of corresponding accidental resonance pattern by building in advance, define the fitting function of various noise power value and brain wave rhythm signal intensity curve, accidental resonance inverting module is in conjunction with this fitting function, by the method such as fuzzy reasoning, Bayesian decision, the optimum inverting stimulated is realized to the corresponding relation of noise figure and brain wave rhythm signal, obtains the optimal stimulus parameter of corresponding accidental resonance pattern.
The optimal stimulus parameter of the corresponding accidental resonance pattern that 4.4 steps 4.3 obtain is input to feedback regulation module by accidental resonance inverting module, physical characteristics according to this accidental resonance pattern stimulates user's human body, produce a kind of noise stimulation energy to realize stimulating the feedback of user, and then regulation and control brain accidental resonance occurrence condition, change brain wave rhythm signal, thus reach the object that EEG signals source is strengthened.
Step (5), user are under the feedback regulation obtaining brain power supply signal, according to the EEG signals that electroencephalogramsignal signal acquisition module collects, and obtain brain wave rhythm feature by brain electricity analytical module, linear discriminant analysis (as Fisher linear discriminant analysis) is adopted to obtain recognition result finally by pattern recognition module, output to human-computer interaction interface, described human-computer interaction interface is graphoscope.
Above-described embodiment is not that the present invention is not limited only to above-described embodiment for restriction of the present invention, as long as meet application claims, all belongs to protection scope of the present invention.
Claims (5)
1. the brain-machine interface method utilizing accidental resonance to strengthen EEG signals, be applied to the brain machine interface system of following motorsensory cortex district EEG signals, this system comprises electroencephalogramsignal signal acquisition module, electroencephalogramsignal signal analyzing module, pattern recognition module, accidental resonance inverting module, human-computer interaction module, feedback regulation module; The output terminal of electroencephalogramsignal signal acquisition module is connected with the input end signal of electroencephalogramsignal signal analyzing module; An output terminal of electroencephalogramsignal signal analyzing module is connected with the input end signal of pattern recognition module, and another output terminal is connected with the input end signal of accidental resonance inverting module; The output terminal of pattern recognition module is connected with the input end signal of human-computer interaction module; The output terminal of accidental resonance inverting module is connected with the input end signal of feedback regulation module; The output stimulus signal of feedback regulation module acts on user;
Described electroencephalogramsignal signal acquisition module is by electrode, impedance matching and protection circuit, signal amplifier, low pass and rejection filter, simulating signal turn digital efm signal and control and communication unit etc. form, for gathering EEG signals;
Described electroencephalogramsignal signal analyzing module is the digital brain electrical signal being received electroencephalogramsignal signal acquisition module output by USB interface or serial line interface RS-232, for analyzing the brain wave rhythm change of sensorimotor cortex district;
Described pattern recognition module carries out digital filtering process, feature extraction and pattern discrimination to the EEG signals that electroencephalogramsignal signal analyzing module exports;
Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, and this module for run in electroencephalogramsignal signal analyzing module in a software form, or becomes separately the device of a software and hardware integration; The anti-release of EEG signals prosodic feature that this module can export according to electroencephalogramsignal signal analyzing module is that brain causes the optimization model of accidental resonance by outside stimulus, and refutation process can be realized by the method such as fuzzy reasoning, Bayesian decision;
Described human-computer interaction module is by the Composition of contents of computer display and upper display thereof, and it is used to refer to user and completes the motion of right-hand man's phenomenon, and presents feedback result with the mark with indicative character;
Described feedback regulation module is made up of the noise energy driving source of one of sound generation unit, transcutaneous electrostimulation, vibratory stimulation and analog line driver, this module, after the output information receiving accidental resonance inverting module, is various output signal and then the corresponding sensory channel of stimulation user convert information; It is characterized in that the method comprises the following steps:
Step (1), according to 10-20 electrode for encephalograms wearing mode, choice for use person left and right brain district C3, C4 are as posting field; Potential electrode is laid in posting field, and lay reference electrode in one-sided ear-lobe position, lay ground electrode at Fpz place of head forehead place, the EEG signals then recorded by above-mentioned electrode sends into electroencephalogramsignal signal acquisition module, through amplifying and being sent to electroencephalogramsignal signal analyzing module after analog to digital conversion;
C3, C4 are motor cortex region, scalp both sides for described left and right brain district;
Step (2), by imagination left hand and the motion of the imagination right hand, can there is at left and right brain district C3, C4 the electrical energy of brain change that desynchronization causes; By the change of electroencephalogramsignal signal analyzing module analysis sensorimotor cortex district brain wave rhythm, wherein brain wave rhythm comprises the Mu rhythm and pace of moving things, the Beta rhythm and pace of moving things;
Step (3), build the accidental resonance parameter model of user in advance:
Described accidental resonance parameter model adopts classical Graz-BCI Mental imagery experimental paradigm to build, wherein after determining accidental resonance pattern, by multidimensional matching function method, form according to the brain wave rhythm signal intensity value curve that noise figure and above-mentioned steps 2 obtain and set up the accidental resonance multi-parameters model of individuation, using the method asking for extreme value to ask for optimal stimulus parameter to its fitting function;
Step (4), two classification task by imagination motion control one dimension cursor movement, imagination motion can cause the brain wave rhythm in above-mentioned C3, C4 brain district to change, in task implementation, there are two class feedbacks: a class is visual feedback, carry out imagination motion in order to instruct user and control cursor moving; Another kind of is based on accidental resonance parameter model, is stimulated the online feedback regulated by random external; Concrete operations are:
4.1 visual feedback tasks carrying processes:
User watches the visual feedback prompting on human-computer interaction interface attentively, can imagine motion accordingly according to prompting user, controls two classification task of one dimension cursor movement on human-computer interaction interface by comparing the EEG signals energy difference led in C3, C4 brain district;
The online feedback that 4.2 random stimulus regulate:
From random electro photoluminescence/mechanical vibration as noise source, across sensation path audile noise, through the electro photoluminescence of skin electric current vestibular, choose a kind of accidental resonance pattern as input stimulus amount, input in the accidental resonance parameter model of the identical accidental resonance pattern built in advance, in order to regulate and control the brain wave rhythm of human cortical brain;
4.3 by accidental resonance parameter model, define the fitting function of different noise level value and brain wave rhythm signal intensity curve, accidental resonance inverting module is in conjunction with this fitting function, by the method such as fuzzy reasoning, Bayesian decision, the optimum inverting stimulated is realized to the corresponding relation of noise figure and brain wave rhythm signal, obtains the optimal stimulus parameter of corresponding accidental resonance pattern;
The optimal stimulus parameter of 4.4 corresponding accidental resonance patterns step 4.3 obtained is input to feedback regulation module, physical characteristics according to this accidental resonance pattern stimulates user's human body, make it produce a kind of noise stimulation energy to realize stimulating the feedback of user, and then regulation and control brain accidental resonance occurrence condition, change brain wave rhythm signal, thus reach the object that EEG signals source is strengthened;
Step (5), user are under the feedback regulation obtaining brain power supply signal, according to the EEG signals that electroencephalogramsignal signal acquisition module collects, and obtain brain wave rhythm feature by brain electricity analytical module, adopt linear discriminant analysis to obtain recognition result finally by pattern recognition module, output to human-computer interaction interface.
2. a kind of accidental resonance that utilizes strengthens the brain-machine interface method of EEG signals as claimed in claim 1, it is characterized in that step (2) specifically:
First 2.1 set a sliding time window, electroencephalogramsignal signal analyzing module carries out filtering pre-treatment for the EEG signals in same time window potential electrode, filtering pre-treatment adopts the Laplace transformation method with spa-tial filter properties, specifically deducts the brain electric strength value E ' of the average brain electric strength value acquisition centre position electrode of surround electrode with the brain electric strength value E1 being positioned at centre position electrode
1, namely
this computing can give prominence to the signal intensity that centre position electrode obtains, the interference of filtering ambient signals;
2.2 for a certain potential electrode, brain wave rhythm signal energy after task starting point is quantized, specifically get first time window rhythm and pace of moving things energy after task starting point as benchmark, then with fixed step size sliding time window, the relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window asking for this potential electrode.
3. a kind of accidental resonance that utilizes strengthens the brain-machine interface method of EEG signals as claimed in claim 1, it is characterized in that the Graz-BCI Mental imagery experimental paradigm task of the classics described in step (3) is divided into right-hand man's action/imagination right-hand man action, finger movement/imagination finger movement, grasping/imagination to grasp, selected irritating noise source is gaussian shaped profile white noise, frequency band limits is at 0 ~ 100 hertz, and concrete operations are:
After selected wherein a kind of accidental resonance pattern, by regulating the threshold of feelings measuring method determination noise threshold of size according to psychophysics experiments of noise energy; According to the number percent of noise threshold intensity, this noise is divided into several intensity levels; Stochastic inputs certain noise level value above-mentioned in test process, then user performs corresponding task and by brain wave acquisition module record brain wave rhythm signal, simultaneously by electroencephalogramsignal signal analyzing module analysis brain wave rhythm signal acquisition noise figure, set up the change curve of noise figure and brain wave rhythm signal, and obtained the optimum noise figure causing the strongest brain wave rhythm signal by extremum method.
4. a kind of accidental resonance that utilizes strengthens the brain-machine interface method of EEG signals as claimed in claim 1, it is characterized in that the Classic Experiments normal form of the accidental resonance described in step (3) stimulates with noise for omnidistance term of execution of test; In order to the identification problem of thinking idle condition in right-hand man's Mental imagery, the applying mode of random noise is except classical noise applying mode, too increase synchronous accidental resonance pattern simultaneously, its step is that the applying process that noise stimulates is synchronous with tasks carrying, namely stimulating with random noise appears in task, and task interval stops applying noise to stimulate.
5. a kind of accidental resonance that utilizes strengthens the brain-machine interface method of EEG signals as claimed in claim 1, it is characterized in that the wherein said random electro photoluminescence/mechanical vibration of step (4.2) are act on inner noise circumstance by sense of touch and sense of hearing sensation path as noise source, across the audile noise of sensation path, and then have influence on cranial nerve maincenter; Described can directly act on human cortical brain through the electro photoluminescence of skin electric current vestibular; Above-mentioned three kinds of irritating noises are gaussian shaped profile white noise, and frequency band limits is at 0 ~ 100 hertz.
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