CN107961120A - A kind of intelligent wheelchair control system based on brain electric control - Google Patents
A kind of intelligent wheelchair control system based on brain electric control Download PDFInfo
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- CN107961120A CN107961120A CN201711294688.9A CN201711294688A CN107961120A CN 107961120 A CN107961120 A CN 107961120A CN 201711294688 A CN201711294688 A CN 201711294688A CN 107961120 A CN107961120 A CN 107961120A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
- A61G5/00—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs
- A61G5/10—Parts, details or accessories
- A61G5/1051—Arrangements for steering
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Abstract
The present invention provides a kind of intelligent wheelchair control system based on brain electric control, including brain wave acquisition module, pretreatment module, characteristic extracting module, Classification and Identification module, device control module, data center's carrier；The brain wave acquisition module is used to gather EEG signals；The pretreatment module is used for the pretreatment to EEG signals；The characteristic extracting module is used for EEG feature extraction, including Mental imagery feature extraction and alertness feature extraction；The Classification and Identification module is used to identify brain-computer interface classification of task；The device control module is instructed EEG's Recognition result is encoded translated in order to control；Data center's carrier is eeg data and the carrier platform of Processing Algorithm.The invention discloses a kind of intelligent wheelchair control system based on brain electric control, it can be achieved that adjusting wheelchair speed and state in real time according to User Status while brain electric control wheel chair sport, a kind of safe and reliable application is provided to the user.
Description
Technical field
The present invention relates to brain-computer interface (brain-computer interface, BCI) technical field, more specifically,
It is related to a kind of intelligent wheelchair control system based on brain electric control.
Background technology
Brain-computer interface (Brain Computer Interfaces, BCI) be it is a kind of independent of brain nervus peripheralis and
Muscle and directly control perform equipment system, by decode human brain realize signal, realize the control to external device (ED).BCI exists
Medical rehabilitation, life ＆ amusement, military operation field have wide practical use, and have become brain science, neuro-physiology, signal
The crossing research hot spot of analysis, control science and computer science.
At present, BCI is still in the starting stage in the application study of disadvantaged group's assisted living, at present to single brain-computer interface
Research more universal and relative maturity, but user's normal use scene is often only considered in utilitarian design system, it is right
Lack in the state of user and consider, it is impossible to automatically control the state of controlled device according to User Status, such as wheelchair it is real-time, from
Dynamic speed Control demand.Switzerland Jos é delR.Mill á n etc. utilize Mental imagery brain-computer interface, and event-related design/go is same
Walk (RED/RES) and successfully extract 2 application of instruction in the left and right control of wheelchair, but since the system uses single mode brain-computer interface,
Therefore multiple free degree control signals cannot be provided, it is impossible to which acceleration, deceleration and startup, stopping, system availability and security be not high.
Patent CN200810053558.0 is disclosed based on the intelligent wheelchair control system of brain-computer interface and its EEG Processing side
Method, devises a set of wheelchair steerable system based on α ripple brain-computer interfaces using α wave resistances phenomenon of breaking, realizes front, rear, left and right four
A direction movement control, but can not realize speed-regulating function.
The content of the invention
For the above situation, to overcome the shortcomings of existing brain control wheelchair control technology, the present invention provide one kind can according to
The intelligent wheelchair control system based on brain electric control of family oneself state adaptive speed regulation.
The present invention includes brain wave acquisition module, pretreatment module, characteristic extracting module, Classification and Identification module, equipment control
Module, data center's carrier；The brain wave acquisition module is used to gather EEG signals；The pretreatment module is used for brain
The pretreatment of electric signal；The characteristic extracting module is used for EEG feature extraction, including Mental imagery feature extraction
With alertness feature extraction；The Classification and Identification module is used to, to brain-computer interface classification of task identification, judge that Mental imagery is appointed
Classification of being engaged in and alertness grade, output control instruction；The device control module is encoded translated by EEG's Recognition result
Instruct in order to control, be used for realization the action of wheelchair.
The present invention judges that Mental imagery is appointed by extracting Mental imagery brain electrical feature and alertness feature, Classification and Identification module
Classification of being engaged in and alertness grade, output control instruction；Finally, to realize that brain-computer interface replaces the Mechanical course of wheelchair control stick,
Design device control module causes electric wheelchair to have communication function, i.e., the instruction by receiving brain-computer interface realizes wheelchair not
With advancing under speed state, turn left, turn right and stop operation.
Brief description of the drawings
Fig. 1 is BCI signal analysis figures.
Fig. 2 is brain electric control intelligent wheelchair system figure.
Embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing.
A kind of intelligent wheelchair control system based on brain electric control, including brain wave acquisition module, pretreatment module, feature carry
Modulus block, Classification and Identification module, device control module, calculating central carrier；The brain wave acquisition module is used to gather brain electricity
Signal；The pretreatment module is used for the pretreatment to EEG signals；The characteristic extracting module is used for EEG signals
Feature extraction, including Mental imagery feature extraction and alertness feature extraction；The Classification and Identification module is used to connect brain-machine
Mouth classification of task identification；The device control module is instructed EEG's Recognition result is encoded translated in order to control；Described
Data center's carrier is eeg data and the carrier platform of Processing Algorithm, and wherein auxiliary program is used to managing between modules
Collaboration.
The brain wave acquisition module includes electrode cap and electroencephalogramdata data collector, and electrode cap is used to gather EEG signals, brain electricity
Acquisition Instrument is used to being amplified the EEG signals of collection, filters and analog-to-digital conversion process；It is single that the control module includes communication
Member and controller, control module are electronic for controlling according to the instruction of multi-mode brain-computer interface, output two-way analog voltage signal
The movement of wheelchair.
The algorithm of the event EEG Processing module and Mental imagery EEG Processing module is by car
Notebook programming is carried to provide.The Mental imagery EEG Processing algorithm flow is：CAR filtering → Mu rhythm and pace of moving things frequency band extraction →
CSP feature extractions → svm classifier.The alertness testing algorithm flow is：Low-pass filtering → P300 amplitude Characteristics → SVM points
Class.
System is first acquired brain electricity by brain wave acquisition module, and brain wave acquisition uses NeuroScan brain wave acquisitions
Equipment, sets the processing of 50Hz traps；
Pretreatment module carries out 8-30Hz bandpass filterings to brain electric information, goes artefact, baseline correction；
Mental imagery feature extraction is used under cospace pattern (CSP) method extraction left hand, the right hand and fantasy state
EEG signals characteristic quantity.Processing step is as follows：
(1) average covariance matrices of 3 class EEG signals are calculated；
(2) to the covariance matrix approximately joint diagonalization of 3 class EEG signals；
(3) suitable spatial filter is selected using maximum mutual information；
(4) EEG signals are filtered using spatial filter.
4 spatial filters are extracted to every class EEG signals, take the average energy value of EEG signals after filtering as feature；
Alertness feature extraction is as follows using algorithm：
(1) to α ripple brain wave rhythms fa(t) Fourier transformation is carried out, obtains Fa(t)；
(2) by Fa(t) square the ENERGY E of brain electrical feature is obtaineda；
(3) in the manner described above, β ripples are sought, δ ripples, θ wave energies, are respectively Eβ,Eδ,Eθ
(4) the energy variation design alertness exponential model of the multiple rhythm and pace of moving things of comprehensive analysis is
(5) found to work as α ripples according to experiment, when β ripples account for dominant advantage, the consciousness of subject is clear-headed, when δ ripples, θ ripples
When leading, the consciousness of people is fuzzy；
Classification and Identification module is used as grader using support vector machines (Support vector machine, SVM), punishment
Factor C and kernel function g is the major parameter for influencing SVM performances, and C affects the data distribution after spatial alternation, and parameter g is determined
The convergence rate and Generalization Ability of support vector machines.
Device control module：Device control module controls direction and the rotating speed of wheelchair.Wherein, three of Mental imagery
State left hand acts (a), right hand action (b) and left-hand rotation, right-hand rotation and the straight trip of state (c) difference controling wheelchair of day-dreaming, alertness
For the rotating speed of controling wheelchair, alertness divides three grades ABC, wherein, A grades represent controling wheelchair proper motion, B grade generations
Table controling wheelchair is slowly moved, and C grades, which represent, forbids wheel chair sport；Research shows that training in 60 minutes is real being carried out to subject
When testing, eeg data is temporally divided into 6 sections (do not have 10 minutes be one section), be denoted as the period 1, the period 2, the period 3, when
Between section 4, period 5 and period 6.Found when carrying out multigroup alertness experiment, alertness changes over time curve and presents figure
Rule.Therefore, two threshold values w1=1.5 and w2=3.5 are set, for dividing the alertness of subject to three grades ABC.Wherein
F is A in 0-1.5, and A is waking state, and F is B in 1.5-3.5, and B is doze state, and F is C more than 3.5, and C is asleep state.
Equipment control strategy：It is normal turn left (A, a), normal to turn right (A, b), normal straight-ahead operation (A, c) slowly turn left (B, a),
Slowly turn right (B, b), slowly keep straight on (B, c), forbid moving (C, a) (C, b) (C, c)；
Calculate central carrier module：The module is used to carry out rapid computations processing to the data collected, then at output
Result information is managed to device control module.
Claims (7)
1. a kind of intelligent wheelchair control system based on brain electric control, it is characterised in that including brain wave acquisition module, pretreatment mould
Block, characteristic extracting module, Classification and Identification module, device control module；The brain wave acquisition module is used to gather EEG signals；
The pretreatment module is used for the pretreatment to EEG signals；The characteristic extracting module is used to put forward EEG signals feature
Take, including Mental imagery feature extraction and alertness feature extraction；The Classification and Identification module is used for brain-computer interface task
Classification and Identification, judges Mental imagery task category and alertness grade, output control instruction；The device control module is by brain
Electric signal recognition result is encoded translated to be instructed in order to control, is used for realization the action of wheelchair.
2. a kind of intelligent wheelchair control system based on brain electric control according to claim 1, it is characterised in that further include
Data center's carrier, data center's carrier are eeg data and the carrier platform of Processing Algorithm, and auxiliary program therein is used
Collaboration between modules are managed.
A kind of 3. intelligent wheelchair control system based on brain electric control according to claim 1, it is characterised in that the brain
Electric acquisition module includes electrode cap and electroencephalogramdata data collector, and electrode cap is used to gather EEG signals, and electroencephalogramdata data collector is used for collection
EEG signals be amplified, filter and analog-to-digital conversion process.
A kind of 4. intelligent wheelchair control system based on brain electric control according to claim 1, it is characterised in that extraction brain
Electrical signal feature includes the feature of right-hand man's Mental imagery and the feature for alertness analysis.
A kind of 5. intelligent wheelchair control system based on brain electric control according to claim 4, it is characterised in that right-hand man
The tagsort recognition result of Mental imagery is used for controling wheelchair and advances, turns left and turn right.
6. a kind of intelligent wheelchair control system based on brain electric control according to claim 4, it is characterised in that according to police
The state of mind for judging active user is tested in Juedu, and wheelchair speed and stopping are adjusted in real time automatically according to the state of mind.
7. a kind of intelligent wheelchair control system based on brain electric control according to claim 1, it is characterised in that described to set
Standby control module includes communication unit and controller, and control module is according to the instruction of multi-mode brain-computer interface, output two-way simulation
Voltage signal, for controlling the movement of electric wheelchair.
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| CN201711294688.9A CN107961120B (en) | 2017-12-08 | 2017-12-08 | Intelligent wheelchair control system based on electroencephalogram control |
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| CN201711294688.9A CN107961120B (en) | 2017-12-08 | 2017-12-08 | Intelligent wheelchair control system based on electroencephalogram control |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108919947A (en) * | 2018-06-20 | 2018-11-30 | 北京航空航天大学 | A kind of brain machine interface system realized by visual evoked potential and method |
| CN109464239A (en) * | 2019-01-09 | 2019-03-15 | 浙江强脑科技有限公司 | Intelligent wheel chair based on E.E.G control |
| CN111522445A (en) * | 2020-04-27 | 2020-08-11 | 兰州交通大学 | Intelligent control method |
| CN112245131A (en) * | 2020-09-03 | 2021-01-22 | 深圳睿瀚医疗科技有限公司 | Wheelchair control system and method based on facial expression electroencephalogram signal driving |
| CN114176920A (en) * | 2021-12-20 | 2022-03-15 | 曲阜师范大学 | Intelligent wheelchair based on electroencephalogram control |
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| CN112245131A (en) * | 2020-09-03 | 2021-01-22 | 深圳睿瀚医疗科技有限公司 | Wheelchair control system and method based on facial expression electroencephalogram signal driving |
| CN114176920A (en) * | 2021-12-20 | 2022-03-15 | 曲阜师范大学 | Intelligent wheelchair based on electroencephalogram control |
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