CN110134240A - Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface - Google Patents
Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface Download PDFInfo
- Publication number
- CN110134240A CN110134240A CN201910397836.2A CN201910397836A CN110134240A CN 110134240 A CN110134240 A CN 110134240A CN 201910397836 A CN201910397836 A CN 201910397836A CN 110134240 A CN110134240 A CN 110134240A
- Authority
- CN
- China
- Prior art keywords
- head
- wheel chair
- module
- robot wheel
- control
- 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
Classifications
-
- 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
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Dermatology (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- Multimedia (AREA)
- User Interface Of Digital Computer (AREA)
- Manipulator (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The robot wheel chair control system based on brain electricity Yu head appearance hybrid interface that the invention discloses a kind of, including head appearance identification division, SSVEP electroencephalogramrecognition recognition part and robot wheel chair control system, brain electrical feature is extracted using canonical correlation analysis algorithm in SSVEP electroencephalogramrecognition recognition part；The head appearance identification division estimates head pose using the random forest combination closest approach alternative manner based on depth image；The robot wheel chair control system is converted into motor control order for that will control signal to drive robot wheel chair.The present invention controls robot wheel chair with SSVEP brain wave using user's head posture, and wherein SSVEP EEG signals are used to control the left-hand rotation of robot wheel chair, turn right, and head appearance signal is used to control the advance, retrogressing and speed control of robot wheel chair.The present invention enriches the function of robot wheel chair, facilitates the trip of physical disabilities and the elderly.
Description
Technical field
The present invention relates to brain-computer interface and field of machine vision more particularly to a kind of based on brain electricity and head appearance hybrid interface
Robot wheel chair control system.
Background technique
With the rapid development of society, aging is just being increasing year by year with disabled population in world population, high for part
The disabled person of position paraplegia, four limbs inconvenience but Clear consciousness, their travel activities in life will be extremely difficult, in order to facilitate this
The trip and life of class crowd, the research in terms of robot wheel chair became the direction of social concerns in recent years, wherein brain electricity
(EEG, Electroencephalography) and head attitude control robot wheel chair are even more research hotspot in recent years.
Control centre of the brain as behavioral activity, people can control external equipment by brain wave to realize oneself
Wish, especially Steady State Visual Evoked Potential (SSVEP, Steady-State Visual Evoked Potentials) task
Under EEG signals, this EEG signals can recognize classification it is more, without training and relative motion the imagination and P300 EEG signals come
It says more simple, can be easier to realize control to external equipment.In field of machine vision, researchers are captured using camera
The specific action on personage head is translated into control command, realizes the control to robot wheel chair.Both above auxiliary controls
Technology processed brings Gospel for numerous elderly and the disabled group.
Currently, the Research tendency to SSVEP in terms of robot wheel chair is mature both at home and abroad, but in practical application to deformity
People user, which lacks, to be considered, such as to real-time, the speed Control demand of wheelchair, therefore the safety of robot wheel chair in many cases
Property and practicability be not high.Wheelchair steerable system and its brain telecommunications based on brain-computer interface disclosed in patent CN200810053558.0
Number processing method, it can be achieved that the front, rear, left and right four direction to wheelchair control, but cannot achieve speed-regulating function, and
During long-time Manipulation of the machine people's wheelchair, it be easy to cause the musculi colli fatigue and secondary damage of disabled user.
Summary of the invention
Goal of the invention: in view of the above problems, the present invention proposes a kind of robot wheel based on brain electricity Yu head appearance hybrid interface
Chair control system realizes that the left and right direction controlling to robot wheel chair, head pose realize robot wheel chair using EEG signals
Front and rear direction control and speed-regulating function, both control methods are fused in a control system, realize to robot wheel
The interactive controlling of chair.
Technical solution: to achieve the purpose of the present invention, the technical scheme adopted by the invention is that: one kind is based on brain electricity and head
The robot wheel chair control system of appearance hybrid interface, including head appearance acquisition module, brain wave acquisition module, head appearance identification module, brain
Electric identification module and control module；The brain wave acquisition module acquires eeg data, and is sent to electroencephalogramrecognition recognition module；The head
Appearance acquisition module acquires head depth image, and is sent to an appearance identification module；The electroencephalogramrecognition recognition module uses canonical correlation
Parser extracts brain electrical feature, and identification specific brain regions electrical feature is used to control the left-hand rotation, right-hand rotation and stopping of robot wheel chair, and will
Recognition result is sent to direction controlling module；The head appearance identification module uses Random Forest model combination iterative closest point algorithm
Head pose is detected, identification particular header posture is used to control advance, retrogressing, acceleration and the deceleration of robot wheel chair, and will know
Other result is sent to direction and rate control module；The control module includes direction controlling module and rate control module, is used
In converting motor control order for the control signal of recognition result to drive robot wheel chair.
Further, the brain wave acquisition module be NeuroScan electroencephalogramdata data collector and connected Quik-Cap electrode cap,
The head appearance acquisition module is Kinect camera.
Further, the electroencephalogramrecognition recognition module and head appearance identification module use PC controller, the direction controlling module
Dsp controller is used with rate control module.
Further, the electroencephalogramrecognition recognition be based on comprising steps of
(1) 0.01-100hz bandpass filtering is set using Scan4.5 brain wave acquisition software, and rejects 50hz Hz noise；
(2) for the frequency of stimulation of all presentations, fundamental frequency and its 2 overtones band are taken, constructs the template of 3 frequencies in advance；
(3) stimulation interface is presented with MATLAB software, user watches some in interface attentively according to the frequency module of fixed flashing,
And eeg data is acquired simultaneously；
(4) EEG signals data segment is extracted, using canonical correlation analysis algorithm, online recognition is carried out to EEG signals,
Corresponding correlation coefficient value is obtained, frequency corresponding to maximum coefficient value ρ is to differentiate result.One is set to correlation coefficient value
Threshold value, when ρ value is less than 0.2, it is invalid to differentiate.
Further, head appearance identification be based on comprising steps of
(1) it acquires head depth image and pre-processes；
(2) it constructs the Random Forest model of head pose estimation and detects head pose；
(3) the head point cloud model of user is constructed in advance；
(4) head pose is detected using Random Forest model combination iterative closest point algorithm.
The utility model has the advantages that the interactive controlling to robot wheel chair is realized in present invention combination brain electricity and the recognition methods of head appearance, for
Height hemiplegia and the elderly group can alleviate musculi colli fatigue, reduce secondary damage, solve machine when manipulating wheelchair
The problem of freedom degree deficiency in device people's wheelchair control system, and can realize the interactive controlling of an appearance Yu brain power mode.
Detailed description of the invention
Fig. 1 is robot wheel chair structure chart；
Fig. 2 is robot wheel chair intersection control routine block diagram；
Fig. 3 is the decision controller schematic diagram based on finite state machine；
Fig. 4 is SSVEP stimulation surface chart；
Fig. 5 is SSVEP recognizer flow chart；
Fig. 6 is an appearance control system flow chart；
Fig. 7 is an attitude control interface schematic diagram.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawings and examples.
Robot wheel chair control system of the present invention based on brain electricity Yu head appearance hybrid interface mainly includes two kinds of moulds
Formula: brain electric control method and head appearance control mode.The present invention combines two kinds of control methods of brain electric control and head attitude control system,
Robot wheel chair increases speed-regulating function on the basis of realizing direction controlling.
Robot wheel chair control system of the present invention based on brain electricity Yu head appearance hybrid interface, including head appearance acquire mould
Block, brain wave acquisition module, head appearance identification module, electroencephalogramrecognition recognition module and control module.
Brain wave acquisition module acquires eeg data, and is sent to electroencephalogramrecognition recognition module；Head appearance acquisition module acquisition head is deep
Image is spent, and is sent to an appearance identification module；Electroencephalogramrecognition recognition module extracts brain electrical feature, identification using canonical correlation analysis algorithm
Specific brain regions electrical feature is used to control the left-hand rotation, right-hand rotation and stopping of robot wheel chair, and sends direction controlling mould for recognition result
Block；Head appearance identification module detects head pose using Random Forest model combination iterative closest point algorithm, identifies particular header appearance
State is used to control advance, retrogressing, acceleration and the deceleration of robot wheel chair, and sends direction and speed control mould for recognition result
Block.
By analysis SSVEP brain electric information and head appearance information, output control instruction is converted by recognition result, is applied to machine
The control of device people's wheelchair.Under brain electric control mode, left-hand rotation, the right side of control robot wheel chair are realized by SSVEP EEG signals
Turn and stops；Under head appearance control model, using head appearance posture realize the control advance of robot wheel chair, retrogressing, acceleration and
Slow down.
Control module includes direction controlling module and rate control module, for converting the control signal of recognition result to
Motor control order is to drive robot wheel chair.
Brain wave acquisition module is NeuroScan electroencephalogramdata data collector and connected Quik-Cap electrode cap, head appearance acquisition module
For Kinect camera.Electroencephalogramrecognition recognition module and head appearance identification module use PC controller, direction controlling module and speed control
Module uses dsp controller.
It is as shown in Figure 1 the structure chart of robot wheel chair, robot wheel chair 100 has a main body and is arranged in main body
On each component, these components include: headrest 101, Kinect camera 102, PC controller 103, control stick 104, motor
105, battery 106, front-wheel 107, rear-wheel 108 and anti-hypsokinesis wheel 109.
Kinect camera 102 is mounted on the front about 50cm on user head or so place, it is ensured that being capable of face head
And be included in entire head in the picture of acquisition, distance 50cm or so can preferably acquire the depth image comprising head,
PC controller is located at below camera and face user.User wears 64 leads being connected with NeuroScan electroencephalogramdata data collector
For acquiring eeg data, electroencephalograph can amplify original eeg data Quik-Cap electrode cap, filter and analog-to-digital conversion
Processing；Kinect camera 102 is used to acquire the depth image on head；PC controller is used for eeg data and head depth map
Pass through the control module that communication module is sent to robot wheel chair as being handled, and by recognition result.
If Fig. 2 is robot wheel chair intersection control routine block diagram, the present invention is mainly by head appearance identification division, SSVEP brain electricity
Identification division and robot wheel chair control system three parts composition.Below by taking robot wheel chair control process as an example, according to thing
The brain wave frequency and head pose first defined, when the end PC detects that specific brain regions electrical feature or head pose detect specific change
When, system adjusts the motion state of robot wheel chair according to recognition result.In the present invention, SSVEP EEG signals are being known
The status switch generated during not, the present invention devise the decision controller based on finite state machine, as shown in figure 3,
To be used to respond different brain electricity events.As shown in table 1, event flag is as shown in table 2 for state definition in the state machine.
Table 1
| State | Explanation |
| Q0 | Wheelchair starting |
| Q1 | Wheelchair bends to right |
| Q2 | Wheelchair turns round to the left |
| Q3 | Wheelchair stops |
Table 2
In robot wheel chair control process, by detecting to User Status, the state of online recognition user is obtained
Output order is to control the motion state of robot wheel chair.
Under brain electric control mode, be illustrated in figure 4 stimulation interface schematic diagram, when detect watch attentively stimulation white piece 1 of block when,
Right-hand rotation control command should be exported；When detecting that user watches white piece 2 of stimulation attentively, left-hand rotation control command should be exported；When detecting use
When family watches white piece 3 of stimulation attentively, stopping control command should being exported.
Under head appearance control model, when user's head face camera works as coordinate points according to current head coordinate (x, y)
It when positioned at the first and second quadrants, detects that new line acts, motor output should be controlled and advance and accelerate control instruction；Work as coordinate points
When positioned at the third and fourth quadrant, movement of bowing is detected, should control motor output retrogressing and deceleration control instruction.Such as Fig. 7 institute
Show, when the intersection point in a certain moment head towards vector and the section is in point (x1,y1) at when, the movement of wheelchair should be controlled at this time
Speed isIn addition, a certain range of redundant area is arranged in section, to also allow to make when wheelchair is static
The head small range of user rotates, and the control interface schematic diagram of two concentric circles composition is illustrated in figure 7, including the coordinate of head
Wheelchair is remain stationary when in circle, then calculates exercise data when head is in annular region.
For brain electric control mode, SSVEP brain computator method flow chart, this control system mainly include brain as shown in connection with fig. 5
Electric acquisition module, brain electric treatment module, robot wheel chair control module, robot wheel chair.Wherein, brain wave acquisition module is
Quik-Cap electrode cap and NeuroScan electroencephalogramdata data collector, brain electric treatment module use PC controller, and robot wheel chair controls mould
Block uses DSP, is controlled robot wheel chair by PC controller the frequency information obtained after EEG Processing.Tool
Body realizes that steps are as follows:
(1) before acquiring data, 0.01-100hz bandpass filtering is set using Scan4.5 brain wave acquisition software, and set
It sets and rejects 50hz Hz noise；For the frequency of stimulation of all presentations, fundamental frequency and its 2 overtones band are taken, utilizes the preparatory structure of MATLAB
Make the template of 3 frequencies；
(2) for SSVEP brain electricity part, stimulation interface is presented with MATLAB software, as shown in figure 4, user watches interface attentively
In some according to the frequency module of fixed flashing, and acquire eeg data simultaneously；
(3) EEG signals data segment is extracted, the present invention has intercepted the 3s time during SSVEP watches attentively, since 0s, with
The 1s time is a data segment (0-1s, 0.5-1.5s, 1-2s, 1.5-2.5s, 2-3s), and 0.5s is sliding window size, can be with
5 data segments are obtained, using canonical correlation analysis (CCA) algorithm, online recognition is carried out to EEG signals, obtains corresponding phase
Coefficient values, frequency corresponding to maximum coefficient value ρ are to differentiate as a result, in addition, the correlation coefficient value to output sets one
A threshold value indicates that this recognition effect is bad when ρ value is less than 0.2, and it is invalid to differentiate, increases the appearance during electroencephalogramrecognition recognition
Error rate prevents from judging by accident.
Under brain power mode, after user watches some white piece of stimulation attentively, the frequency signal that will test by electroencephalogramrecognition recognition algorithm
It is sent to interface circuit, control motor executes corresponding left-hand rotation, turns right and cease and desist order.
For head appearance control model, head appearance control system flow chart as shown in connection with fig. 6 mainly includes in this control system
Image capture module, image processing module, robot wheel chair control module and robot wheel chair.Wherein, image capture module is
Kinect camera, image processing module use PC controller, and robot wheel chair control module uses DSP, passes through PC controller
Advanced to the head pose information identified after image procossing to robot wheel chair, retreated and speed regulating control.Specifically
Realize that steps are as follows:
(1) pass through depth image of the depth transducer acquisition comprising head and pretreatment；
(2) it constructs the Random Forest model of head pose estimation and detects head pose；
(3) the head point cloud model of user is constructed in advance；
(4) head pose is detected using random forest combination ICP algorithm；
(5) control to robot wheel chair is realized in such a way that head rotation simulates control rocking bar.
User keeps right sitting position gesture in robot wheel chair, and Kinect camera prevents 50 centimetres or so the positions in front of head
Set, after opening head appearance control model, user can by coming back, the mode of bowing realize the advance, retrogressing and speed regulation of robot wheel chair
Function.
Claims (6)
1. a kind of robot wheel chair control system based on brain electricity Yu head appearance hybrid interface, which is characterized in that acquired including head appearance
Module, brain wave acquisition module, head appearance identification module, electroencephalogramrecognition recognition module and control module；
The brain wave acquisition module acquires eeg data, and is sent to electroencephalogramrecognition recognition module；The head appearance acquisition module collection head
Portion's depth image, and it is sent to an appearance identification module；
The electroencephalogramrecognition recognition module extracts brain electrical feature using canonical correlation analysis algorithm, and identification specific brain regions electrical feature is for controlling
Left-hand rotation, right-hand rotation and the stopping of robot wheel chair, and direction controlling module is sent by recognition result；
The head appearance identification module detects head pose using Random Forest model combination iterative closest point algorithm, identifies specific head
Portion's posture is used to control the advance, retrogressing and speed of robot wheel chair, and sends speed and direction controlling mould for recognition result
Block；
The control module includes direction controlling module and rate control module, for converting the control signal of recognition result to
Motor control order is to drive robot wheel chair.
2. the robot wheel chair control system according to claim 1 based on brain electricity Yu head appearance hybrid interface, feature exist
In the brain wave acquisition module is NeuroScan electroencephalogramdata data collector and connected Quik-Cap electrode cap, and the head appearance acquires mould
Block is Kinect camera.
3. the robot wheel chair control system according to claim 1 based on brain electricity Yu head appearance hybrid interface, feature exist
In the electroencephalogramrecognition recognition module and head appearance identification module use PC controller, the direction controlling module and rate control module
Use dsp controller.
4. the robot wheel chair control system according to claim 1 based on brain electricity Yu head appearance hybrid interface, feature exist
Be based in, the electroencephalogramrecognition recognition comprising steps of
(1) 0.01-100hz bandpass filtering is set using Scan4.5 brain wave acquisition software, and rejects 50hz Hz noise；
(2) for the frequency of stimulation of all presentations, fundamental frequency and its 2 overtones band is taken, the template of 3 frequencies is constructed；
(3) use MATLAB software that stimulation interface is presented, user watches some in interface attentively according to the frequency module of fixed flashing, and together
When acquire eeg data；
(4) EEG signals data segment is extracted, using canonical correlation analysis algorithm, online recognition is carried out to EEG signals, is obtained
Corresponding correlation coefficient value, frequency corresponding to maximum coefficient value ρ are to differentiate result.
5. the robot wheel chair control system according to claim 4 based on brain electricity Yu head appearance hybrid interface, feature exist
In to correlation coefficient value one threshold value of setting, when ρ value is less than 0.2, it is invalid to differentiate.
6. the robot wheel chair control system according to claim 1 based on brain electricity Yu head appearance hybrid interface, feature exist
Be based in, head appearance identification comprising steps of
(1) it acquires head depth image and pre-processes；
(2) it constructs the Random Forest model of head pose estimation and detects head pose；
(3) the head point cloud model of user is constructed in advance；
(4) head pose is detected using Random Forest model combination iterative closest point algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910397836.2A CN110134240A (en) | 2019-05-14 | 2019-05-14 | Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910397836.2A CN110134240A (en) | 2019-05-14 | 2019-05-14 | Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110134240A true CN110134240A (en) | 2019-08-16 |
Family
ID=67573838
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910397836.2A Pending CN110134240A (en) | 2019-05-14 | 2019-05-14 | Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110134240A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110515465A (en) * | 2019-08-29 | 2019-11-29 | 张洋 | Control method and device based on brain wave and gesture recognition |
| CN110658742A (en) * | 2019-09-05 | 2020-01-07 | 四川省康复辅具技术服务中心 | Multi-mode cooperative control wheelchair control system and method |
| CN113616436A (en) * | 2021-08-23 | 2021-11-09 | 南京邮电大学 | Intelligent wheelchair based on motor imagery electroencephalogram and head posture and control method |
| CN115590695A (en) * | 2022-10-08 | 2023-01-13 | 华南脑控（广东）智能科技有限公司（Cn） | Wheelchair control system based on electro-oculogram and face recognition |
| CN115804695A (en) * | 2023-01-09 | 2023-03-17 | 华南脑控(广东)智能科技有限公司 | Multi-modal brain-computer interface wheelchair control system integrating double attitude sensors |
| CN112230768B (en) * | 2020-09-30 | 2023-05-23 | 深圳睿瀚医疗科技有限公司 | Wheelchair driven by SSMVEP-ERP-OSR hybrid brain-computer interface |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103263324A (en) * | 2013-05-06 | 2013-08-28 | 西安电子科技大学 | Intelligent wheelchair system based on SSVEP (steady-state visual evoked potential) |
| CN105534648A (en) * | 2016-01-14 | 2016-05-04 | 马忠超 | Wheelchair control method and control device based on brain waves and head movements |
| CN107066093A (en) * | 2017-03-22 | 2017-08-18 | 南京邮电大学 | A kind of SSVEP intelligent home service systems based on improvement CCA |
| CN107621880A (en) * | 2017-09-29 | 2018-01-23 | 南京邮电大学 | A kind of robot wheel chair interaction control method based on improvement head orientation estimation method |
-
2019
- 2019-05-14 CN CN201910397836.2A patent/CN110134240A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103263324A (en) * | 2013-05-06 | 2013-08-28 | 西安电子科技大学 | Intelligent wheelchair system based on SSVEP (steady-state visual evoked potential) |
| CN105534648A (en) * | 2016-01-14 | 2016-05-04 | 马忠超 | Wheelchair control method and control device based on brain waves and head movements |
| CN107066093A (en) * | 2017-03-22 | 2017-08-18 | 南京邮电大学 | A kind of SSVEP intelligent home service systems based on improvement CCA |
| CN107621880A (en) * | 2017-09-29 | 2018-01-23 | 南京邮电大学 | A kind of robot wheel chair interaction control method based on improvement head orientation estimation method |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110515465A (en) * | 2019-08-29 | 2019-11-29 | 张洋 | Control method and device based on brain wave and gesture recognition |
| CN110658742A (en) * | 2019-09-05 | 2020-01-07 | 四川省康复辅具技术服务中心 | Multi-mode cooperative control wheelchair control system and method |
| CN112230768B (en) * | 2020-09-30 | 2023-05-23 | 深圳睿瀚医疗科技有限公司 | Wheelchair driven by SSMVEP-ERP-OSR hybrid brain-computer interface |
| CN113616436A (en) * | 2021-08-23 | 2021-11-09 | 南京邮电大学 | Intelligent wheelchair based on motor imagery electroencephalogram and head posture and control method |
| CN113616436B (en) * | 2021-08-23 | 2024-01-16 | 南京邮电大学 | Intelligent wheelchair based on motor imagery electroencephalogram and head gesture and control method |
| CN115590695A (en) * | 2022-10-08 | 2023-01-13 | 华南脑控（广东）智能科技有限公司（Cn） | Wheelchair control system based on electro-oculogram and face recognition |
| CN115804695A (en) * | 2023-01-09 | 2023-03-17 | 华南脑控(广东)智能科技有限公司 | Multi-modal brain-computer interface wheelchair control system integrating double attitude sensors |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110134240A (en) | Robot wheel chair control system based on brain electricity Yu head appearance hybrid interface | |
| US11602300B2 (en) | Brain-computer interface based robotic arm self-assisting system and method | |
| CN107957783B (en) | Multi-mode intelligent control system and method based on electroencephalogram and electromyogram information | |
| CN109521880B (en) | Teleoperation robot system and method based on mixed bioelectricity signal driving | |
| Lv et al. | Implementation of the EOG-based human computer interface system | |
| CN102309366B (en) | Control system and control method for controlling upper prosthesis to move by using eye movement signals | |
| CN113616436B (en) | Intelligent wheelchair based on motor imagery electroencephalogram and head gesture and control method | |
| CN112114670B (en) | Man-machine co-driving system based on hybrid brain-computer interface and control method thereof | |
| CN103349595A (en) | Intelligent brain-computer interface wheelchair based on multi-mode hierarchical control | |
| CN106491251B (en) | Non-invasive brain-computer interface-based robot arm control system and control method thereof | |
| CN110824979A (en) | Unmanned equipment control system and method | |
| CN110179460A (en) | Device of waking up is detected and promoted based on the brainfag of eye electricity and head pose | |
| CN111571587A (en) | Brain-controlled mechanical arm dining assisting system and method | |
| CN105137830A (en) | Traditional Chinese painting mechanical hand based on visual evoking brain-machine interface, and drawing method thereof | |
| CN110716578A (en) | Aircraft control system based on hybrid brain-computer interface and control method thereof | |
| Ianez et al. | Multimodal human-machine interface based on a brain-computer interface and an electrooculography interface | |
| CN113156861A (en) | Intelligent wheelchair control system | |
| KR101632830B1 (en) | Apparatus for Controlling Driving of Vehicle | |
| CN113359991A (en) | Intelligent brain-controlled mechanical arm auxiliary feeding system and method for disabled people | |
| WO2022099807A1 (en) | Robot natural control method based on electromyographic signal and error electroencephalographic potential | |
| Liu et al. | Detection of lower-limb movement intention from EEG signals | |
| Nayak et al. | Development of an EOG based computer aided communication support system | |
| Ravirahul et al. | Mind wave controlled assistive robot | |
| Liu et al. | Mind controlled vehicle based on lidar SLAM navigation and SSVEP technology | |
| CN110051351B (en) | Tooth biting signal acquisition method and control method and device of electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190816 |
|
| RJ01 | Rejection of invention patent application after publication |