CN111007725A - Method for controlling intelligent robot based on electroencephalogram neural feedback - Google Patents
Method for controlling intelligent robot based on electroencephalogram neural feedback Download PDFInfo
- Publication number
- CN111007725A CN111007725A CN201911333102.4A CN201911333102A CN111007725A CN 111007725 A CN111007725 A CN 111007725A CN 201911333102 A CN201911333102 A CN 201911333102A CN 111007725 A CN111007725 A CN 111007725A
- Authority
- CN
- China
- Prior art keywords
- robot
- computer
- speed
- electroencephalogram
- brain
- 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
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000001537 neural effect Effects 0.000 title claims abstract description 13
- 230000000638 stimulation Effects 0.000 claims abstract description 48
- 238000012360 testing method Methods 0.000 claims abstract description 40
- 210000004556 brain Anatomy 0.000 claims abstract description 29
- 230000003993 interaction Effects 0.000 claims description 13
- 230000002452 interceptive effect Effects 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 5
- 238000011217 control strategy Methods 0.000 claims description 4
- 238000010219 correlation analysis Methods 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 230000000763 evoking effect Effects 0.000 claims description 3
- 239000004973 liquid crystal related substance Substances 0.000 claims description 3
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims 1
- 230000004936 stimulating effect Effects 0.000 abstract 2
- 238000011160 research Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 210000005036 nerve Anatomy 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 210000000578 peripheral nerve Anatomy 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention relates to a method for controlling an intelligent robot based on electroencephalogram neural feedback, and belongs to the field of brain science. The invention comprises a computer, a routing system, a robot testing platform and an electroencephalogram acquisition system; the computer screen presents an SSVEP stimulation paradigm, and the robot testing platform is connected to the brain-computer interface client and starts timing; a tested person observes a robot and obstacles on a robot testing platform screen, plans a robot motion path, watches a stimulating target corresponding to an expected motion direction and speed on a computer screen, converts an acquired electroencephalogram signal into a digital signal by a wireless electroencephalogram amplifier in an electroencephalogram acquisition system, synchronously sends the digital signal to a computer, analyzes and identifies the received electroencephalogram signal by the computer, converts the identified stimulating target into a control instruction corresponding to the received electroencephalogram signal, and sends the control instruction to the robot testing platform. The invention not only provides the function of simultaneously controlling the direction and the speed of the robot, but also provides the function of controlling a plurality of movement directions and multi-stage speeds of the robot.
Description
Technical Field
The invention relates to a method for controlling an intelligent robot based on electroencephalogram neural feedback, and belongs to the technical field of brain science.
Background
Brain-computer interface/Brain-computer interaction (BCI/BMI) is the participation of peripheral nerves and muscles around the Brain, and the Brain signals realize direct communication and control between the Brain and external devices, and an important application of the BCR in the field of robot control is Brain-controlled robot (BCR) technology, namely, research for controlling a robot by sensing and thinking intentions. The research is cross-disciplinary research of brain science, information science and control science, and has become an international important frontier breakthrough at present. The technology is expected to be used for national defense and military purposes strategically, provide a new communication and control channel for the severely disabled patients, improve the quality of life of the severely disabled patients, provide brain-controlled robots or external equipment for healthy users under special conditions and improve the quality of life of the severely disabled patients.
The traditional brain-controlled robot mainly realizes simple direction control of the robot and is difficult to realize speed control of the robot. However, in practical applications, flexible control of the direction and speed of the device is required, and these requirements present a great challenge. Most of the existing brain-computer interactive paradigms based on Motor Imagery (MI) brain electricity are simple Motor imagery modes, only small instruction sets are provided, and the control requirements of flexible movement of a robot on multiple directions and multiple speeds are difficult to meet. In addition, a practical brain-controlled robot system should satisfy the requirement that most users need less or no training to realize control. However, the brain-computer interaction performance based on the motor imagery mode has great variability both within the subject (the ability of the same subject motor imagery and its state over time) and between subjects (the ability of different individual motor imagery), and studies have shown that there is a serious BCI blindness problem.
In addition to the above brain-computer interaction based on motor imagery, although the number of recognition targets of the brain-computer interaction based on P300 may exceed 30, at least 2 repetition times are required to ensure recognition accuracy, and it is difficult to perform single recognition, and the real-time performance of the brain-computer robot motion is limited. Compared with the two paradigms, the brain-computer interaction based on SSVEP not only can identify more targets (more than 40 targets), but also can provide a large instruction set (more fine motion control instructions) so as to meet the hierarchical control of the brain-computer robot flexible motion on the direction and speed; and this type of brain-computer interaction requires less adaptive training to be tested.
Therefore, the research plans to adopt a brain-computer interaction method based on SSVEP to directly control the direction and the speed of the robot, is more generally applicable to the paradigm of character input, is effectively used for a brain-controlled robot interface, optimizes the design and control strategy of the paradigm according to the requirements of the flexible motion of the robot on the direction and the speed, and properly sets the SSVEP stimulation target number and the layout thereof; then, a typical correlation analysis (CCA) method suitable for SSVEP decoding is employed in conjunction with the optimized stimulation paradigm. In addition, to overcome the limitations of SSVEP, preliminary studies on SSVEP in combination with MI to control the orientation and velocity of the robot have been conducted. The invention is expected to provide elicitation for the research and application of the brain-computer interaction based on SSVEP or mixed with MI for the complex and flexible movement of the brain-controlled robot, and lays a certain foundation for promoting the practical application of the direct brain-controlled robot technology.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides a method for controlling an intelligent robot based on electroencephalogram nerve feedback, which is used for simultaneously controlling multiple motion directions and multiple speeds of the robot.
The technical scheme of the invention is as follows: a method for controlling an intelligent robot based on electroencephalogram nerve feedback comprises a computer 1, a routing system 2, a robot testing platform 3 and an electroencephalogram acquisition system 4, wherein the computer is used for realizing hierarchical control on the motion direction and speed of the robot;
for controlling the moving direction and speed of the robot and a plurality of moving directions and multi-stage speeds of the robot simultaneously by brain: firstly, a computer 1 starts a brain-computer interface client program, a screen presents an SSVEP stimulation mode, and a robot testing platform 3 is connected to the brain-computer interface client and starts timing to finish preparation work; then, a testee observes a robot and obstacles on a screen of the robot test platform 3 and plans a robot motion path, then watches a stimulation target corresponding to an expected motion direction and speed on a screen of the computer 1, a wireless electroencephalogram amplifier in the electroencephalogram acquisition system 4 converts acquired electroencephalogram signals (analog signals) into digital signals, the digital signals are synchronously transmitted to the computer 1 through the routing system 2 (the time precision is less than 1 millisecond), the computer 1 analyzes and identifies the received electroencephalogram signals, and the computer 1 converts the identified stimulation target into a control instruction corresponding to the stimulation target and transmits the control instruction to the robot test platform 3 through the routing system 2; finally, the testee continues to observe the motion path of the robot, plans the next motion path and control strategy according to the motion state, position and peripheral obstacles of the robot, and controls the motion direction and speed of the robot by watching the stimulation target corresponding to the expected motion direction and speed on the screen of the computer 1 again, and the steps are repeated to finally enable the robot to reach the target position; thereby realizing the hierarchical control of the moving direction and speed of the robot.
Further, the computer 1 can brain-control the robot direction and speed simultaneously; the computer 1 adopts a steady-state visual evoked potential (SSVEP) brain-computer interaction method, plans 4 directions of left, right, forward and backward for the movement of the brain-controlled robot, designs 3-stage movement speeds of low speed, medium speed and high speed and combines 9 brain control instructions, and the 9 brain control instructions are respectively 'low-speed forward', 'medium-speed forward', 'high-speed forward', 'low-speed backward', 'medium-speed backward', 'low-speed left turn', 'medium-speed left turn', 'low-speed right turn', 'medium-speed right turn'; the computer 1 performs identification by using a typical correlation analysis (CCA); the testing steps of the robot testing platform 3 are as follows: starting from a starting point, bypassing the barrier and finally reaching an end point; the computer 1 and the robot testing platform 3 work synchronously.
Further, the electroencephalogram acquisition system 4 acquires 32 channels of electroencephalogram signals together, the sampling frequency is 250 Hz, the recording electrodes are Pz, P3, P4, PO3, PO4, PO7, O1, Oz, O2, PO8 and a reference electrode Cz, the grounding electrode is FPz, and the electrode impedance is below 5 k ohms; the communication between the robot testing platform 3 and the BCI system adopts a client/server structure; the robot testing platform 3 is a server side, the computer 1 is a client side, and the robot testing platform 3 and the computer 1 are connected through TCP/IP.
Further, the SSVEP brain-computer interactive paradigm on the computer 1 is implemented using the psychdolox (ptb) toolbox of Matlab; the refresh rate of the liquid crystal display is 60 frames/second, and the resolution is 1366x768 (pixels); the stimulation target consists of 9 squares of size 150x150 (pixels); each stimulation target is modulated by a specific frequency, and the flicker frequency of the stimulation targets from left to right and from top to bottom is [ 8129139.5101410.515 ] Hz; at the beginning, the stimulation interface is firstly statically presented for 3 seconds, and 9 stimulation targets flicker with the frequency after 3 seconds; the converted control instruction on the computer 1 consists of 13 characters; the robot testing platform 3 automatically records the time and the times of touching the obstacle; penalty 5 seconds per collision; all codes of the method for controlling the intelligent robot based on the electroencephalogram neural feedback are realized through VC + + programming.
Further, there are three different stimulation paradigms on the computer 1; the difference between the three stimulation paradigms is the spacing between the stimulation targets, where the horizontal and vertical spacing between the paradigms-stimulation targets are both 10; the horizontal and vertical spacing between the paradigm two stimulation targets is 100; the horizontal spacing between the paradigm tri-stimulus targets is 428, and the vertical spacing is 198; the presentation time of the three paradigm stimuli was set to 3 seconds, and the visual transition time (i.e., the interval time) was set to 1.5 seconds.
The invention has the beneficial effects that: the invention not only provides the function of simultaneously controlling the direction and the speed of the robot, but also provides the function of controlling a plurality of movement directions and multi-stage speeds of the robot.
Drawings
FIG. 1 is a schematic structural diagram of a direct brain-controlled robot system based on SSVEP brain-computer interaction according to the present invention;
FIG. 2 is a block diagram of the direct brain-controlled robotic system of the present invention;
FIG. 3 is a schematic view of the robotic test platform of the present invention;
FIG. 4 is a first SSVEP brain-computer interaction stimulation paradigm in accordance with the present invention;
FIG. 5 is a second SSVEP brain-computer interactive stimulation paradigm in accordance with the present invention;
FIG. 6 is a SSVEP brain-computer interactive stimulation paradigm three of the present invention.
The respective reference numerals in fig. 1: 1-a computer; 2-a routing system; 3-a robot test platform; 4-electroencephalogram acquisition system.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Example 1: as shown in fig. 1-6, a method for controlling an intelligent robot based on electroencephalogram neural feedback comprises a computer 1, a routing system 2, a robot testing platform 3 and an electroencephalogram acquisition system 4, wherein the computer is used for realizing hierarchical control of the motion direction and speed of the robot;
for controlling the moving direction and speed of the robot and a plurality of moving directions and multi-stage speeds of the robot simultaneously by brain: firstly, a computer 1 starts a brain-computer interface client program, a screen presents an SSVEP stimulation mode, and a robot testing platform 3 is connected to the brain-computer interface client and starts timing to finish preparation work; then, a testee observes a robot and obstacles on a screen of the robot test platform 3 and plans a robot motion path, then watches a stimulation target corresponding to an expected motion direction and speed on a screen of the computer 1, a wireless electroencephalogram amplifier in the electroencephalogram acquisition system 4 converts acquired electroencephalogram signals (analog signals) into digital signals, the digital signals are synchronously transmitted to the computer 1 through the routing system 2 (the time precision is less than 1 millisecond), the computer 1 analyzes and identifies the received electroencephalogram signals, and the computer 1 converts the identified stimulation target into a control instruction corresponding to the stimulation target and transmits the control instruction to the robot test platform 3 through the routing system 2; finally, the testee continues to observe the motion path of the robot, plans the next motion path and control strategy according to the motion state, position and peripheral obstacles of the robot, and controls the motion direction and speed of the robot by watching the stimulation target corresponding to the expected motion direction and speed on the screen of the computer 1 again, and the steps are repeated to finally enable the robot to reach the target position; thereby realizing the hierarchical control of the moving direction and speed of the robot.
Further, the computer 1 can brain-control the robot direction and speed simultaneously; the computer 1 adopts a steady-state visual evoked potential (SSVEP) brain-computer interaction method, plans 4 directions of left, right, forward and backward for the movement of the brain-controlled robot, designs 3-stage movement speeds of low speed, medium speed and high speed and combines 9 brain control instructions, and the 9 brain control instructions are respectively 'low-speed forward', 'medium-speed forward', 'high-speed forward', 'low-speed backward', 'medium-speed backward', 'low-speed left turn', 'medium-speed left turn', 'low-speed right turn', 'medium-speed right turn'; the computer 1 performs identification by using a typical correlation analysis (CCA); the testing steps of the robot testing platform 3 are as follows: starting from a starting point, bypassing the barrier and finally reaching an end point; the computer 1 and the robot testing platform 3 work synchronously.
Further, the electroencephalogram acquisition system 4 acquires 32 channels of electroencephalogram signals together, the sampling frequency is 250 Hz, the recording electrodes are Pz, P3, P4, PO3, PO4, PO7, O1, Oz, O2, PO8 and a reference electrode Cz, the grounding electrode is FPz, and the electrode impedance is below 5 k ohms; the communication between the robot testing platform 3 and the BCI system adopts a client/server structure; the robot testing platform 3 is a server side, the computer 1 is a client side, and the robot testing platform 3 and the computer 1 are connected through TCP/IP.
Further, the SSVEP brain-computer interactive paradigm on the computer 1 is implemented using the psychdolox (ptb) toolbox of Matlab; the refresh rate of the liquid crystal display is 60 frames/second, and the resolution is 1366x768 (pixels); the stimulation target consists of 9 squares of size 150x150 (pixels); each stimulation target is modulated by a specific frequency, and the flicker frequency of the stimulation targets from left to right and from top to bottom is [ 8129139.5101410.515 ] Hz; at the beginning, the stimulation interface is firstly statically presented for 3 seconds, and 9 stimulation targets flicker with the frequency after 3 seconds; the converted control instruction on the computer 1 consists of 13 characters; the robot testing platform 3 automatically records the time and the times of touching the obstacle; penalty 5 seconds per collision; all codes of the method for controlling the intelligent robot based on the electroencephalogram neural feedback are realized through VC + + programming.
Further, there are three different stimulation paradigms on the computer 1; the difference between the three stimulation paradigms is the spacing between the stimulation targets, where the horizontal and vertical spacing between the paradigms-stimulation targets are both 10; the horizontal and vertical spacing between the paradigm two stimulation targets is 100; the horizontal spacing between the paradigm tri-stimulus targets is 428, and the vertical spacing is 198; the presentation time of the three paradigm stimuli was set to 3 seconds, and the visual transition time (i.e., the interval time) was set to 1.5 seconds.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes and modifications can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (5)
1. A method for controlling an intelligent robot based on electroencephalogram neural feedback is characterized by comprising the following steps: the robot test system comprises a computer (1), a routing system (2), a robot test platform (3) and an electroencephalogram acquisition system (4) and is used for realizing the hierarchical control of the movement direction and speed of the robot;
for controlling the moving direction and speed of the robot and a plurality of moving directions and multi-stage speeds of the robot simultaneously by brain: firstly, a computer (1) starts a brain-computer interface client program, a screen presents an SSVEP stimulation mode, and a robot testing platform (3) is connected to the brain-computer interface client and starts timing to finish preparation work; then, a testee observes a robot and obstacles on a screen of a robot testing platform (3) and plans a robot motion path, then watches a stimulation target corresponding to an expected motion direction and speed on a screen of a computer (1), a wireless electroencephalogram amplifier in an electroencephalogram acquisition system (4) converts acquired electroencephalogram signals into digital signals and synchronously sends the digital signals to the computer (1) through a routing system (2), the computer (1) analyzes and identifies the received electroencephalogram signals, and the computer (1) converts the identified stimulation target into a control instruction corresponding to the stimulation target and sends the control instruction to the robot testing platform (3) through the routing system (2); finally, the testee continues to observe the motion path of the robot, plans the next motion path and control strategy according to the motion state, position and peripheral obstacles of the robot, and then controls the motion direction and speed of the robot by watching a stimulation target corresponding to the expected motion direction and speed on the screen of the computer (1) again, and the steps are repeated to finally enable the robot to reach the target position; thereby realizing the hierarchical control of the moving direction and speed of the robot.
2. The method for controlling the intelligent robot based on the electroencephalogram neural feedback, according to claim 1, is characterized in that: the computer (1) can simultaneously brain control the direction and the speed of the robot; the computer (1) adopts a steady-state visual evoked potential (SSVEP) brain-computer interaction method, 4 directions of left, right, forward and backward are planned for the movement of the brain-controlled robot, 3-stage movement speeds of low speed, medium speed and high speed are designed, 9 brain control instructions are combined, and the 9 brain control instructions are respectively 'low-speed forward', 'medium-speed forward', 'high-speed forward', 'low-speed backward', 'medium-speed backward', 'low-speed left turn', 'medium-speed left turn', 'low-speed right turn' and 'medium-speed right turn'; the computer (1) adopts typical correlation analysis CCA for identification; the testing steps of the robot testing platform (3) are as follows: starting from a starting point, bypassing the barrier and finally reaching an end point; the computer (1) and the robot testing platform (3) are in a synchronous working mode.
3. The method for controlling the intelligent robot based on the electroencephalogram neural feedback, according to claim 1, is characterized in that: the electroencephalogram acquisition system (4) acquires 32 channels of electroencephalogram signals together, the sampling frequency is 250 Hz, the recording electrodes are Pz, P3, P4, PO3, PO4, PO7, O1, Oz, O2, PO8 and a reference electrode Cz, the grounding electrode is FPz, and the electrode impedance is below 5 k ohms; the communication between the robot testing platform (3) and the BCI system adopts a client/server structure; the robot testing platform (3) is a server side, the computer (1) is a client side, and the robot testing platform (3) is connected with the computer (1) through TCP/IP.
4. The method for controlling the intelligent robot based on the electroencephalogram neural feedback, according to claim 1, is characterized in that: the SSVEP brain-computer interactive paradigm on the computer (1) is realized by utilizing a Psychtoolbox (PTB) tool box of Matlab; the refresh rate of the liquid crystal display is 60 frames/second, and the resolution is 1366x768 pixels; the stimulation target consists of 9 squares of 150x150 pixels in size; each stimulation target is modulated by a specific frequency, and the flicker frequency of the stimulation targets from left to right and from top to bottom is [ 8129139.5101410.515 ] Hz; at the beginning, the stimulation interface is firstly statically presented for 3 seconds, and 9 stimulation targets flicker with the frequency after 3 seconds; the converted control instruction on the computer (1) consists of 13 characters; the robot testing platform (3) automatically records the time and the times of touching the obstacle; penalty 5 seconds per collision; all codes of the method for controlling the intelligent robot based on the electroencephalogram neural feedback are realized through VC + + programming.
5. The method for controlling the intelligent robot based on the electroencephalogram neural feedback, according to claim 1, is characterized in that: the computer (1) has three different stimulation paradigms; the difference between the three stimulation paradigms is the spacing between the stimulation targets, where the horizontal and vertical spacing between the paradigms-stimulation targets are both 10; the horizontal and vertical spacing between the paradigm two stimulation targets is 100; the horizontal spacing between the paradigm tri-stimulus targets is 428, and the vertical spacing is 198; the presentation time of the three paradigm stimuli was set to 3 seconds, and the visual transition time, i.e., the interval time, was set to 1.5 seconds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911333102.4A CN111007725A (en) | 2019-12-23 | 2019-12-23 | Method for controlling intelligent robot based on electroencephalogram neural feedback |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911333102.4A CN111007725A (en) | 2019-12-23 | 2019-12-23 | Method for controlling intelligent robot based on electroencephalogram neural feedback |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111007725A true CN111007725A (en) | 2020-04-14 |
Family
ID=70116754
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911333102.4A Pending CN111007725A (en) | 2019-12-23 | 2019-12-23 | Method for controlling intelligent robot based on electroencephalogram neural feedback |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111007725A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111571587A (en) * | 2020-05-13 | 2020-08-25 | 南京邮电大学 | Brain-controlled mechanical arm dining assisting system and method |
| CN112000087A (en) * | 2020-09-06 | 2020-11-27 | 天津大学 | Intent priority fuzzy fusion control method for brain-controlled vehicle |
| CN112936292A (en) * | 2021-03-29 | 2021-06-11 | 昆明理工大学 | Open-source slicing path planning robot arc additive manufacturing method |
| CN114089628A (en) * | 2021-10-25 | 2022-02-25 | 西北工业大学 | Brain-driven mobile robot control system and method based on steady-state visual stimulation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108415568A (en) * | 2018-02-28 | 2018-08-17 | 天津大学 | The intelligent robot idea control method of complex network is migrated based on mode |
| CN108415554A (en) * | 2018-01-18 | 2018-08-17 | 大连理工大学 | A kind of brain man-controlled mobile robot system and its implementation based on P300 |
| WO2018182534A1 (en) * | 2017-03-31 | 2018-10-04 | Agency For Science, Technology And Research | A computer system for acquiring a control command |
-
2019
- 2019-12-23 CN CN201911333102.4A patent/CN111007725A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018182534A1 (en) * | 2017-03-31 | 2018-10-04 | Agency For Science, Technology And Research | A computer system for acquiring a control command |
| CN108415554A (en) * | 2018-01-18 | 2018-08-17 | 大连理工大学 | A kind of brain man-controlled mobile robot system and its implementation based on P300 |
| CN108415568A (en) * | 2018-02-28 | 2018-08-17 | 天津大学 | The intelligent robot idea control method of complex network is migrated based on mode |
Non-Patent Citations (1)
| Title |
|---|
| 伏云发等: "基于SSVEP直接脑控机器人方向和速度研究", 《自动化学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111571587A (en) * | 2020-05-13 | 2020-08-25 | 南京邮电大学 | Brain-controlled mechanical arm dining assisting system and method |
| CN111571587B (en) * | 2020-05-13 | 2023-02-24 | 南京邮电大学 | Brain-controlled mechanical arm dining assisting system and method |
| CN112000087A (en) * | 2020-09-06 | 2020-11-27 | 天津大学 | Intent priority fuzzy fusion control method for brain-controlled vehicle |
| CN112936292A (en) * | 2021-03-29 | 2021-06-11 | 昆明理工大学 | Open-source slicing path planning robot arc additive manufacturing method |
| CN112936292B (en) * | 2021-03-29 | 2022-05-24 | 昆明理工大学 | Open-source slicing path planning robot arc additive manufacturing method |
| CN114089628A (en) * | 2021-10-25 | 2022-02-25 | 西北工业大学 | Brain-driven mobile robot control system and method based on steady-state visual stimulation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111007725A (en) | Method for controlling intelligent robot based on electroencephalogram neural feedback | |
| Wang et al. | A wearable SSVEP-based BCI system for quadcopter control using head-mounted device | |
| CN108415554B (en) | Brain-controlled robot system based on P300 and implementation method thereof | |
| CN103885445A (en) | Brain-controlling animal robot system and brain-controlling method of animal robot | |
| CN108762303A (en) | A kind of portable brain control UAV system and control method based on Mental imagery | |
| CN104965584A (en) | Mixing method for brain-computer interface based on SSVEP and OSP | |
| CN104799984A (en) | Assistance system for disabled people based on brain control mobile eye and control method for assistance system | |
| CN110262658B (en) | Brain-computer interface character input system based on enhanced attention and implementation method | |
| CN107510555B (en) | Wheelchair electroencephalogram control device and control method | |
| Mei et al. | Using SSVEP-BCI to continuous control a quadcopter with 4-DOF motions | |
| CN102436302A (en) | Method for brain-computer interface based on amplitude modulated visual evoked potential | |
| CN103083014A (en) | Method controlling vehicle by electroencephalogram and intelligent vehicle using method | |
| CN105137830A (en) | Traditional Chinese painting mechanical hand based on visual evoking brain-machine interface, and drawing method thereof | |
| CN112051780A (en) | Brain-computer interface-based mobile robot formation control system and method | |
| CN103294192A (en) | LED lamp switch control device and control method thereof based on motor imagery | |
| CN101339413B (en) | Switching control method based on brain electric activity human face recognition specific wave | |
| Puanhvuan et al. | Semi-automatic P300-based brain-controlled wheelchair | |
| CN112140113B (en) | Robot control system and control method based on brain-computer interface | |
| Zhang et al. | Brain-controlled wheelchair system based on SSVEP | |
| CN107066091A (en) | A kind of brain-computer interface P300 signal characteristic body-sensing inducing devices and its application process | |
| Arpaia et al. | Multimodal Feedback in Assisting a Wearable Brain-Computer Interface Based on Motor Imagery | |
| Mori et al. | Multi–command chest tactile brain computer interface for small vehicle robot navigation | |
| Qi et al. | Lower limb rehabilitation exoskeleton control based on SSVEP-BCI | |
| Gao et al. | Intelligent wheelchair research based on SSVEP brain-computer interface | |
| Wu et al. | Intelligent command and control of uav based on brain computer and eye tracking combined technology |
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: 20200414 |
|
| RJ01 | Rejection of invention patent application after publication |