CN101301244A - Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof - Google Patents
Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof Download PDFInfo
- Publication number
- CN101301244A CN101301244A CNA2008100535580A CN200810053558A CN101301244A CN 101301244 A CN101301244 A CN 101301244A CN A2008100535580 A CNA2008100535580 A CN A2008100535580A CN 200810053558 A CN200810053558 A CN 200810053558A CN 101301244 A CN101301244 A CN 101301244A
- Authority
- CN
- China
- Prior art keywords
- brain
- signal
- signal processing
- control system
- eeg signals
- 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.)
- Pending
Links
Images
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention relates to an intelligent wheel chair control system based on a brain-machine interface and a method for processing the EEG signal of the same. The system comprises a preposed amplified signal preprocessing circuit which collects the EEG signal of a testee, a signal collection card A/D converter, a signal processing device, a signal collection card D/A converter, an interface circuit, an end-around lamp control panel and a wheel chair which are connected in turn; and the end-around lamp control panel is also connected with the testee so as to receive the feedback signal sent out by the testee. The method comprises the following steps that: the differential input of an EEG signal is completed by a signal collection card; the EEG signal is filtered; the RMS smoothing algorithm of the filtered EEG signal is carried out; the filtered EEG signal is divided into two signals with one signal entering into a main control channel after 400 to 500 ms averaging and the other signal entering into an auxiliary control channel through 50 ms averaging method; and the signals are output when the signals are alpha wave signals. The intelligent wheel chair control system has simple and convenient operation, and can help handicapped people, aged people and severe paralysis patients still having thinking in the brain to move freely just through placing two electroencephalograph detection electrodes at the two lead parts of the pillow part and the ear parts of a testee, thereby improving the quality of life of the people.
Description
Technical field
The present invention relates to a kind of intelligent wheel chair.Particularly relate to and a kind ofly can help physically disabled, aging crowd, and serious symptom paralysis but brain has the people of thinking to act on one's own, can carry out communication for information and control with the external world, with intelligent wheelchair control system and the brain-electrical signal processing method thereof of quality of life of improving them based on brain-computer interface.
Background technology
Brain-computer interface (Brain-Computer Interface-BCI) is a kind of direct communication for information and the control channel of setting up between human brain and computer or other electronic equipments, is a kind of brand-new information exchanging system that does not rely on conventional brain output channel (peripheral nervous and muscular tissue).
Therefore, brain-computer interface is the important topic in the intelligent wheel chair research, involves a wide range of knowledge, and it is more to comprise problem.The research early start of BCI the seventies in last century, if U.S. national defense general headquarters have seen that the operational pilot can directly control aircraft with thinking great application prospect will be arranged, is therefore launched research to BCI.Because specific at that time technical limitations, successful probability is very little, and plan has been cancelled.But some element tasks of being done originally but are the fast development in this field today to lay the foundation.The main purpose of current research BCI is to suffer cerebral palsy (cerebral palsy) in order to help, or spinal injury loses peripheral nervous control but patient that ability of thinking remains, makes them realize to external world interchange and control again.In the past few decades, this research has obtained great advance.
Basic BCI system design and control are as shown in Figure 1.Signal obtains from scalp or intracranial by resistance, extract the signal characteristic of reflection user intention through signal processing, these features are converted into the order of control appliance again (as a word processor, handling wheelchair or nerve injury remedies), have only user to coordinate relation between signal characteristic and its intention, BCI selection simultaneously, feature extraction also transfer it to the equipment control command effectively and accurately, and system could valid function.
Intelligent wheel chair is that the intelligent robot technology is applied to electric wheelchair, merge the research in multiple field, comprise robot navigation and location, pattern recognition, Multi-sensor Fusion and man machine interface etc., relate to technology such as machinery, control, pick off, artificial intelligence, communication, also claim intelligent wheel chair formula mobile robot.Since Britain in 1986 began to develop first intelligent wheel chair, many countries dropped into more fund research intelligent wheel chair.As the WHEELESLEY of Massachusetts Institute Technology project, French VAHM project, the MAID of ULM, Germany university (old people and people with disability's mobility aid) project, Bremen AutonomousWheelchair project, Spain SIAMO project, Canadian AAI company's T AO project, the TIDE of European Union project, the TINMAN of KISS institute project, the department of electrical engineering LUOSON of Taiwan Zhongzheng Univ project, China's 863 intelligent robot intelligent wheel chair projects and Third Military Medical University's institute of Surgical Research project etc.Because each breadboard target and research method are not quite similar, the problem of every kind of wheelchair solution and the ability difference that reaches.The United Nations delivers report and points out, whole world aged tendency of population process is accelerated, in 50 years from now on, population ratio more than 60 years old is estimated and will be doubled, because the disabled that various disasters and disease cause also increase year by year, there is Disability in various degree in they, as walking, vision, start and language etc.For the walking-replacing tool of superior performance being provided for old people and disabled, help them to improve the freedom of action degree and reintegrate society, at present countries such as the U.S., Germany, Japan, France, Canada, Spain and China are studied intelligent wheel chair, make intelligent wheel chair have the memory map, keep away barrier, walk automatically, and function such as user interactions.
Brain-computer interface is to set up a kind of direct information exchanging system between human brain and computer or other electronic equipment.The normal people of common 90% cranial nerve function is at the short time hindbrain electricity (Electroencephalography that closes one's eyes, EEG) alpha (α) wave amplitude all can obviously strengthen in, simultaneously, because EEG has write down the electrical activity of brain different parts neural cell group, people's mental awareness activity also should reflect in the brain electricity in theory, so EEG becomes among the BCI and to replace the N﹠M system to carry the first-selected instrument of information.
Summary of the invention
Technical problem to be solved by this invention is, provide a kind of and can help physically disabled, aging crowd, and serious symptom paralysis but brain has the people of thinking to act on one's own, can carry out communication for information and control with the external world, with intelligent wheelchair control system and the brain-electrical signal processing method thereof of quality of life of improving them based on brain-computer interface.
The technical solution adopted in the present invention is: a kind of intelligent wheelchair control system and brain-electrical signal processing method thereof based on brain-computer interface.Wherein, intelligent wheelchair control system based on brain-computer interface, include successively and to link to each other: preamplification signal pre-process circuit, data acquisition card A/D conversion, signal processing apparatus, data acquisition card D/A conversion, interface circuit, circulation lamp control panel and the wheelchair of gathering experimenter's EEG signals, wherein, circulation lamp control panel also is connected with the experimenter and receives the feedback signal that the experimenter sends.
Described preamplification signal pre-process circuit be by be arranged on experimenter's occipitalia or ear two lead the place two brain electro-detection electrodes gather experimenter's EEG signals.
Described signal processing apparatus adopts computer.
Described interface circuit includes: pulse generator, four bidirectional shift registers that link to each other with pulse generator, four four input nand gates that link to each other with the gate cell outfan with four bidirectional shift registers, the interface contact that links to each other with four input nand gates, described four also are connected the outfan of data acquisition card D/A conversion with the gate cell input, before described interface contact connects expression on the circulation lamp control panel, right a, left side, after four circulation display lamps before, right a, left side, after.
Be used for the brain-electrical signal processing method of requirement 1 described intelligent wheel chair, include following steps:
(1) by the differential input EEG signals of data acquisition card;
(2) EEG signals is carried out filtering;
(3) filtered EEG signals is carried out the RMS smoothing algorithm;
(4) divide two-way: one tunnel average through 400～500ms enters the main control passage, and judges whether to surpass threshold voltage, if do not surpass the average algorithm of proceeding 400～500ms, above then entering next step with; Another road is through the average algorithm of 50ms, enters next step after entering auxiliary control channel;
(5) judge whether signal is the α wave control signal, is then to export, otherwise continue to divide two-way to carry out the average of two-way.
Described EEG signals being carried out filtering, is the main constituent that keeps the α ripple through the bandpass filtering of 8～13Hz.
The described RMS of carrying out smoothing algorithm is the RMS smoothing algorithm that carries out 10ms.
Intelligent wheelchair control system and brain-electrical signal processing method thereof based on brain-computer interface of the present invention, be utilize in the brain electricity corresponding to the characteristics of the obvious rhythmicity of tool of alpha (a) ripple of visual cortex and in most cranial nerve function normal persons the disconnected phenomenon of ubiquitous a wave resistance (be close one's eyes the short time back a wave amplitude can obviously strengthen), and, designed the brain-computer interface intelligent wheel chair control system of a cover based on brain electricity a ripple in conjunction with the control principle of TV remote controller.The present invention is easy and simple to handle, only need to place two brain electro-detection electrodes at experimenter's occipitalia and ear two places of leading, needn't introduce vision or auditory stimulus signal (in order to avoid to patient menticide) by force, almost need not to learn with biofeedback training or only need the very short time training just can stablize and control advancing of intelligent wheel chair reliably.Help physically disabled, aging crowd, and serious symptom paralysis but brain has the people of thinking to act on one's own, improving their quality of life, and obtain considerable social benefit and economic benefit.
Description of drawings
Fig. 1 is basic BCI system control sketch map;
Fig. 2 is the brain wave acquisition position view;
Fig. 3 is an entire block diagram of the present invention;
Fig. 4 is the circuit block diagram of preamplifier Signal Pretreatment;
Fig. 5 is the interface circuit block diagram;
Fig. 6 is the interface circuit principle;
Fig. 7 is the flow chart of brain signal processing method of the present invention.
Wherein:
5: preamplification signal pre-process circuit 6: data acquisition card A/D conversion
7: signal processing apparatus 8: data acquisition card D/A conversion
9: interface circuit 10: circulation lamp control panel
11: the experimenter 12: wheelchair
91: 92: four bidirectional shift registers of pulse generator
93: 94: four input nand gates of power supply
95: interface contact
The specific embodiment
Below in conjunction with the embodiment accompanying drawing intelligent wheelchair control system and the brain-electrical signal processing method thereof based on brain-computer interface of the present invention made a detailed description.
The present invention utilize in the brain electricity corresponding to the characteristics of the obvious rhythmicity of tool of alpha (a) ripple of visual cortex and in most cranial nerve function normal persons the disconnected phenomenon of ubiquitous a wave resistance (be close one's eyes the short time back a wave amplitude can obviously strengthen), and, designed the brain-computer interface intelligent wheelchair control system of a cover based on brain electricity a ripple in conjunction with the control principle of TV remote controller.Only need pass through silver-silver chloride electrode during eeg signal acquisition of the present invention, and adopt the unipolar lead mode, with the public connecting end of ears as a reference, simultaneously also as the earth terminal input amplifier.Described brain wave acquisition position as shown in Figure 2.
As shown in Figure 3, intelligent wheelchair control system based on brain-computer interface of the present invention, include successively and to link to each other: preamplification signal pre-process circuit 5, data acquisition card A/D conversion 6, signal processing apparatus 7, data acquisition card D/A conversion 8, interface circuit 9, circulation lamp control panel 10 and the wheelchair 12 of gathering experimenter's 11 EEG signals, wherein, circulation lamp control panel 10 also is connected with experimenter 11 and receives the feedback signal that experimenter 11 sends.Described preamplification signal pre-process circuit 5 be by be arranged on experimenter's 11 occipitalias or ear two lead the place two brain electro-detection electrodes gather experimenter's 11 EEG signals.Described signal processing apparatus 7 adopts computer to carry out the processing of EEG signals.
At first, the experimenter produces the EEG signal that contains the control intention by 10 promptings of circulation lamp control panel; This signal carries out A/D conversion back through pretreatment such as preposition amplification, filtering and capture card and imports computer; On the Labview of computer platform, finish then secondary filtering denoising, RMS (root-mean-square) level and smooth-average, with threshold voltage relatively, produce a series of signal work of treatment such as pulse control signal; At last this control signal is connected to the motion of the electromechanical interface circuit control wheelchair of wheelchair.
As shown in Figure 4, described preamplification signal pre-process circuit 5 includes the prime amplification 51,50HZ filtering 52,40HZ low-pass filtering 53 and the back level that link to each other successively and amplifies 54.
Brain electricity (EEG) signal amplitude lower (about 5～300 μ V) requires preamplifier to have high-gain, high input impedance, high cmrr, low noise, low drift characteristic.The EEG signal has instability and nonlinear feature, so having adopted bandwidth is the eight passage preamplifiers of 0～30Hz, comprises prime amplification, filtering and back level amplification three parts, and is undistorted to guarantee EEG signals.
Data acquisition card A/D conversion 6 is to select for use American National instrument (NI) company to produce the A/D conversion that the 6024E data collecting card carries out the EEG signal.It has the adapter of 16 tunnels analogy input ports, 2 analog output mouths, 8 digital I/O ports (control I/O able to programme) and 68 pins.Each channel gain can be set at 0.5,1.0,10 or 100 respectively, and its input range can change with gain.Data acquisition card D/A conversion 8 is also finished on above-mentioned capture card.
As shown in Figure 5, described interface circuit 9 includes: pulse generator 91, four bidirectional shift registers 92 that link to each other with pulse generator 91, four four input nand gates 94 that link to each other with the gate cell outfan with four bidirectional shift registers 92, the interface contact 95 that links to each other with four input nand gates 94, described four also are connected the outfan of data acquisition card D/A conversion 8 with the gate cell input, before described interface contact 95 connects expression on the circulation lamp control panels 10, right a, left side, after four circulation display lamp a, b, c, d.
Control panel is made of the lampet of four circulation flickers: two red lights place upper and lower, and two green lights are positioned at left and right, and the arrangement that assumes diamond in shape is corresponding with preceding a, back d, left c, the right b direction of motion of wheelchair control respectively.Four display lamps are with clockwise direction circulation flicker and continue for some time, and entire circuit is realized by clock generator and shift register.
The EEG signals of data acquisition card D/A conversion 8 outputs are drawn a or back d or left c or right b before the control signal that needs by interface circuit 9 and circulation lamp control panel 10, be connected to then in the wheelchair operation control chamber, when certain direction control signal is exported in elected, control interface is connected wheelchair, finishes corresponding operation.
In interface circuit 9, adopt 555 intervalometers to make pulse generator 91; The model of four bidirectional shift registers 92 is 74LS194; The model of four input nand gates 94 is 74LS00.
The physical circuit schematic diagram of described interface circuit 9 as shown in Figure 6.After a series of signal processing procedure, what computer was exported is pulse voltage signal, and amplitude can be set by computer program as required, and in order to satisfy the needs of subsequent conditioning circuit design, the value of She Dinging is+5V in the present invention.According to the characteristic of pulse signal, designed the triggering control system of four options, four circulation lampets on the control panel have been simulated the motion of the front, rear, left and right four direction of wheelchair.555 clockings are imported four bidirectional shift registers, the EEG signals after the pulse that displacement produces and the D/A conversion with gate cell with, insert four input nand gates, make four display lamps circulate and glimmer and continue for some time with clockwise direction.
The wheelchair of present embodiment is that the electric wheelchair of Tianjin Xia Boke skill company limited production is reequiped, follower added the form of back (about two) driving wheel before it adopted, and was controlled the motion of wheelchair to the promotion of front, rear, left and right four direction by stick.(red line is the 5V power supply to six output leads of wheelchair stick, black line is a ground wire, blue, green, yellow, orange line voltage is respectively 1.7V to 3.3V) what export is six 0 voltage signals to 5V, by the voltage difference that various combination between them occurs seesawing of two driving wheels of wheelchair controlled, realized the motion of wheelchair four direction.
EEG signal after data acquisition card A/D conversion is finished the subsequent treatment to EEG signals on the software Labview of computer platform.
As shown in Figure 7, the brain-electrical signal processing method that is used for based on the intelligent wheelchair control system of brain-computer interface of the present invention includes following steps:
(1) by the differential input EEG signals of data acquisition card;
(2) EEG signals is carried out filtering; Described EEG signals being carried out filtering, is the main constituent that keeps the α ripple through the bandpass filtering of 8～13Hz.
(3) to filtered EEG signals again behind the RMS of 10ms smoothing algorithm signal be divided into two-way; Described RMS is meant root-mean-square, and english abbreviation is root mean square, virtual value just, be exactly one group of statistical data square the square root of meansigma methods.The statistical calculations formula:
(4) divide two-way: one tunnel average through 400～500ms, enter the main control passage, and judge whether to surpass threshold voltage, if do not surpass the average algorithm of proceeding 400～500ms, above then entering the main control passage, and enter next step with; Another road is through the average algorithm of 50ms, enters next step after entering auxiliary control channel;
(5) judge whether main channel signal is the α wave control signal, be then to export this signal to give the experimenter in order to control wheel chair sport and to produce a prompt tone, otherwise continue to divide two-way to carry out the average of two-way to the interface circuit on the wheelchair.
8 experimenters have been carried out two kinds of experiments: 1. under the direction control test-quiet environment, the experimenter utilizes the disconnected phenomenon of α wave resistance to produce the control of control signal realization wheelchair travel direction (as advance, retreat, turn left, turn right etc.) by panel circulation lamp indicating status; 2. the wheelchair test-experimenter that advances utilizes the disconnected phenomenon of α wave resistance to produce control signal by the panel indicating status to realize that wheelchair travels by projected route.Experimental data is as shown in table 1.In the experiment 1.: do not have trigger switch after closing one's eyes, be designated as wrong I; Hear triggered again after prompt tone is opened eyes first power to, be designated as wrong II; Just trigger wheelchair when not closing one's eyes, be designated as wrong III; The actual time of triggering has surpassed circulation lamp scintillation time and has caused next option selected, is designated as wrong IV.In the experiment 2.:
I class mistake is that pseudo-nagative potential during by signals collecting (when experimenter's peace and quiet are closed order and produced control signal, but can not triggered as normal system) causes; Ii class mistake is the performance of pseudo-positive potential (when the experimenter does not have peace and quiet to close order generation control signal, having the input positive voltage to trigger whole system owing to other interference causes above threshold potential).To experiment 1. and success rate 2. and error rate add up, find that therefrom 8 experimenters have higher success rate, but still have difference between them that wherein preceding 2 experimenter's success rates are higher than back 6 experimenters' success rate.This is because the former has passed through training repeatedly, and number of times and the time of latter training all will be less than the former, and the training that has also proved certain means thus can help the experimenter to strengthen that it is opened, the amplitude difference and shortening trigger switch selection operation time of α ripple when closing one's eyes.Find in the experiment: can accelerate the triggering selection operation when concentrating one's energy to imagine a certain button operation after requiring the experimenter to close one's eyes, this can help to improve the efficient of whole experiment.
Table 1 BCI wheelchair remote manipulation experiment success rate and error rate statistics
Claims (7)
1. intelligent wheelchair control system based on brain-computer interface, it is characterized in that, include successively and to link to each other: the preamplification signal pre-process circuit (5), data acquisition card A/D conversion (6), signal processing apparatus (7), the data acquisition card D/A that gather experimenter (11) EEG signals change (8), interface circuit (9), circulation lamp control panel (10) and wheelchair (12), wherein, circulation lamp control panel (10) also is connected with experimenter (11) and receives the feedback signal that experimenters (11) send.
2. the intelligent wheelchair control system based on brain-computer interface according to claim 1, it is characterized in that, described preamplification signal pre-process circuit (5) be by be arranged on experimenter's (11) occipitalia or ear two lead the place two brain electro-detection electrodes gather experimenter (11) EEG signals.
3. the intelligent wheelchair control system based on brain-computer interface according to claim 1 is characterized in that, described signal processing apparatus (7) adopts computer.
4. the intelligent wheelchair control system based on brain-computer interface according to claim 1, it is characterized in that, described interface circuit (9) includes: pulse generator (91), four bidirectional shift registers (92) that link to each other with pulse generator (91), four four input nand gates (94) that link to each other with the gate cell outfan with four bidirectional shift registers (92), the interface contact (95) that links to each other with four input nand gates (94), described four also are connected the outfan of data acquisition card D/A conversion (8) with the gate cell input, before described interface contact (95) connects expression on the circulation lamp control panel (10), right, a left side, after four circulation display lamps before (a), right (b), a left side (c), back (d).
5. a brain-electrical signal processing method that is used for requirement 1 described intelligent wheel chair is characterized in that, includes following steps:
(1) by the differential input EEG signals of data acquisition card;
(2) EEG signals is carried out filtering;
(3) filtered EEG signals is carried out the RMS smoothing algorithm;
(4) divide two-way: one tunnel average through 400～500ms enters the main control passage, and judges whether to surpass threshold voltage, if do not surpass the average algorithm of proceeding 400～500ms, above then entering next step with; Another road is through the average algorithm of 50ms, enters next step after entering auxiliary control channel;
(5) judge whether signal is the α wave control signal, is then to export, otherwise continue to divide two-way to carry out the average of two-way.
6. the brain-electrical signal processing method that is used for requirement 1 described intelligent wheel chair according to claim 5 is characterized in that, described EEG signals is carried out filtering, is the main constituent that keeps the α ripple through the bandpass filtering of 8～13Hz.
7. the brain-electrical signal processing method that is used for requirement 1 described intelligent wheel chair according to claim 5 is characterized in that, the described RMS of carrying out smoothing algorithm is the RMS smoothing algorithm that carries out 10ms.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2008100535580A CN101301244A (en) | 2008-06-18 | 2008-06-18 | Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2008100535580A CN101301244A (en) | 2008-06-18 | 2008-06-18 | Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101301244A true CN101301244A (en) | 2008-11-12 |
Family
ID=40111467
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNA2008100535580A Pending CN101301244A (en) | 2008-06-18 | 2008-06-18 | Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101301244A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101897640A (en) * | 2010-08-10 | 2010-12-01 | 北京师范大学 | Novel movement imagery electroencephalogram control-based intelligent wheelchair system |
| US8516568B2 (en) | 2011-06-17 | 2013-08-20 | Elliot D. Cohen | Neural network data filtering and monitoring systems and methods |
| CN103271802A (en) * | 2013-05-24 | 2013-09-04 | 桂林电子科技大学 | Wheelchair system based on control of FNIRI and EEG |
| CN104083258A (en) * | 2014-06-17 | 2014-10-08 | 华南理工大学 | Intelligent wheel chair control method based on brain-computer interface and automatic driving technology |
| CN105534648A (en) * | 2016-01-14 | 2016-05-04 | 马忠超 | Wheelchair control method and control device based on brain waves and head movements |
| CN106726209A (en) * | 2016-11-24 | 2017-05-31 | 中国医学科学院生物医学工程研究所 | A kind of method for controlling intelligent wheelchair based on brain-computer interface Yu artificial intelligence |
| WO2017113138A1 (en) * | 2015-12-30 | 2017-07-06 | 深圳市赛亿科技开发有限公司 | Method, device, and system for controlling home appliance |
| CN111805546A (en) * | 2020-07-20 | 2020-10-23 | 中国人民解放军国防科技大学 | Human-multi-robot sharing control method and system based on brain-computer interface |
| CN113281399A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Magnetic focusing sensor and detection system for simultaneously detecting multiple defects |
| CN113281398A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Detection sensor and detection system for needle type magnetic repulsion focusing |
-
2008
- 2008-06-18 CN CNA2008100535580A patent/CN101301244A/en active Pending
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101897640A (en) * | 2010-08-10 | 2010-12-01 | 北京师范大学 | Novel movement imagery electroencephalogram control-based intelligent wheelchair system |
| CN101897640B (en) * | 2010-08-10 | 2012-01-11 | 北京师范大学 | Novel movement imagery electroencephalogram control-based intelligent wheelchair system |
| US8516568B2 (en) | 2011-06-17 | 2013-08-20 | Elliot D. Cohen | Neural network data filtering and monitoring systems and methods |
| CN103271802A (en) * | 2013-05-24 | 2013-09-04 | 桂林电子科技大学 | Wheelchair system based on control of FNIRI and EEG |
| CN104083258B (en) * | 2014-06-17 | 2016-10-05 | 华南理工大学 | A kind of method for controlling intelligent wheelchair based on brain-computer interface and automatic Pilot technology |
| CN104083258A (en) * | 2014-06-17 | 2014-10-08 | 华南理工大学 | Intelligent wheel chair control method based on brain-computer interface and automatic driving technology |
| WO2017113138A1 (en) * | 2015-12-30 | 2017-07-06 | 深圳市赛亿科技开发有限公司 | Method, device, and system for controlling home appliance |
| CN105534648A (en) * | 2016-01-14 | 2016-05-04 | 马忠超 | Wheelchair control method and control device based on brain waves and head movements |
| CN106726209A (en) * | 2016-11-24 | 2017-05-31 | 中国医学科学院生物医学工程研究所 | A kind of method for controlling intelligent wheelchair based on brain-computer interface Yu artificial intelligence |
| CN106726209B (en) * | 2016-11-24 | 2018-08-14 | 中国医学科学院生物医学工程研究所 | A kind of method for controlling intelligent wheelchair based on brain-computer interface and artificial intelligence |
| CN111805546A (en) * | 2020-07-20 | 2020-10-23 | 中国人民解放军国防科技大学 | Human-multi-robot sharing control method and system based on brain-computer interface |
| CN111805546B (en) * | 2020-07-20 | 2021-11-26 | 中国人民解放军国防科技大学 | Human-multi-robot sharing control method and system based on brain-computer interface |
| CN113281399A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Magnetic focusing sensor and detection system for simultaneously detecting multiple defects |
| CN113281398A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Detection sensor and detection system for needle type magnetic repulsion focusing |
| CN113281398B (en) * | 2021-05-21 | 2023-09-01 | 华中科技大学 | Needle type magnetic repulsion focusing detection sensor and detection system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101301244A (en) | Intelligent wheelchair control system based on brain-machine interface and brain-electrical signal processing method thereof | |
| CN110765920B (en) | Motor imagery classification method based on convolutional neural network | |
| Lusted et al. | Controlling computers with neural signals | |
| CN101391129B (en) | Brain-machine interface intelligentized upper-limb recovery training device based on P300 signal and signal processing method | |
| CN107252525A (en) | A kind of multichannel electrical stimulation device based on EMG feedback | |
| CN106236503B (en) | The wearable exoskeleton system of the electrically driven (operated) upper limb of flesh and control method | |
| CN109366508A (en) | A kind of advanced machine arm control system and its implementation based on BCI | |
| WO2020118797A1 (en) | Prosthesis control method, apparatus, system and device, and storage medium | |
| CN103793058A (en) | Method and device for classifying active brain-computer interaction system motor imagery tasks | |
| Tang et al. | Single-trial classification of different movements on one arm based on ERD/ERS and corticomuscular coherence | |
| CN107928980A (en) | A kind of autonomous rehabilitation training system of the hand of hemiplegic patient and training method | |
| CN104001264B (en) | The electro stimulation treatment apparatus of electrode contact status monitoring can be carried out | |
| CN102184019B (en) | Method for audio-visual combined stimulation of brain-computer interface based on covert attention | |
| CN105496392B (en) | A kind of have electrode and connect the three of discrimination function and lead cardioelectric monitor system | |
| CN102799267B (en) | Multi-brain-computer interface method for three characteristics of SSVEP (Steady State Visual Evoked Potential), blocking and P300 | |
| CN110262658B (en) | Brain-computer interface character input system based on enhanced attention and implementation method | |
| CN100525854C (en) | Intelligent paralytic patient recovering aid system | |
| CN105920735A (en) | Physical and psychological recovery feedback training device | |
| WO2014194609A1 (en) | Control method based on electromyographic signal and sensor signal for implementing fine real-time motion | |
| CN105056396A (en) | Mirror evaluation training low-frequency electric stimulation rehabilitation system and use method | |
| CN103300853A (en) | Diagnosis and treatment system based on surface myoelectricity | |
| CN106267557A (en) | A kind of brain control based on wavelet transformation and support vector machine identification actively upper limb medical rehabilitation training system | |
| CN111584031A (en) | Brain-controlled intelligent limb rehabilitation system based on portable electroencephalogram acquisition equipment and application | |
| CN212941005U (en) | Myoelectric biofeedback therapeutic instrument | |
| CN107463250A (en) | The method for improving P300 spellings device using effect under Mental Workload state |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Open date: 20081112 |