CN104951082B - A kind of brain-machine interface method for strengthening EEG signals using accidental resonance - Google Patents
A kind of brain-machine interface method for strengthening EEG signals using accidental resonance Download PDFInfo
- Publication number
- CN104951082B CN104951082B CN201510405265.4A CN201510405265A CN104951082B CN 104951082 B CN104951082 B CN 104951082B CN 201510405265 A CN201510405265 A CN 201510405265A CN 104951082 B CN104951082 B CN 104951082B
- Authority
- CN
- China
- Prior art keywords
- brain
- module
- noise
- signal
- accidental resonance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The present invention discloses a kind of brain-machine interface method for strengthening EEG signals using accidental resonance.This method utilizes the Stochastic Resonance Phenomenon being had found in human brain perception, the synchronized method of Cortical ECoG is regulated and controled by accidental resonance, attempt to improve the signal to noise ratio of EEG signals from source, reduce subject's otherness and improve the stability of signal source, the new way to an enhancing EEG signals source.The present invention is modeled to the relation of sensorimotor brain area bioelectrical activity rule, sensory perception and random noise, a kind of brain electricity feedback regulation pattern based on classical brain-computer interface technology is built, reaches the purpose for the brain-computer interface technique effect that brain wave rhythm signal of the enhancing based on scalp brain electricity is characterized.
Description
Technical field
The invention belongs to biomedical engineering and medical Instrument field, is related to a kind of brain-machine interface method, more particularly to one
The method of kind accidental resonance enhancing brain-computer interface brain wave rhythm signal.
Background technology
Brain-computer interface technology is not dependent on the information transfer channel of peripheral nerve and muscle, and directly by obtaining brain
Signal realizes the technology with external environment communication.Clinically, the brain wave rhythm signal in sensorimotor cortex area is commonly used as control
Signal source processed, its application may be summarized to be two kinds：It is a kind of to be to aid in disabled person's realization and the communication of external environment, such as artificial limb and wheel
The motion control of chair, various electrical equipment etc. are operated using domestic environment controller；Another kind is for kinesitherapy nerve pathway injury
Personnel provide initiative rehabilitation auxiliary, and this rehabilitation maneuver can not only help self assistant massaging of patient and motion, can also strengthen
The function of corresponding nerve pathway, help to recover the real motion function of limbs.In addition to both typical cases, based on head
The brain machine interface system of skin EEG signals also have the advantages that cost it is low, it is easy to operate, to brain not damaged, be to work as forebrain-machine
One of Main way of interface research.
Due to the complexity of brain so that the brain-computer interface signal source based on scalp EEG signals is faint, and stability
Difference.At present, the brain-computer interface technology based on the independent rhythm of sensorimotor cortex mainly passes through feedback training, introducing pin
Signal transacting and method for classifying modes to property, change experiment model and overcome this to ask the methods of Improving Equipment matches
Topic.Although these methods improve to systematic function, but still have some shortcomings：Such as length of tested training time, or even nothing
Effect；Signal detection is limited to the shortcomings such as brain wave rhythm signal to noise ratio is low.Which greatly limits the process of brain-computer interface technical application.
Stochastic Resonance Phenomenon is a kind of usual upper mechanism that is opposite, eliminating random (noise) of understanding with people.It is present
In nonlinear system, it is typically characterised by, when random perturbation (noise) adds nonlinear system, may occur in which that system exports noise
Than the phenomenon to go up not down.Nervous system is typical nonlinear system, have numerous studies confirm nervous system, sensory perception,
The operative condition of accidental resonance all be present in cognition and cortex synchronization etc..Inspired by these Research Thinkings, the present invention from
The accidental resonance regulation synchronized phenomenon of brain wave rhythm is started with, and is introduced into and is felt the Stochastic Resonance Phenomenon in cognitive process to strengthen phase
The intensity of the brain wave rhythm of brain area is answered, is improved from the direction exploitation for improving EEG signals source with brain wave rhythm signal brain-computer interface control
The technology of the practicality of signal processed.It is vision induced to strengthen that patent [publication No., CN 103970273.A] employs accidental resonance
The response intensity of current potential, reach and lift the precision of existing brain-computer interface and the purpose of efficiency.Evoked ptential is in brain-computer interface technology
In be a kind of important signal source, have anti-interference strong, and user is without the advantages that excessively training.With the spontaneous section of scalp brain electricity
Rule (such as cortical slow potential (SCP) and μ or beta response) is the brain-computer interface technology of signal source independent of the defeated of extra stimulation
Enter, visual fatigue will not be produced, do not have the problem of adaptability, there is broader and flexible use demand.But spontaneous rhythm
Easily it is interfered, generally requires to carry out prolonged training by user to reach the stability of performance.
The content of the invention
The purpose of the present invention is to be directed to brain-computer interface technology of the sensorimotor cortex area brain wave rhythm as signal source, is utilized
The Stochastic Resonance Phenomenon being had found in human brain perception, the synchronized method of Cortical ECoG is regulated and controled by accidental resonance, attempted
Improve the signal to noise ratio of EEG signals from source, reduce subject's otherness and improve the stability of signal source, utilization is non-thread
Stochastic Resonance Theory builds a kind of practical brain electricity regulation and control method in sexual system.
Described Stochastic Resonance Phenomenon is defined as：A kind of machine with people's upper opposite elimination of generally understanding random (noise)
System.It is present in nonlinear system, is typically characterised by, when random perturbation (noise) adds some nonlinear systems, may occur in which
The phenomenon that output signal-to-noise ratio goes up not down.Wide in range definition is：Random perturbation (or noise) is added to be advantageous at system information
Reason or the phenomenon for strengthening target signature.The general principle that the present invention is utilized is using external random noise, by regulation
The information processing capability of raw noise source and then regulation nervous system.
To reach this purpose, the technology contents that the present invention uses are as follows：
The inventive method is applied to the brain machine interface system of following motorsensory cortex area EEG signals, and the system includes brain
It is electrical signal collection module, electroencephalogramsignal signal analyzing module, pattern recognition module, accidental resonance inverting module, human-computer interaction module, anti-
Present adjustment module；The output end of electroencephalogramsignal signal acquisition module is connected with the input end signal of electroencephalogramsignal signal analyzing module；Brain telecommunications
One output end of number analysis module and the input end signal of pattern recognition module connect, and another output end and accidental resonance are anti-
Drill the input end signal connection of module；The output end of pattern recognition module and the input end signal of human-computer interaction module connect；With
The output end of machine resonance inverting module is connected with the input end signal of feedback regulation module；The output of feedback regulation module stimulates letter
Number act on user；
Described electroencephalogramsignal signal acquisition module is by electrode, impedance matching and protection circuit, signal amplifier, low pass and with resistance
Wave filter, analog signal turn the composition such as digital efm signal and control and communication unit, for gathering EEG signals.
Described electroencephalogramsignal signal analyzing module is to receive eeg signal acquisition mould by USB interface or serial line interface RS-232
The digital brain electrical signal of block output, for analyzing the brain wave rhythm change of sensorimotor cortex area.
Described pattern recognition module be the EEG signals of electroencephalogramsignal signal analyzing module output are carried out digital filtering processing,
Feature extraction and pattern discrimination.
Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, and the module can be with
Software form is run in electroencephalogramsignal signal analyzing module, can also individually turn into the device of a software and hardware integration, the module
It is that brain is caused at random by outside stimulus that the EEG signals prosodic feature that can be exported according to electroencephalogramsignal signal analyzing module, which is instead released,
The optimization model of resonance, refutation process can be realized by the methods of fuzzy reasoning, Bayesian decision.
The Composition of contents that described human-computer interaction module is shown by computer display and thereon, it is used to refer to user
Complete the motion of right-hand man phenomenon, and feedback result is presented with the mark with indicative character such as icon.
Described feedback regulation module is by the noise of one of them such as sound generation unit, transcutaneous electrostimulation and vibratory stimulation
Energy-activation source and analog line driver are formed, and the module turns information after the output information of accidental resonance inverting module is received
Turn to various output signals and then stimulate the corresponding sensory channel of user.
The present invention proposes a kind of brain-machine interface method for strengthening EEG signals using accidental resonance based on said system, should
Method comprises the steps of：
Step (1), according to 10-20 electrode for encephalograms wearing modes, select left and right brain area C3, C4 of user as recording areas
Domain；Lay measuring electrode in posting field, and reference electrode is laid in unilateral ear-lobe opening position, at the forehead of head at Fpz
Ground electrode is laid, the EEG signals for then measuring above-mentioned electrode are sent into electroencephalogramsignal signal acquisition module, amplified and analog-to-digital conversion
After be sent to electroencephalogramsignal signal analyzing module；
Described left and right brain area C3, C4 is scalp both sides motor cortex region.
Step (2), by imagine left hand and the imagination right hand move, desynchronization can occur in left and right brain area C3, C4 and cause
Electrical energy of brain change；Electroencephalogramsignal signal analyzing module analysis sensorimotor cortex area brain wave rhythm change, wherein described brain electricity
The rhythm and pace of moving things is the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things, is specifically：
2.1 set a sliding time window first, and electroencephalogramsignal signal analyzing module is directed to same time window measuring electrode
On EEG signals be filtered pre-treatment, wherein filtering pre-treatment use with spa-tial filter properties Laplace transformation method, tool
Body is the brain electric strength value (E with centrally located electrode1) subtract electrode around average brain electric strength value obtain interposition
Put the brain electric strength value E ' of electrode1, i.e.,This computing can protrude centre position
The signal intensity that electrode obtains, filters out the interference of ambient signals.
2.2 are directed to a certain measuring electrode, and the brain wave rhythm signal energy after task starting point is quantified, and specifically take and appoint
First time window rhythm and pace of moving things energy after starting point of being engaged in, then with fixed step size sliding time window, asks for the survey as benchmark
Measure the relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window of electrode.
The accidental resonance parameter model of step (3), in advance structure user：
Described accidental resonance parameter model is using classical Graz-BCI Mental imageries experimental paradigm structure, wherein true
After determining accidental resonance pattern (i.e. input stimulus source), function method is fitted by multidimensional, according to institute in noise figure and above-mentioned steps 2.2
The changing value curve matching for the brain wave rhythm signal for stating to obtain forms and establishes the accidental resonance multi-parameters model of individuation, uses
The method for asking for extreme value asks for optimal stimulus parameter to its fitting function.
It is manual that described classical Graz-BCI Mental imagery experimental paradigm tasks are divided into right-hand man's action/imagination or so
Work, finger movement/imagination finger movement, grasping/imagination grasping etc., selected irritating noise source is Gaussian shaped profile white noise,
Frequency band is limited in 0-100 hertz, and concrete operations are：
After selected one of which accidental resonance pattern (i.e. irritating noise source), by adjust the size of noise energy according to
The threshold of feelings measuring method of psychophysics experiments determines noise threshold；According to the percentage of noise threshold intensity, this is made an uproar
Sound is divided into several intensity levels；Some above-mentioned noise level value of stochastic inputs in test process, then user's execution is corresponding
Task simultaneously records brain wave rhythm signal by brain wave acquisition module, while is believed by electroencephalogramsignal signal analyzing module analysis brain wave rhythm
Number noise figure is obtained, establish the change curve of noise figure and brain wave rhythm signal, and obtained by extremum method and cause most strong brain electric
The optimal noise figure of circadian signal；
There is noise in the Classic Experiments normal form of described accidental resonance, in the present invention for left and right all the time during being task
The identification problem of thinking idle condition in the hands movement imagination, the applying mode of random noise allow noise to exist all the time except classics
During various tasks, while synchronous accidental resonance pattern is also increased, its step is the application process and tasks carrying of noise
It is synchronous.
Step (4), two classification tasks by imagining the one-dimensional cursor movement of motion control, imagination motion can cause above-mentioned
, two classes feedback in task implementation procedure be present in the brain wave rhythm change of C3, C4 brain area：One kind is visual feedback, to instruct
User carries out imagining motion and controls cursor movement；Another kind of is based on accidental resonance parameter model, is stimulated by random external
The online feedback of regulation；Concrete operations are：
4.1 visual feedback tasks carrying processes：
The visual feedback prompting that user is watched attentively on human-computer interaction interface, can be imagined accordingly according to prompting user
Motion, two of one-dimensional cursor movement on human-computer interaction interface is controlled by the EEG signals energy difference for comparing C3, C4 brain area lead
Classification task；
The online feedback of 4.2 random stimulus regulation：
From random electro photoluminescence/mechanical oscillation as noise source, across audile noise, the percutaneous electric current vestibular electricity thorn for feeling path
A kind of accidental resonance pattern is chosen in swashing as input stimulus amount, input to the identical accidental resonance pattern built in advance with
In machine resonance parameter model, to regulate and control the brain wave rhythm of human cortical brain；Wherein described random electro photoluminescence/mechanical oscillation conduct
Noise source, across feeling that the audile noise of path is to feel path effect in the noise circumstance of inside by tactile and the sense of hearing, and then
Have influence on cranial nerve maincenter；Described percutaneous electric current vestibular electro photoluminescence can be done directly on human cortical brain；Three kinds of above-mentioned stimulations
Noise is Gaussian shaped profile white noise, and frequency band is limited in 0~100 hertz.
4.3, by accidental resonance parameter model, form different noise level values and brain wave rhythm signal intensity curve
Fitting function, accidental resonance inverting module combine the fitting function, the corresponding relation of noise figure and brain wave rhythm signal are passed through
The methods of fuzzy reasoning, Bayesian decision, realizes the inverting of optimal stimulation, obtains the optimal thorn of corresponding accidental resonance pattern
Swash parameter.
The optimal stimulus parameter of the 4.4 corresponding accidental resonance patterns for obtaining step 4.3 is input to feedback regulation module,
User's human body is stimulated according to the physical characteristic of the accidental resonance pattern, a kind of noise stimulation energy is produced and realizes to using
The feedback of person stimulates, and then regulates and controls brain accidental resonance occurrence condition, changes brain wave rhythm signal, so as to reach to EEG signals
The purpose of source enhancing.
Step (5), user collect in the case where obtaining the feedback regulation of brain power supply signal according to electroencephalogramsignal signal acquisition module
EEG signals, and brain wave rhythm feature is obtained by brain electricity analytical module, finally by pattern recognition module using linearly sentencing
Other method (such as Fisher linear discriminant analysis) obtains recognition result, is output to human-computer interaction interface, and the human-computer interaction interface is meter
Calculation machine display.
The beneficial effects of the invention are as follows：
1st, the present invention is provided a kind of technical method for enhancing brain source signal, is using exogenous signals regulation and control brain wave rhythm
Brain-computer interface technology provides the signal of high s/n ratio.
2nd, the present invention can be the brain machine using the brain wave rhythm in scalp motorsensory cortex area as BCI system control signals
Interface user, the time of feedback training is reduced, improve the applicability of user.
3rd, the invention provides the applying method of a variety of external random noises, by the use of random electro photoluminescence/mechanical oscillation as
Noise source, the interaction of across sensory channel sensation and percutaneous source of the electric current vestibular stimulation as outside stimulus.
4th, the invention provides a kind of determination method of offline accidental resonance adjustment parameter, further, there is provided be based on
The online brain-computer interface technology of accidental resonance regulation.
5th, the present invention realizes offline or online brain wave rhythm brain-computer interface control technology simple, that stability is high, can expand
Similar brain-computer interface technology applies crowd's scope.
Brief description of the drawings
Fig. 1 is motorsensory cortex area schematic diagram, and wherein a is that electrode for encephalograms lays schematic diagram, and b is that brain electricity space filtering shows
It is intended to；
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 of the brain wave rhythm regulation based on accidental resonance.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the present invention is described in further detail.
The inventive method is applied to the brain machine interface system of following motorsensory cortex area EEG signals, and the system includes brain
It is electrical signal collection module, electroencephalogramsignal signal analyzing module, pattern recognition module, accidental resonance inverting module, human-computer interaction module, anti-
Present adjustment module；The output end of electroencephalogramsignal signal acquisition module is connected with the input end signal of electroencephalogramsignal signal analyzing module；Brain telecommunications
One output end of number analysis module and the input end signal of pattern recognition module connect, and another output end and accidental resonance are anti-
Drill the input end signal connection of module；The output end of pattern recognition module and the input end signal of human-computer interaction module connect；With
The output end of machine resonance inverting module is connected with the input end signal of feedback regulation module；The output of feedback regulation module stimulates letter
Number act on user；
Described electroencephalogramsignal signal acquisition module is by electrode, impedance matching and protection circuit, signal amplifier, low pass and with resistance
Wave filter, analog signal turn the composition such as digital efm signal and control and communication unit, for gathering EEG signals.
Described electroencephalogramsignal signal analyzing module is to receive eeg signal acquisition mould by USB interface or serial line interface RS-232
The digital brain electrical signal of block output, for analyzing the brain wave rhythm change of sensorimotor cortex area.
Described pattern recognition module be the EEG signals of electroencephalogramsignal signal analyzing module output are carried out digital filtering processing,
Feature extraction and pattern discrimination.
Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, and the module can be with
Software form is run in electroencephalogramsignal signal analyzing module, can also individually turn into the device of a software and hardware integration, the module
It is that brain is caused at random by outside stimulus that the EEG signals prosodic feature that can be exported according to electroencephalogramsignal signal analyzing module, which is instead released,
The optimization model of resonance, refutation process can be realized by the methods of fuzzy reasoning, Bayesian decision.
The Composition of contents that described human-computer interaction module is shown by computer display and thereon, it is used to refer to user
Complete the motion of right-hand man phenomenon, and feedback result is presented with the mark with indicative character such as icon.
Described feedback regulation module is by the noise of one of them such as sound generation unit, transcutaneous electrostimulation and vibratory stimulation
Energy-activation source and analog line driver are formed, and the module turns information after the output information of accidental resonance inverting module is received
Turn to various output signals and then stimulate the corresponding sensory channel of user.
Strengthen the method for brain-computer interface brain wave rhythm signal using the accidental resonance of said system, comprise the steps of, see
Fig. 4：
Step (1), according to 10-20 electrode for encephalograms wearing modes, select left and right brain area C3, C4 of user as recording areas
Domain (see Fig. 1 (a), in dotted line frame)；Measuring electrode is laid in posting field, and is laid in unilateral ear-lobe opening position with reference to electricity
Pole, ground electrode is being laid at the forehead of head at Fpz, the EEG signals for then measuring above-mentioned electrode are sent into eeg signal acquisition mould
Block, electroencephalogramsignal signal analyzing module is sent to after amplified and analog-to-digital conversion；
Described left and right brain area C3, C4 is scalp both sides motor cortex region.
Step (2), by imagine left hand and the imagination right hand move, desynchronization can occur in left and right brain area C3, C4 and cause
Electrical energy of brain change；Electroencephalogramsignal signal analyzing module analysis sensorimotor cortex area brain wave rhythm change, wherein described brain electricity
The rhythm and pace of moving things is the Mu rhythm and pace of moving things and the Beta rhythm and pace of moving things, is specifically
2.1 set a sliding time window first, and electroencephalogramsignal signal analyzing module is directed to same time window measuring electrode
On EEG signals be filtered pre-treatment, wherein filtering pre-treatment use with spa-tial filter properties Laplace transformation method, such as
Fig. 1 (b), specifically with the brain electric strength value (E of centrally located electrode1) the average brain electric strength value that subtracts electrode around obtains
Obtain the brain electric strength value E ' of centre position electrode1, i.e.,This computing can protrude
The signal intensity that centre position electrode obtains, filters out the interference of ambient signals.
2.2 are directed to a certain measuring electrode, and the brain wave rhythm signal energy after task starting point is quantified, and specifically take and appoint
On the basis of first time window rhythm and pace of moving things energy after starting point of being engaged in, then with fixed step size sliding time window, the measurement is asked for
The relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window of electrode.
The accidental resonance parameter model of step (3), in advance structure user：
Described accidental resonance parameter model is using classical Graz-BCI Mental imageries experimental paradigm structure, wherein true
After determining accidental resonance pattern (i.e. input stimulus source), by multidimensional you and function method, according to noise figure and brain wave rhythm signal
Change curve fitting forms fitting function and establishes the accidental resonance multi-parameters model of individuation, using asking for the method for extreme value to it
Fitting function asks for optimized parameter.
It is manual that described classical Graz-BCI Mental imagery experimental paradigm tasks are divided into right-hand man's action/imagination or so
Work, finger movement/imagination finger movement, grasping/imagination grasping etc., selected irritating noise source is Gaussian shaped profile white noise,
Frequency band is limited in 0-100 hertz, and concrete operations are：
After selected one of which accidental resonance pattern (i.e. irritating noise source), by adjust the size of noise energy according to
The threshold of feelings measuring method of psychophysics experiments determines noise threshold；According to the percentage of noise threshold intensity, this is made an uproar
Sound is divided into several intensity levels, and such as the 10% of threshold intensity, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%
Deng.One noise level value of stochastic inputs in test process.Then user performs corresponding task and passes through brain wave acquisition mould
Block records brain wave rhythm signal, while obtains noise figure by electroencephalogramsignal signal analyzing module analysis brain wave rhythm signal, and foundation is made an uproar
Sound value and the change curve of brain wave rhythm signal.The optimal noise figure for causing most strong brain wave rhythm signal is obtained by extremum method；
The Classic Experiments normal form of described accidental resonance is whole during being performed for experiment to stimulate with noise.In the present invention
For the identification problem of thinking idle condition in right-hand man's Mental imagery, the applying mode of random noise applies except classical noise
Mode, while synchronous accidental resonance pattern is also increased, its step is that the application process that noise stimulates is synchronous with tasks carrying, i.e.,
Stimulating with random noise occurs in task, and the phase, which stops application noise, between task stimulates, and sees Fig. 3.
The described size by adjusting noise energy determines according to the threshold of feelings measuring method of psychophysics experiments
Noise threshold, experiment is using from zero to the section of subject " tolerable ", and according to step change, each stimulus intensity is at random
Existing, its tolerance value is obtained by the threshold of feelings method of psychophysics experiments, and the tolerance value is noise threshold.
Step (4), two classification tasks by imagining the one-dimensional cursor movement of motion control, imagination motion can cause above-mentioned
The brain wave rhythm change of C3, C4 brain area.Two classes during tasks carrying be present to feed back：One kind is visual feedback, to instruct to make
User carries out imagining motion and controls cursor movement；Another kind of is based on accidental resonance parameter model, is stimulated and adjusted by random external
The online feedback of section, concrete operations are：
4.1 visual feedback tasks carrying processes：
The visual feedback prompting that user is watched attentively on human-computer interaction interface, can be imagined accordingly according to prompting user
Motion, two of one-dimensional cursor movement on human-computer interaction interface is controlled by the EEG signals energy difference for comparing C3, C4 brain area lead
Classification task；
User is sitting on armed chair, the visual feedback on eye gaze human-computer interaction interface 500.Single experiment
Time is 8 seconds, and first 2 seconds are relaxation state, and the briefing represented by different directions arrow occurs in screen when the 3rd second starts, by
The imagination that examination person's time started is 6 seconds is moved, wherein rear have visual feedback in 5 seconds.By the electrical energy of brain for comparing C3 and C4 leads
Difference controls the motion in one dimension of cursor, and the corresponding control cursor of imagination left hand motion moves upwards, and imagine right hand motion and then control
Cursor moves downward.
The online feedback of 4.2 random stimulus regulation：
From random electro photoluminescence/mechanical oscillation as noise source, across audile noise, the percutaneous electric current vestibular electricity thorn for feeling path
Any one accidental resonance pattern is chosen in swashing as input stimulus amount, is inputted to the accidental resonance parameter model built in advance
In, to regulate and control the brain wave rhythm of human cortical brain；Wherein described random electro photoluminescence/mechanical oscillation as noise source, across sensation
The audile noise of path is to feel path effect in the noise circumstance of inside by tactile and the sense of hearing, and then is had influence in cranial nerve
Pivot；Described percutaneous electric current vestibular electro photoluminescence can be done directly on human cortical brain, see Fig. 2；Three kinds of above-mentioned irritating noises are height
This type is distributed white noise, and frequency band is limited in 0-100 hertz.
Described random electro photoluminescence/mechanical oscillation are as noise source, and stimulation location is subject's hand/foot, application side
Method is homonymy and heteropleural both of which.It can form accordingly：Hand exercise/imagination motion+homonymy hand random stimulus, hand fortune
Dynamic/imagination motion+offside hand random stimulus, hand exercise/imagination motion+homonymy vola random stimulus, the hand exercise/imagination
Four kinds of patterns such as motion+offside vola random stimulus.
Due to being by feeling sensorimotor cortex caused by interaction using the accidental resonance effect phenomenon across sensory channel
A kind of regulatory pathway of area's brain wave rhythm change；And audile noise has bigger convenience in brain-computer interface technical process,
Therefore selection stimulates across the audile noise for feeling path, and experiment uses two kinds of stimulation modes：Firstth, produced by high performance earphone
Audile noise source；Secondth, the strong music of selection cycle or sound, as collating condition.Applying mode is：Bilateral, one side and
Offside etc..
Described percutaneous electric current vestibular electro photoluminescence 101 verifies outer source noise stimulus sequence to the synchronized regulation of brain wave rhythm
Effect, this kind of method are capable of the change of direct regulation and control encephalomere rule.In experimentation, according to above-mentioned experimental paradigm, to feeling to transport
Dynamic cortical area is stimulated using the electrical noise of varying strength, records its EEG signals, and analyzes quantitative electrical noise to brain wave rhythm
Adjustment effect.
4.3, by the accidental resonance parameter model of the corresponding accidental resonance pattern built in advance, form various noises
The fitting function of energy value and brain wave rhythm signal intensity curve, accidental resonance inverting module combines the fitting function, to noise
The corresponding relation of value and brain wave rhythm signal by realizing the inverting of optimal stimulation the methods of fuzzy reasoning, Bayesian decision,
Obtain the optimal stimulus parameter of corresponding accidental resonance pattern.
The optimal stimulus parameter for the corresponding accidental resonance pattern that 4.4 steps 4.3 obtain passes through accidental resonance inverting module
Feedback regulation module is input to, user's human body is stimulated according to the physical characteristic of the accidental resonance pattern, one kind is produced and makes an uproar
Sound stimulat energy, which is realized, to stimulate the feedback of user, and then regulates and controls brain accidental resonance occurrence condition, changes brain wave rhythm letter
Number, so as to reach the purpose to the enhancing of EEG signals source.
Step (5), user collect in the case where obtaining the feedback regulation of brain power supply signal according to electroencephalogramsignal signal acquisition module
EEG signals, and brain wave rhythm feature is obtained by brain electricity analytical module, finally by pattern recognition module using linearly sentencing
Other method (such as Fisher linear discriminant analysis) obtains recognition result, is output to human-computer interaction interface, and the human-computer interaction interface is meter
Calculation machine display.
It is that the present invention is not limited only to above-described embodiment, as long as meeting for limitation of the invention that above-described embodiment, which is not,
Application claims, belong to protection scope of the present invention.
Claims (5)
- A kind of 1. brain-machine interface method for strengthening EEG signals using accidental resonance, applied to following motorsensory cortex area brain electricity The brain machine interface system of signal, the system include electroencephalogramsignal signal acquisition module, electroencephalogramsignal signal analyzing module, pattern recognition module, Accidental resonance inverting module, human-computer interaction module, feedback regulation module；The output end and EEG signals of electroencephalogramsignal signal acquisition module The input end signal connection of analysis module；One output end of electroencephalogramsignal signal analyzing module and the input of pattern recognition module are believed Number connection, another output end is connected with the input end signal of accidental resonance inverting module；The output end of pattern recognition module with The input end signal connection of human-computer interaction module；The output end of accidental resonance inverting module is believed with the input of feedback regulation module Number connection；The output stimulus signal of feedback regulation module acts on user；Described electroencephalogramsignal signal acquisition module includes electrode, impedance matching and protection circuit, signal amplifier, low pass and filtered with resistance Ripple device, analog signal turn digital efm signal and control and communication unit, for gathering EEG signals；Described electroencephalogramsignal signal analyzing module is defeated by USB interface or serial line interface RS-232 reception electroencephalogramsignal signal acquisition modules The digital brain electrical signal gone out, for analyzing the brain wave rhythm change of sensorimotor cortex area；Described pattern recognition module is to carry out digital filtering processing, feature to the EEG signals of electroencephalogramsignal signal analyzing module output Extraction and pattern discrimination；Described accidental resonance inverting module is the intelligent inference algoritic module based on Stochastic Resonance Theory, and the module is with software Form is run in electroencephalogramsignal signal analyzing module, or individually turns into the device of a software and hardware integration；The module being capable of basis It is that brain causes accidental resonance most by outside stimulus that the EEG signals prosodic feature of electroencephalogramsignal signal analyzing module output, which is instead released, Excellent pattern, refutation process can be realized by fuzzy reasoning, Bayesian decision method；The Composition of contents that described human-computer interaction module is shown by computer display and thereon, it is used to refer to user's completion Right-hand man's phenomenon is moved, and feedback result is presented with mark with indicative character；Described feedback regulation module by one of sound generation unit, transcutaneous electrostimulation, vibratory stimulation noise energy driving source Formed with analog line driver, the module is converted into information various defeated after the output information of accidental resonance inverting module is received Go out signal and then stimulate the corresponding sensory channel of user；It is characterized in that this method comprises the following steps：Step (1), according to 10-20 electrode for encephalograms wearing modes, select left and right brain area C3, C4 of user as posting field； Measuring electrode is laid in posting field, and reference electrode is laid in unilateral ear-lobe opening position, is laying ground at Fpz at the forehead of head Electrode, the EEG signals for then measuring above-mentioned electrode are sent into electroencephalogramsignal signal acquisition module, are sent to after amplified and analog-to-digital conversion Electroencephalogramsignal signal analyzing module；Described left and right brain area C3, C4 is scalp both sides motor cortex region；Step (2), by imagine left hand and the imagination right hand move, brain caused by desynchronization can occur in left and right brain area C3, C4 Electric flux changes；Changed by electroencephalogramsignal signal analyzing module analysis sensorimotor cortex area brain wave rhythm, wherein brain wave rhythm bag Include the Mu rhythm and pace of moving things, the Beta rhythm and pace of moving things；The accidental resonance parameter model of step (3), in advance structure user：Described accidental resonance parameter model using classical Graz-BCI Mental imageries experimental paradigm structure, wherein it is determined that with After machine resonance mode, function method is fitted by multidimensional, the brain wave rhythm signal intensity obtained according to noise figure and above-mentioned steps (2) Value curve matching forms and establishes the accidental resonance multi-parameters model of individuation, using asking for the method for extreme value to its fitting function Ask for optimal stimulus parameter；Step (4), two classification tasks by imagining the one-dimensional cursor movement of motion control, imagination motion can cause above-mentioned C3, C4 , two classes feedback in task implementation procedure be present in the brain wave rhythm change of brain area：One kind is visual feedback, to instruct user Carry out imagining motion and control cursor movement；Another kind of is based on accidental resonance parameter model, and regulation is stimulated by random external Online feedback；Concrete operations are：4.1 visual feedback tasks carrying processes：The visual feedback prompting that user is watched attentively on human-computer interaction interface, corresponding imagination fortune can be carried out according to prompting user Dynamic, the EEG signals energy difference by comparing C3, C4 brain area lead controls two points of one-dimensional cursor movement on human-computer interaction interface Generic task；The online feedback of 4.2 random stimulus regulation：From random electro photoluminescence/mechanical oscillation as noise source, across in the audile noise of sensation path, percutaneous electric current vestibular electro photoluminescence A kind of accidental resonance pattern is chosen as input stimulus amount, inputs to the random of the identical accidental resonance pattern built in advance and is total to Shake in parameter model, to regulate and control the brain wave rhythm of human cortical brain；4.3, by accidental resonance parameter model, form the fitting of different noise level values and brain wave rhythm signal intensity curve Function, accidental resonance inverting module combine the fitting function, the corresponding relation of noise figure and brain wave rhythm signal are passed through fuzzy Reasoning, Bayesian decision method realize the inverting of optimal stimulation, obtain the optimal stimulus parameter of corresponding accidental resonance pattern；The optimal stimulus parameter of the 4.4 corresponding accidental resonance patterns for obtaining step 4.3 is input to feedback regulation module, according to The physical characteristic of the accidental resonance pattern stimulates user's human body, it is produced a kind of noise stimulation energy and realizes to using The feedback of person stimulates, and then regulates and controls brain accidental resonance occurrence condition, changes brain wave rhythm signal, so as to reach to EEG signals The purpose of source enhancing；Step (5), user are in the case where obtaining the feedback regulation of brain power supply signal, the brain that is collected according to electroencephalogramsignal signal acquisition module Electric signal, and brain wave rhythm feature is obtained by brain electricity analytical module, use linear discriminant analysis finally by pattern recognition module Recognition result is obtained, is output to human-computer interaction interface.
- A kind of 2. brain-machine interface method for strengthening EEG signals using accidental resonance as claimed in claim 1, it is characterised in that Step (2) is specifically：2.1 set a sliding time window first, and electroencephalogramsignal signal analyzing module is in same time window measuring electrode EEG signals are filtered pre-treatment, and filtering pre-treatment uses the Laplace transformation method with spa-tial filter properties, specifically with position In the brain electric strength value E of centre position electrode1The average brain electric strength value for subtracting electrode around obtains the brain of centre position electrode Electric strength value E '1, i.e.,It is strong that this computing protrudes the signal that centre position electrode obtains Degree, filters out the interference of ambient signals；2.2 are directed to a certain measuring electrode, and the brain wave rhythm signal energy after task starting point is quantified, and specifically take task to rise First time window rhythm and pace of moving things energy after point, then with fixed step size sliding time window, asks for measurement electricity as benchmark The relative value of rhythm and pace of moving things energy and benchmark rhythm and pace of moving things energy in each time window of pole.
- A kind of 3. brain-machine interface method for strengthening EEG signals using accidental resonance as claimed in claim 1, it is characterised in that Classical Graz-BCI Mental imagery experimental paradigm tasks described in step (3) be divided into right-hand man act/imagine right-hand man action, Finger movement/imagination finger movement, grasping/imagination grasp, and selected irritating noise source is Gaussian shaped profile white noise, and frequency band limits At 0~100 hertz, concrete operations are system：After selected one of which accidental resonance pattern, by adjusting the size of noise energy according to the sense of psychophysics experiments Feel that threshold measurement method determines noise threshold；According to the percentage of noise threshold intensity, the noise is divided into several intensity levels； Some above-mentioned noise level value of stochastic inputs in test process, then user perform corresponding task and pass through brain wave acquisition mould Block records brain wave rhythm signal, while obtains noise figure by electroencephalogramsignal signal analyzing module analysis brain wave rhythm signal, and foundation is made an uproar Sound value and the change curve of brain wave rhythm signal, and the optimal noise for causing most strong brain wave rhythm signal is obtained by extremum method Value.
- A kind of 4. brain-machine interface method for strengthening EEG signals using accidental resonance as claimed in claim 1, it is characterised in that The Classic Experiments normal form of accidental resonance described in step (3) is whole during being performed for experiment to stimulate with noise；For right-hand man The identification problem of thinking idle condition in Mental imagery, the applying mode of random noise is except classical noise applying mode, simultaneously Synchronous accidental resonance pattern is also increased, its step is that the application process that noise stimulates is synchronous with tasks carrying, i.e., task occurs Stimulated with random noise, the phase, which stops application noise, between task stimulates.
- A kind of 5. brain-machine interface method for strengthening EEG signals using accidental resonance as claimed in claim 1, it is characterised in that The wherein described random electro photoluminescence/mechanical oscillation of step (4.2) as noise source, across feel path audile noise be by touch Feel and the sense of hearing feels path effect in the noise circumstance of inside, and then have influence on cranial nerve maincenter；Described percutaneous electric current vestibular Electro photoluminescence can be done directly on human cortical brain；Above-mentioned three kinds of irritating noises are Gaussian shaped profile white noise, frequency band is limited in 0~ 100 hertz.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510405265.4A CN104951082B (en) | 2015-07-09 | 2015-07-09 | A kind of brain-machine interface method for strengthening EEG signals using accidental resonance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510405265.4A CN104951082B (en) | 2015-07-09 | 2015-07-09 | A kind of brain-machine interface method for strengthening EEG signals using accidental resonance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104951082A CN104951082A (en) | 2015-09-30 |
| CN104951082B true CN104951082B (en) | 2018-01-12 |
Family
ID=54165779
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510405265.4A Active CN104951082B (en) | 2015-07-09 | 2015-07-09 | A kind of brain-machine interface method for strengthening EEG signals using accidental resonance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104951082B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105727442B (en) * | 2015-12-16 | 2018-11-06 | 深圳先进技术研究院 | The brain control functional electric stimulation system of closed loop |
| CN106022256B (en) * | 2016-05-18 | 2019-03-05 | 大连理工大学 | A kind of parameter optimization method of brain machine interface system decision model |
| CN106095086B (en) * | 2016-06-06 | 2019-07-12 | 深圳先进技术研究院 | A kind of Mental imagery brain-computer interface control method based on noninvasive electro photoluminescence |
| WO2020027904A1 (en) * | 2018-07-31 | 2020-02-06 | Hrl Laboratories, Llc | Enhanced brain-machine interfaces with neuromodulation |
| CN109656365B (en) | 2018-12-19 | 2021-03-30 | 东南大学 | Brain-computer interface method and system based on real-time closed-loop vibration stimulation enhancement |
| CN111678698B (en) * | 2020-06-17 | 2022-03-04 | 沈阳建筑大学 | Rolling bearing fault detection method based on sound and vibration signal fusion |
| CN112617863B (en) * | 2020-12-30 | 2023-01-24 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Hybrid online computer-computer interface method for identifying lateral direction of left and right foot movement intention |
| CN113180706B (en) * | 2021-04-19 | 2023-08-15 | 西安交通大学 | FHN stochastic resonance-based SSVEP characteristic frequency extraction method |
| CN117617995B (en) * | 2024-01-26 | 2024-04-05 | 小舟科技有限公司 | Method for collecting and identifying brain-computer interface key brain region code and computer equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101236406A (en) * | 2008-02-04 | 2008-08-06 | 中国计量学院 | Random resonance feedback control method |
| CN102068246A (en) * | 2010-12-30 | 2011-05-25 | 上海电机学院 | Method for enhancing toughness of weak evoked potential (EP) signal |
| CN102499675A (en) * | 2011-10-27 | 2012-06-20 | 杭州电子科技大学 | Feedback system random resonance intensification method of electro-corticogram signal |
| JP2014071825A (en) * | 2012-10-01 | 2014-04-21 | Toyota Motor Corp | Brain machine interface |
| CN103970273A (en) * | 2014-05-09 | 2014-08-06 | 西安交通大学 | Steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement |
-
2015
- 2015-07-09 CN CN201510405265.4A patent/CN104951082B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101236406A (en) * | 2008-02-04 | 2008-08-06 | 中国计量学院 | Random resonance feedback control method |
| CN102068246A (en) * | 2010-12-30 | 2011-05-25 | 上海电机学院 | Method for enhancing toughness of weak evoked potential (EP) signal |
| CN102499675A (en) * | 2011-10-27 | 2012-06-20 | 杭州电子科技大学 | Feedback system random resonance intensification method of electro-corticogram signal |
| JP2014071825A (en) * | 2012-10-01 | 2014-04-21 | Toyota Motor Corp | Brain machine interface |
| CN103970273A (en) * | 2014-05-09 | 2014-08-06 | 西安交通大学 | Steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104951082A (en) | 2015-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104951082B (en) | A kind of brain-machine interface method for strengthening EEG signals using accidental resonance | |
| CN107315478B (en) | A kind of Mental imagery upper limb intelligent rehabilitation robot system and its training method | |
| Zhao et al. | SSVEP-based brain–computer interface controlled functional electrical stimulation system for upper extremity rehabilitation | |
| CN102886102B (en) | Mirror movement neuromodulation system | |
| CN109925582B (en) | Dual-mode brain-computer interactive motor nerve feedback training device and method | |
| KR101229244B1 (en) | Rehabilitation training system with functional electrical stimulation based on steady-state visually evoked potentials | |
| CN105853140B (en) | The brain control lower limb master of view-based access control model exercise induced passively cooperates with rehabilitation training system | |
| CN104978035B (en) | Brain machine interface system and its implementation based on body-sensing electric stimulus inducing P300 | |
| Gordleeva et al. | Exoskeleton control system based on motor-imaginary brain–computer interface | |
| CN105411580B (en) | A kind of brain control wheelchair system based on tactile auditory evoked potential | |
| Yu et al. | EEG-based brain-controlled lower extremity exoskeleton rehabilitation robot | |
| CN110993056A (en) | Hybrid active rehabilitation method and device based on mirror image neurons and brain-computer interface | |
| CN107510555B (en) | Wheelchair electroencephalogram control device and control method | |
| CN107562191A (en) | The online brain-machine interface method of fine Imaginary Movement based on composite character | |
| CN100525854C (en) | Intelligent paralytic patient recovering aid system | |
| CN112617863B (en) | Hybrid online computer-computer interface method for identifying lateral direction of left and right foot movement intention | |
| CN111481799A (en) | Brain wave closed-loop control equipment | |
| CN110908506B (en) | Bionic intelligent algorithm-driven active and passive integrated rehabilitation method, device, storage medium and equipment | |
| CN109125921A (en) | A kind of pulse acupuncture and moxibustion therapeutic apparatus based on evoked brain potential signal | |
| Missiroli et al. | Haptic stimulation for improving training of a motor imagery BCI developed for a hand-exoskeleton in rehabilitation | |
| Li et al. | Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion | |
| Choi et al. | A hybrid BCI-controlled FES system for hand-wrist motor function | |
| Qi et al. | Lower limb rehabilitation exoskeleton control based on SSVEP-BCI | |
| Virdi et al. | Home automation control system implementation using SSVEP based brain computer interface | |
| Yao et al. | Mechanical vibrotactile stimulation effect in motor imagery based brain-computer interface |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |