CN109620219A - A kind of attention rehabilitation training and appraisal procedure based on spectrum entropy - Google Patents
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Abstract
The present invention relates to a kind of attention rehabilitation trainings and appraisal procedure based on spectrum entropy, including S1: acquiring rehabilitation training object for the brain wave EEG related potential of simple phonologic attention task, and pre-process to EEG data；S2: the power spectral density of brain wave data after pretreatment is calculated；S3: spectrum entropy is calculated；S4: support vector machines is utilized, the attention EEG signal in rehabilitation training is trained characterized by composing entropy, obtains disaggregated model；S5: the tranquillization state data before acquisition rehabilitation training object training；S6: rehabilitation training is carried out to rehabilitation training object, and real-time grading is carried out to the EEG data in rehabilitation training；S7: being fed back according to real-time grading result, provides corresponding prompt, helps to focus on；S8: the tranquillization state data after acquisition rehabilitation training object training；S9: rehabilitation training effect is judged according to the variation of the spectrum entropy of training front and back tranquillization state data acquisition.
Description
Technical field
The invention belongs to attention rehabilitation technique fields, are related to a kind of based on the attention rehabilitation training for composing entropy and assessment side
Method.
Background technique
Currently, including drug therapy and non-drug therapy for the essential therapeutic arsenals of attention deficit person.With methylphenidate
Central nervous excitation agent for representative is most common drug, rapid-action, significant effect, but since the action time of drug is short,
And its drug side-effect is generated to patient, more and more patients are more likely to selection non-drug therapy.It is controlled in numerous non-drug
In treatment means, EEG Biofeedback Training pays attention to because the advantages that its is safe and effective, curative effect is lasting, without side-effects is increasingly becoming treatment
The Main way of power defect patient.A large amount of clinical test proves that electroencephalographic biofeedback therapy treats the curative effect of attention deficit
With the therapeutic equivalence of central nervous excitation agent.
Occur combining the report to help children's hyperkinetic syndrome rehabilitation with game with BCI recent years.In report, θ/β
For ratio as the index for distinguishing normal child and children's hyperkinetic syndrome children, applying game can be such that infant keeps easily, and light
Facilitate θ/β in loose situation and tend to normal level, detect whether θ/β ratio in game process declines, does not play biological anti-
Present effect.
Recently, more and more about the research of entropy, point out that ADHD patient is executing attention cognition times in existing research report
The ApEn of right side frontal lobe is substantially less than normal person (referring to Linear and non-linear EEG analysis of in business
adolescents with attention-deficit/hyperactivity disorder during a cognitive
Task), this illustrates that entropy can be used as one reference index of attention deficit patient degree.Another report research discovery
Patient's (consciousness is normal, agraphia with movement) of Healthy subjects and block comprehensive disease composes entropy in the state of active attention and compares
Increase in tranquillization state and passive attention (referring to Toward an Attention-Based Diagnostic Tool for
Patients With Locked-in Syndrome).This illustrates composing entropy can be used as whether be in one of attention state
Index.
It refers to BCI now with some patents come the rehabilitation training to attention deficit patient is, but almost without
People notices the evaluation index to rehabilitation training effect.For example test and instruction are reached come real-time human brain attention using EEG signal
Practice purpose system (application number: CN 107024987) but the patent focus on be building for system, needle is not set forth in detail
Selection to the characteristic of division in rehabilitation training is paid attention to, meanwhile, also therapeutic effect is not assessed.Patent is based on brain telecommunications
Number feature extraction and its monitoring extraction system (application number: CN 108236464) in also refer to using entropy as feature progress
Pay attention to rehabilitation training, but the spectrum entropy in the present invention is mainly different from using the method for comentropy, and the invention does not also provide health
The evaluation index of multiple training effect.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of attention rehabilitation training and appraisal procedure based on spectrum entropy,
Using spectrum entropy as feature, to attention deficit, patient first trains disaggregated model, real to the data in rehabilitation training
When classify, robot makes corresponding prompt according to classification results.Patient need to only keep body motionless and concentrate during this
Attention watches the performance of robot, is adjusted according to the prompt of robot, to play the effect of rehabilitation training.This method
Classified according to spectrum entropy as feature, active training effectively is carried out to patient, fundamentally treats attention deficit
Illness；It is simultaneously that the spectrum entropy of training front and back is more convincing as the evaluation index of therapeutic effect.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of attention rehabilitation training and appraisal procedure based on spectrum entropy, comprising the following steps:
S1: rehabilitation training object is acquired for the brain wave EEG related potential of simple phonologic attention task, and to EEG
Data are pre-processed；
S2: the power spectral density of brain wave data after pretreatment is calculated；
S3: spectrum entropy is calculated；
S4: support vector machines is utilized, the attention EEG signal in rehabilitation training is trained characterized by composing entropy, is obtained
To disaggregated model；
S5: the tranquillization state data before acquisition rehabilitation training object training；
S6: rehabilitation training is carried out to rehabilitation training object, and the EEG data in rehabilitation training is divided in real time
Class；
S7: being fed back according to real-time grading result, provides corresponding prompt, helps to focus on；
S8: the tranquillization state data after acquisition rehabilitation training object training；
S9: rehabilitation training effect is judged according to the spectrum changes of entropy of training front and back tranquillization state data acquisition.
Further, EEG data is pre-processed described in step S1, including the segmentation of brain electricity, bad track are rejected, bad track reparation, filter
Wave, baseline correction and artefact removal；
Wherein, brain electricity is segmented: 1. in off-line data, stimulation repeats to present, and each trial points are one section；2. in training
In, it is desirable that the long-term state for keeping attention to concentrate, so every 10s is divided into one section；
Baseline correction: in data acquisition, outside noise and due to being tested itself, signal may be generated
Drift, for the mistake for overcoming this phenomenon to generate, the data segment of 200ms is average as baseline before stimulating, post-stimulatory
Data segment will subtract baseline；
Remove artefact: the artefacts such as removal eye movement blink.
Further, in step S2, pretreated data find out power spectral density P (f) using Welch method, by P (f)
Standardization obtains the probability density function about frequency f
Further, in step S3, the power spectrum data by choosing full frequency band 0.5-45Hz calculates spectrum entropy
Further, in step S4, the training method of disaggregated model includes: that keyword the correct trail note of key is occurred
For positive sample, key is not denoted as negative sample, is denoted as positive sample for what not no key occurred in no keyword, button operation
It is denoted as negative sample, trains disaggregated model using SVM method.
The beneficial effects of the present invention are: the invention proposes spectrum entropy as the characteristic value for paying attention to classification, greatly improves
The accuracy rate of classification.The variation of invention spectrum entropy provides a foundation for Quantified therapy effect.Meanwhile the invention belongs to base
In the electroencephalographic biofeedback therapy of BCI, loop under entire cortex and cortex can be adjusted by adjusting the electrical activity of brain of sufferer
Functional level, strengthen suitable adjusting, inhibit unfavorable adjusting, to really improve entire brain function.Before patient's training
The spectrum entropy of tranquillization state data is compared afterwards, can effectively quantify rehabilitation training effect.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the attention rehabilitation training system figure based on spectrum entropy；
Fig. 2 is rehabilitation training detailed process figure；
Fig. 3 is off-line training marker samples flow chart；
Fig. 4 is the spectrum entropy comparison diagram of training front and back and standard (normal person).
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
As shown in Figure 1-3, the present invention provides a kind of attention rehabilitation training and appraisal procedure based on spectrum entropy, wherein sharing
Three roles, respectively attention deficit person, robot, therapist.Task about these three roles is allocated as follows:
Attention deficit person: wearing simple electrode cap, focus on keep and it is motionless, mentioned according to the voice of robot
Show the behavior and consciousness for actively adjusting itself.
Robot: voice is made to patient according to the classification results of the EEG signal of patient and movement is fed back.
Therapist: helping patient to wear electrode cap, sets treatment time according to conditions of patients.
It should be noted that power handicapped wears easy electrode cap in rehabilitation training, connection brain electric data collecting is set
Standby, robot can make corresponding reaction according to the state of patient.
Basic principle of the invention is when patient's attention is concentrated, and spectrum entropy increases, and when distractibility, spectrum entropy subtracts
Few, spectrum entropy is classified as feature, and classification results are fed back to patient by the prompt of the feedback ends such as robot, allows patient
The state of itself is adjusted in real time.
Need to acquire patient before attention training for simple phonologic attention task related potential, to off-line data into
Spectrum entropy is extracted in row pretreatment.During off-line training, is there is into the correct trail of key in keyword and is denoted as positive sample, is not had
Have key is denoted as negative sample, and no keyword occurs not to key is denoted as positive sample, and button operation is denoted as negative sample,
Disaggregated model is trained using SVM method.
Tranquillization state data of the patient before and after training are acquired, according to the change of the spectrum entropy of patient's tranquillization state data acquisition twice
Change to judge therapeutic effect, if the spectrum entropy of training front and back tranquillization state has apparent increase, proves that this is achieved well
Therapeutic effect.
Patient is required to keep body motionless and attention concentration in attention training process, according to specific reading chapter to phase
The number for answering keyword (for example, character in a novel, place, the adjective etc. of the description scenery in the article that describes the scenery) to occur carries out
Learn by heart, and when robot reads keyword makes the sense organ input of required movement (such as: carry arm) Lai Jiaqiang patient.If process
It is middle that robot provides corresponding prompt when there are abnormal conditions that are absent minded or tampering, if patient remind it is multiple
Still attention can not be concentrated afterwards, and robot starts to dance to prompt more to attract patient's attention or patient to occur continuing moving more
Can not still it stop after secondary, robot, which will sing, at this time helps patient to quiet down.After abnormal conditions disappear, robot is after resuming studies
Book, after the completing setting time of the task, robot voice praises patient, completes this time to pay attention to training, as shown in Figure 2.
EEG data pretreatment includes that brain electricity is segmented, baseline correction, removes artefact, the operation such as filtering.Brain electricity segmentation: 1. from
Line number according to when, stimulation repeats to present, and each trial point is one section.2. and in training, it is desirable that patient keeps attention to concentrate for a long time
State, so for this part, every 10s is divided into one section.The purpose of brain electricity segmentation is will not according to the difference of stimulation
Same sample extraction comes out, and is divided into isometric data segment for continuous stimulation event.Baseline correction: in data acquisition
In, outside noise and due to being tested itself, signal may generate drift, for the mistake for overcoming this phenomenon to generate
Accidentally, for the data segment of 200ms as baseline, post-stimulatory data segment will subtract baseline before stimulating.Remove artefact: removal eye
The artefacts such as dynamic, blink.
Pretreated data find out power spectral density P (f) using Welch method.PSD is the function about frequency f, will
The available probability density function about frequency f of P (f) standardization, such as formulaChoose full range
The power spectrum data of section 0.5-45Hz calculates spectrum entropy
Therapeutic effect composes the comparison diagram of entropy as shown in figure 4,1,2 therapeutic effect of patient is obvious, and patient 3 does not play expected
Therapeutic effect, 4 effect of patient are unobvious.
The shared of the EEG data under normal form is paid attention in order to realize, can develop a data management system.System is divided into general
General family, therapist, data administrator, customer administrator, five kinds of roles of super keepe.Wherein, common user is auditing
It can be obtained the permission for uploading data after, the data of upload need data administrator to audit, can be obtained down after the approval
Carry the permission of data.If the user after succeeding in registration is badly in need of downloading data, the corresponding amount of money can be paid according to download.User
After the data of upload are downloaded successfully by other users, corresponding remuneration can be obtained.Therapist is a kind of special user, is had free
The permission of downloading data is obtained, but the data of the treatment acquisition of patient are automatically uploaded in system every time, referred to as the system
Data.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (5)
1. a kind of attention rehabilitation training and appraisal procedure based on spectrum entropy, it is characterised in that: the following steps are included:
S1: rehabilitation training object is acquired for the brain wave EEG related potential of simple phonologic attention task, and to EEG data
It is pre-processed；
S2: the power spectral density of brain wave data after pretreatment is calculated；
S3: spectrum entropy is calculated；
S4: support vector machines is utilized, the attention EEG signal in rehabilitation training is trained characterized by composing entropy, is divided
Class model；
S5: the tranquillization state data before acquisition rehabilitation training object training；
S6: rehabilitation training is carried out to rehabilitation training object, and real-time grading is carried out to the EEG data in rehabilitation training；
S7: being fed back according to real-time grading result, provides corresponding prompt, helps to focus on；
S8: the tranquillization state data after acquisition rehabilitation training object training；
S9: rehabilitation training effect is judged according to the spectrum changes of entropy of training front and back tranquillization state data acquisition.
2. the attention rehabilitation training and appraisal procedure according to claim 1 based on spectrum entropy, it is characterised in that: step S1
Described in EEG data pre-process, including brain electricity segmentation, bad track reject, bad track reparation, filtering, baseline correction and artefact removal；
Wherein, brain electricity is segmented: 1. in off-line data, stimulation repeats to present, and each trial points are one section；2. being wanted in training
The long-term state for keeping attention to concentrate is sought, so every 10s is divided into one section；
Baseline correction: in data acquisition, outside noise and due to being tested itself, signal may generate drift
It moves, for the mistake for overcoming this phenomenon to generate, the data segment of 200ms is as baseline before stimulating, and post-stimulatory data segment is all
Subtract baseline；
Remove artefact: removal eye movement blink artefact.
3. the attention rehabilitation training and appraisal procedure according to claim 1 based on spectrum entropy, it is characterised in that: step S2
In, pretreated data find out power spectral density P (f) using Welch method, and P (f) standardization is obtained about frequency f's
Probability density function
4. the attention rehabilitation training and appraisal procedure according to claim 1 based on spectrum entropy, it is characterised in that: step S3
In, the power spectrum data by choosing full frequency band 0.5-45Hz calculates spectrum entropy
5. the attention rehabilitation training and appraisal procedure according to claim 1 based on spectrum entropy, it is characterised in that: step S4
In, the training method of disaggregated model includes: that the correct trail of key is occurred in keyword to be denoted as positive sample, not the note of key
For negative sample, it is denoted as positive sample by what not no key occurred in no keyword, button operation is denoted as negative sample, uses the side SVM
Method trains disaggregated model.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110068466A (en) * | 2019-04-30 | 2019-07-30 | 山东理工大学 | Vehicle sound quality evaluation method based on brain wave |
| CN110200625A (en) * | 2019-07-05 | 2019-09-06 | 郭长娥 | A kind of brain brain neuroblastoma intensive training device and method |
| CN110584663A (en) * | 2019-09-20 | 2019-12-20 | 深圳大学 | Drug effect judgment device for herpes zoster and use method thereof |
| CN110652294A (en) * | 2019-09-16 | 2020-01-07 | 清华大学 | Creativity personality trait measuring method and device based on electroencephalogram signals |
| CN113192601A (en) * | 2021-04-15 | 2021-07-30 | 杭州国辰迈联机器人科技有限公司 | Attention deficit hyperactivity disorder rehabilitation training method and training task based on brain-computer interface |
| CN113679386A (en) * | 2021-08-13 | 2021-11-23 | 北京脑陆科技有限公司 | Method, device, terminal and medium for recognizing attention |
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2019
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| CN110068466A (en) * | 2019-04-30 | 2019-07-30 | 山东理工大学 | Vehicle sound quality evaluation method based on brain wave |
| CN110068466B (en) * | 2019-04-30 | 2021-03-12 | 山东理工大学 | Brain wave-based vehicle sound quality evaluation method |
| CN110200625A (en) * | 2019-07-05 | 2019-09-06 | 郭长娥 | A kind of brain brain neuroblastoma intensive training device and method |
| CN110652294A (en) * | 2019-09-16 | 2020-01-07 | 清华大学 | Creativity personality trait measuring method and device based on electroencephalogram signals |
| CN110584663A (en) * | 2019-09-20 | 2019-12-20 | 深圳大学 | Drug effect judgment device for herpes zoster and use method thereof |
| CN110584663B (en) * | 2019-09-20 | 2022-04-12 | 深圳大学 | Drug effect judgment device for herpes zoster and use method thereof |
| CN113192601A (en) * | 2021-04-15 | 2021-07-30 | 杭州国辰迈联机器人科技有限公司 | Attention deficit hyperactivity disorder rehabilitation training method and training task based on brain-computer interface |
| CN113679386A (en) * | 2021-08-13 | 2021-11-23 | 北京脑陆科技有限公司 | Method, device, terminal and medium for recognizing attention |
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