CN111694425A - Target identification method and system based on AR-SSVEP - Google Patents
Target identification method and system based on AR-SSVEP Download PDFInfo
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Abstract
The invention discloses a target identification method, which combines a target identification technology, an augmented reality technology and a brain-computer interface system, transmits far-end video information to VR glasses, frames and selects an attention target under a complex background by using the target identification technology, provides a selection for a subject, and presents an SSVEP stimulation-type stimulation interface for the subject by using the VR glasses, thereby realizing the electroencephalogram selection of a specific target. The invention discloses a target identification system which comprises a remote camera, a wireless transmission module, a signal receiving module, AR glasses, a target identification module, a communication interface module, electroencephalogram acquisition equipment and an upper computer. The method can improve the recognition efficiency and accuracy of specific targets in similar attention targets or slightly different targets in the target recognition process, and obviously improve the decision-making precision of moving target detection.
Description
Technical Field
The invention relates to the technical field of target identification, in particular to a target identification method and a target identification system based on AR-SSVEP.
Background
Brain-computer interface (BCI) is a completely new external information communication and control technology that is not dependent on a conventional brain information output channel and is established between the human brain and a computer or other electronic devices. Wherein the SSVEP-BCI presents a plurality of periodic visual stimuli with different frequencies and phases to the user, when the user focuses on a certain frequency visual stimulus, an EEG signal with specific characteristics, i.e., an SSVEP signal, is induced in the main visual cortex. In order to obtain an electroencephalogram signal of a human under the stimulation of the SSVEP, the system is used for researching the relationship between the electroencephalogram signal and a stimulated signal, in the prior art, a system based on the SSVEP usually comprises a computer display screen specially used for displaying an SSVEP stimulation model, a subject can generate an electroencephalogram phenomenon described by the SSVEP by watching the display screen, then an electroencephalogram collecting device is used for collecting the electroencephalogram signal of the subject, and then a recognition result (recognizing the extracted electroencephalogram signal) is obtained by analyzing the electroencephalogram signal so as to be further used for researching the influence of the stimulation on the human brain.
However, in the prior art, the brain-computer interface system based on the SSVEP includes devices such as a computer, which is bulky, and on the other hand, the subject is a stimulation interface watching a computer display screen, and the subject cannot perform other behavior operations when receiving stimulation, so the prior SSVEP stimulation paradigm can only meet the use under laboratory conditions, is inconvenient to apply to outdoor environments, and has very limited practicability in actual life and production. Object recognition refers to the process by which a particular object (or a particular type of object) is distinguished from other objects (or other types of objects). It includes the identification of both two very similar objects and the identification of one type of object with another type of object.
At present, after a target recognition system is trained, only one type of attention target can be recognized from an image or a video stream, and the attention target often has slight individual differences, such as blue vehicles of different models, twins and the like; the accuracy of specific targets in similar or slightly different attention targets identified by the existing target identification method is low.
Disclosure of Invention
The invention aims to provide an AR-SSVEP-based target identification method and system, which can improve the identification efficiency and accuracy of similar concerned targets or specific targets in slightly different targets in the target identification process, provide possibility for an SSVEP stimulation paradigm to be suitable for an outdoor environment, and improve the practicability of the SSVEP stimulation paradigm in actual life production.
The technical scheme adopted by the invention is as follows:
an AR-SSVEP-based target identification method comprises the following steps:
the method comprises the following steps: the subject wears AR glasses and electroencephalogram equipment;
step two: the camera collects video stream information of a target scene and transmits the collected video stream information to the AR glasses and the target identification module respectively;
step three: the target identification module identifies an attention target in the video stream in real time, calculates target information of the attention target and transmits the target information to the AR glasses; the target information comprises the number n of the concerned targets, the target position of each concerned target and the number of each concerned target; the target position comprises the height of a target frame for framing a concerned target, the width of the target frame and the position coordinates of the central point of the target frame;
setting the labels of the n attention objects as an attention object 1 and an attention object 2 … attention object i … attention object n respectively; numbers corresponding to the attention object 1 and the attention object 2 … attention object i … attention object n are respectively a number 1 and a number 2 …, i … and n; i is more than or equal to 1 and less than or equal to n;
step four: after receiving the target information, the AR glasses draw a target frame according to the n concerned targets, select the n concerned targets, and generate n stimulation blocks corresponding to the n concerned targets one by one;
setting n stimulation blocks as a stimulation block 1 and a stimulation block 2 …, namely a stimulation block i … stimulation block n; the stimulation block i corresponds to the attention target i; the stimulation blocks flicker respectively at specific frequencies, and the flicker frequency of any one stimulation block is different from the flicker frequencies of the other n-1 stimulation blocks;
step five: the AR glasses draw numbers i at the stimulation block i and the attention target i simultaneously in real time;
step six: the AR glasses display a stimulation interface to the subject in real time, and the subject selects a specific target through brain electricity; the stimulation interface comprises n attention targets and n stimulation blocks which are selected by a frame; the method comprises the following steps that a subject selects a specific target from n concerned targets according to the states of the n concerned targets in a stimulation interface, and stares at a stimulation block corresponding to the selected specific target to output an SSVEP electroencephalogram signal; the electroencephalogram acquisition equipment acquires the SSVEP electroencephalogram signals output by the subject, analyzes the stimulation block watched by the subject, and completes the electroencephalogram selection of a specific target;
step seven: and outputting the target position information of the specific target according to the result of the electroencephalogram selection and the target information of the target of interest.
In the third step, the target identification module identifies the concerned target in the video stream information by adopting a YOLO algorithm and calculates the target information of the concerned target.
The distribution of the stimulation blocks comprises two modes, wherein the first mode is that the stimulation blocks are positioned in or around a target frame and follow the target frame; and the second mode is that the stimulation blocks are arranged around the screen frame, and one mode is selected from the two modes to generate the stimulation blocks.
An AR-SSVEP-based target identification system comprises a remote camera, a wireless transmission module, a signal receiving module, AR glasses, a target identification module, a communication interface module, electroencephalogram acquisition equipment and an upper computer;
the remote camera is used for acquiring remote scene video stream information;
the wireless transmission module is used for transmitting the acquired remote scene video stream information to the signal receiving module;
the signal receiving module transmits the received remote scene video stream information to the AR glasses and the target identification module respectively;
the AR glasses are used for receiving and projecting a video stream transmitted back by the front camera, superposing the display stimulation blocks and displaying a stimulation interface to the testee;
the target identification module is used for identifying an attention target in the video stream in real time and solving target information of the attention target;
the communication interface module is used for connecting AR glasses and electroencephalogram acquisition equipment;
the electroencephalogram acquisition device is used for acquiring SSVEP electroencephalogram signals output by a subject, analyzing stimulation blocks watched by the subject and completing electroencephalogram selection of a specific target;
the upper computer is used for acquiring target position information of a specific target according to the result of electroencephalogram selection and target information of the concerned target;
the remote camera is connected with the signal receiving module through the wireless transmission module, a first output end of the signal receiving module is connected with a first input end of the AR glasses, a second output end of the signal receiving module is connected with an input end of the target identification module, an output end of the AR glasses is connected with the electroencephalogram acquisition equipment through the communication interface module, and a first output end of the target identification module is connected with a second input end of the AR glasses; the first output end of the target identification module and the output end of the electroencephalogram acquisition equipment are both connected with an upper computer.
The AR glasses adopt Microsoft HoloLens AR equipment.
The wireless transmission module is RTC67055.8G wireless video transmission chip.
The target identification method combines a target identification technology, an augmented reality technology and a brain-computer interface system, transmits far-end video information to VR glasses, frames an attention target under a complex background by using the target identification technology, presents an SSVEP stimulation mode stimulation interface for a subject, and realizes electroencephalogram selection of a specific target, so that the target identification difficulty of the subject is reduced, and the selection time is reduced; for the aspect of a brain-computer interface system, VR glasses are adopted to present an SSVEP stimulation model stimulation interface for a subject, the distance is short, interference of natural light is small, accuracy of SSVEP induced electroencephalogram is improved, accuracy of target identification is further improved, possibility of being applied outdoors is developed for the SSVEP stimulation model and the brain-computer interface system, and practicability of the SSVEP stimulation model in actual life and production is improved; the problem that a YOLO target recognition algorithm cannot be operated on AR glasses in the combination of a target recognition technology and an augmented reality technology is solved by performing target recognition operation through the external target recognition module, and the recognition and display effects are achieved;
the target identification system is provided with the AR glasses and the electroencephalogram acquisition equipment, combines the target identification technology, the augmented reality technology and the brain-computer interface system, develops the possibility of being applied outdoors for the SSVEP stimulation paradigm and the brain-computer interface system, improves the practicability of the SSVEP stimulation paradigm in actual life production, improves the identification efficiency and the accuracy rate of specific targets in similar concerned targets in the target identification process, and obviously improves the moving target detection decision-making accuracy.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic illustration of a stimulation interface of an embodiment of the present invention;
FIG. 3 is a schematic diagram of an electroencephalogram signal channel of the present invention;
FIG. 4 is a functional block diagram of a target recognition system of the present invention;
1. a stimulation block; 2. a target frame; 3. an object of interest; 4. a screen frame; 5. and (6) numbering.
Detailed Description
As shown in fig. 1, the present invention comprises the steps of:
the method comprises the following steps: the subject wears AR glasses and electroencephalogram equipment;
step two: the camera collects video stream information of a target scene and transmits the collected video stream information to the AR glasses and the target identification module respectively;
step three: the target identification module identifies an attention target in the video stream in real time, calculates target information of the attention target and transmits the target information to the AR glasses; in this embodiment, the target identification module identifies an attention target in the video stream information by using a YOLO algorithm and calculates target information of the attention target.
The target information comprises the number n of the concerned targets, the target position of each concerned target and the number of each concerned target; the target position comprises the height of a target frame for framing a concerned target, the width of the target frame and the position coordinates of the central point of the target frame;
setting the labels of the n attention objects as an attention object 1 and an attention object 2 … attention object i … attention object n respectively; numbers corresponding to the attention object 1 and the attention object 2 … attention object i … attention object n are respectively a number 1 and a number 2 …, i … and n; i is more than or equal to 1 and less than or equal to n;
the target position is calculated by a YOLO algorithm and comprises the height of a target frame, the width of the target frame and the position coordinates of the central point of the target frame; the number of the concerned targets is obtained by counting the number of the position coordinates of the central point; the number is the default number of the classification result, the number is sequentially numbered from 1 to n from left to right according to the coordinate position of the center point of the target from top to bottom according to the numbering principle, and the number has no special meaning and only marks the identified attention target.
As shown in fig. 2, in the present embodiment, the camera acquires road video information, the target of interest is a vehicle, and the target identification module identifies 4 targets of interest.
In fig. 2, the target of interest on the picture and 1, 2, 3, 4 on the stimulation block are the numbers according to the present invention. And 1, 2, 3, 4 and 5 on the transverse line are reference numbers.
Step four: after receiving the target information, the AR glasses draw a matched target frame around each concerned target according to the n concerned targets, select the n concerned targets, and generate n stimulation blocks corresponding to the n concerned targets one by one;
setting n stimulation blocks as a stimulation block 1 and a stimulation block 2 …, namely a stimulation block i … stimulation block n; the stimulation block i corresponds to the attention target i; the stimulation blocks flicker with specific frequencies respectively, and the flicker frequency of any one stimulation block is different from the flicker frequency of other n-1 stimulation blocks.
The distribution of the stimulation blocks comprises two modes, wherein the first mode is that the stimulation blocks are positioned in or around a target frame and follow the target frame; and the second mode is that the stimulation blocks are arranged around the screen frame, and one mode is selected from the two modes to generate the stimulation blocks.
The target identification module and the AR glasses realize data sharing through a network, the data sharing means that the target identification module can transmit identified target information to the AR glasses in real time, and the AR glasses draw corresponding target frames and numbers in real time through a drawing program according to the received target information, so that the requirement on the computing capacity of the AR glasses is reduced.
The AR glasses generate n stimulation blocks according to the identified target number n: in the embodiment, the second mode is selected, and the stimulation blocks are arranged around the screen frame according to a fixed sequence; the stimulation block flickers according to a specific frequency; the specific frequency is represented by the formula
Is calculated to obtain, wherein fcIndicating the stimulation frequency (i.e. the blinking frequency of the stimulation block), fsRepresenting the refresh frequency of the device and N represents a positive integer.
For example, when the refresh frequency of the AR glasses display is 60Hz, the stimulation frequency obtained when N is 4,5,6,7,8,9,10 meets the requirements, and is 15Hz,12Hz,10Hz,8.57Hz,7.5Hz,6.67Hz,6Hz respectively.
Preferably, the stimulation paradigm of the stimulation block is a red-white color block flashing pattern.
In this embodiment, four stimulation blocks are disposed around the screen frame, and the frequencies are 7.5Hz, 8.5Hz, 10Hz, and 12Hz, respectively, although other forms and frequencies may also be provided.
Step five: the AR glasses draw numbers i at the stimulation block i and the attention target i simultaneously in real time;
step six: the AR glasses display a stimulation interface to the subject in real time, and the subject selects a specific target through brain electricity; the stimulation interface comprises n attention targets and n stimulation blocks which are selected by a frame; the method comprises the following steps that a subject selects a specific target from n concerned targets according to the states of the n concerned targets in a stimulation interface, and stares at a stimulation block corresponding to the selected specific target to output an SSVEP electroencephalogram signal; the electroencephalogram acquisition equipment acquires the SSVEP electroencephalogram signals output by the subject, analyzes the stimulation block watched by the subject, and completes the electroencephalogram selection of a specific target; in the embodiment, as shown in fig. 3, the channels for acquiring electroencephalogram signals are T5, T6, P3, P4, O1 and O2.
Step seven: and outputting the target position information of the specific target according to the result of the electroencephalogram selection and the target information of the target of interest.
The target identification method combines a target identification technology, an augmented reality technology and a brain-computer interface system, transmits far-end video information to VR glasses, frames an attention target under a complex background by using the target identification technology, presents an SSVEP stimulation-type stimulation interface for a subject, and realizes electroencephalogram selection of a specific target, thereby reducing the target identification difficulty of the subject and reducing the selection time.
The method of the invention also has outstanding beneficial effects on SSVEP-BCI technology. Because VR glasses are adopted to present the stimulation interface of the SSVEP stimulation paradigm for the subject, the distance is short, the interference of natural light is small, the accuracy of inducing the electroencephalogram by the SSVEP is improved, the possibility of being applied outdoors is developed for the SSVEP stimulation paradigm and a brain-computer interface system, and the practicability of the SSVEP stimulation paradigm in actual life and production is improved.
The AR glasses have limited computing capability, and the target identification computing is performed through the external target identification module, so that the problem that a YOLO target identification algorithm cannot be operated on the AR glasses in the process of combining the target identification technology and the augmented reality technology is solved, and the identification and display effects are achieved.
The target identification method provided by the invention can be suitable for decision selection of unmanned vehicle driving, and mainly solves the problems that the unmanned vehicle remote moving target identification is difficult to track and position.
In the hit-and-run vehicle search, the conventional target recognition can only identify the similar vehicles, and can not accurately identify whether the vehicle is the hit-and-run vehicle or not and whether the driver is the hit-and-run driver or not.
The AR-SSVEP-based target identification system comprises a remote camera, a wireless transmission module, a signal receiving module, AR glasses, a target identification module, a communication interface module, electroencephalogram acquisition equipment and an upper computer;
the remote camera is used for acquiring remote scene video stream information;
the wireless transmission module is used for transmitting the acquired remote scene video stream information to the signal receiving module;
the signal receiving module transmits the received remote scene video stream information to the AR glasses and the target identification module respectively;
the AR glasses are used for receiving and projecting a video stream transmitted back by the front camera, superposing the display stimulation blocks and displaying a stimulation interface to the testee;
the target identification module is used for identifying an attention target in the video stream in real time and solving target information of the attention target;
the communication interface module is used for connecting AR glasses and electroencephalogram acquisition equipment;
the electroencephalogram acquisition device is used for acquiring SSVEP electroencephalogram signals output by a subject, analyzing stimulation blocks watched by the subject and completing electroencephalogram selection of a specific target;
the upper computer is used for acquiring target position information of a specific target according to the result of electroencephalogram selection and target information of the concerned target;
the remote camera is connected with the signal receiving module through the wireless transmission module, a first output end of the signal receiving module is connected with a first input end of the AR glasses, a second output end of the signal receiving module is connected with an input end of the target identification module, an output end of the AR glasses is connected with the electroencephalogram acquisition equipment through the communication interface module, and a first output end of the target identification module is connected with a second input end of the AR glasses; the first output end of the target identification module and the output end of the electroencephalogram acquisition equipment are both connected with an upper computer.
The AR glasses adopt Microsoft HoloLens AR equipment.
The wireless transmission module is RTC67055.8G wireless video transmission chip.
The target identification system is provided with AR glasses and brain electricity acquisition equipment, combines a target identification technology, an augmented reality technology and a brain-computer interface system, transmits far-end video information to VR glasses, frames and selects an attention target under a complex background by using the target identification technology, presents an SSVEP stimulation-type stimulation interface for a subject, realizes brain electricity selection of a specific target, reduces the target identification difficulty of the subject, reduces selection time, and adds the step of brain electricity selection for the target identification technology, so that specific target identification in similar attention targets or slightly different targets is more accurately identified, the identification efficiency and the accuracy of the specific target in the target identification process are improved, and the moving target detection decision precision is remarkably improved.
For the aspect of a brain-computer interface system, VR glasses are arranged to present an SSVEP stimulation paradigm stimulation interface for a subject, the distance is short, interference of natural light is small, accuracy of SSVEP induced electroencephalogram is improved, accuracy of target identification is further improved, possibility of being applied outdoors is developed for the SSVEP stimulation paradigm and the brain-computer interface system, and practicability of the SSVEP stimulation paradigm in actual life and production is improved.
The target recognition system is provided with the external target recognition module for carrying out target recognition operation, solves the problem that a YOLO target recognition algorithm cannot be operated on AR glasses in the combination of the target recognition technology and the augmented reality technology, and achieves the recognition and display effects.
Claims (6)
1. An AR-SSVEP-based target identification method is characterized in that: the method comprises the following steps:
the method comprises the following steps: the subject wears AR glasses and electroencephalogram equipment;
step two: the camera collects video stream information of a target scene and transmits the collected video stream information to the AR glasses and the target identification module respectively;
step three: the target identification module identifies an attention target in the video stream in real time, calculates target information of the attention target and transmits the target information to the AR glasses; the target information comprises the number n of the concerned targets, the target position of each concerned target and the number of each concerned target; the target position comprises the height of a target frame for framing a concerned target, the width of the target frame and the position coordinates of the central point of the target frame;
setting the labels of the n attention objects as an attention object 1 and an attention object 2 … attention object i … attention object n respectively; numbers corresponding to the attention object 1 and the attention object 2 … attention object i … attention object n are respectively a number 1 and a number 2 …, i … and n; i is more than or equal to 1 and less than or equal to n;
step four: after receiving the target information, the AR glasses draw a target frame according to the n concerned targets, select the n concerned targets, and generate n stimulation blocks corresponding to the n concerned targets one by one;
setting n stimulation blocks as a stimulation block 1 and a stimulation block 2 …, namely a stimulation block i … stimulation block n; the stimulation block i corresponds to the attention target i; the stimulation blocks flicker respectively at specific frequencies, and the flicker frequency of any one stimulation block is different from the flicker frequencies of the other n-1 stimulation blocks;
step five: the AR glasses draw numbers i at the stimulation block i and the attention target i simultaneously in real time;
step six: the AR glasses display a stimulation interface to the subject in real time, and the subject selects a specific target through brain electricity; the stimulation interface comprises n attention targets and n stimulation blocks which are selected by a frame; the method comprises the following steps that a subject selects a specific target from n concerned targets according to the states of the n concerned targets in a stimulation interface, and stares at a stimulation block corresponding to the selected specific target to output an SSVEP electroencephalogram signal; the electroencephalogram acquisition equipment acquires the SSVEP electroencephalogram signals output by the subject, analyzes the stimulation block watched by the subject, and completes the electroencephalogram selection of a specific target;
step seven: and outputting the target position information of the specific target according to the result of the electroencephalogram selection and the target information of the target of interest.
2. The AR-SSVEP-based target recognition method of claim 1, wherein: in the third step, the target identification module identifies the concerned target in the video stream information by adopting a YOLO algorithm and calculates the target information of the concerned target.
3. The AR-SSVEP-based target recognition method of claim 1, wherein: the distribution of the stimulation blocks comprises two modes, wherein the first mode is that the stimulation blocks are positioned in or around a target frame and follow the target frame; and the second mode is that the stimulation blocks are arranged around the screen frame, and one mode is selected from the two modes to generate the stimulation blocks.
4. An AR-SSVEP-based target identification system is characterized in that: the device comprises a remote camera, a wireless transmission module, a signal receiving module, AR glasses, a target identification module, a communication interface module, electroencephalogram acquisition equipment and an upper computer;
the remote camera is used for acquiring remote scene video stream information;
the wireless transmission module is used for transmitting the acquired remote scene video stream information to the signal receiving module;
the signal receiving module transmits the received remote scene video stream information to the AR glasses and the target identification module respectively;
the AR glasses are used for receiving and projecting a video stream transmitted back by the front camera, superposing the display stimulation blocks and displaying a stimulation interface to the testee;
the target identification module is used for identifying an attention target in the video stream in real time and solving target information of the attention target;
the communication interface module is used for connecting AR glasses and electroencephalogram acquisition equipment;
the electroencephalogram acquisition device is used for acquiring SSVEP electroencephalogram signals output by a subject, analyzing stimulation blocks watched by the subject and completing electroencephalogram selection of a specific target;
the upper computer is used for acquiring target position information of a specific target according to the result of electroencephalogram selection and target information of the concerned target;
the remote camera is connected with the signal receiving module through the wireless transmission module, a first output end of the signal receiving module is connected with a first input end of the AR glasses, a second output end of the signal receiving module is connected with an input end of the target identification module, an output end of the AR glasses is connected with the electroencephalogram acquisition equipment through the communication interface module, and a first output end of the target identification module is connected with a second input end of the AR glasses; the first output end of the target identification module and the output end of the electroencephalogram acquisition equipment are both connected with an upper computer.
5. The AR-SSVEP based target recognition system of claim 5, wherein: the AR glasses adopt Microsoft HoloLens AR equipment.
6. The AR-SSVEP based target recognition system of claim 6, wherein: the wireless transmission module is RTC67055.8G wireless video transmission chip.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113138668A (en) * | 2021-04-25 | 2021-07-20 | 清华大学 | Method, device and system for selecting destination of automatic wheelchair driving |
| CN113377212A (en) * | 2021-08-16 | 2021-09-10 | 南京中谷芯信息科技有限公司 | Eye movement tracking AR interface navigation system and method based on electroencephalogram detection |
| CN114138108A (en) * | 2021-10-19 | 2022-03-04 | 杭州回车电子科技有限公司 | Brain-computer interaction device, system and method |
| CN114138107A (en) * | 2021-10-19 | 2022-03-04 | 杭州回车电子科技有限公司 | Brain-computer interaction device, system and method |
| GR1010460B (en) * | 2022-05-30 | 2023-05-16 | Ιδρυμα Τεχνολογιας Και Ερευνας, | A mobility system and a related controller, method, software and computer-readable medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106339091A (en) * | 2016-08-31 | 2017-01-18 | 博睿康科技（常州）股份有限公司 | Augmented reality interaction method based on brain-computer interface wearing system |
| CN106859645A (en) * | 2017-03-06 | 2017-06-20 | 广东工业大学 | Wearable device and eeg collection system based on VR technologies and SSVEP |
| CN107346179A (en) * | 2017-09-11 | 2017-11-14 | 中国人民解放军国防科技大学 | Multi-moving-target selection method based on evoked brain-computer interface |
| CN107748622A (en) * | 2017-11-08 | 2018-03-02 | 中国医学科学院生物医学工程研究所 | A kind of Steady State Visual Evoked Potential brain-machine interface method based on face perception |
| US20180104482A1 (en) * | 2016-10-14 | 2018-04-19 | Boston Scientific Neuromodulation Corporation | Systems and methods for determining orientation of an implanted lead |
| US20190369727A1 (en) * | 2017-06-29 | 2019-12-05 | South China University Of Technology | Human-machine interaction method based on visual stimulation |
-
2020
- 2020-04-27 CN CN202010341613.7A patent/CN111694425A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106339091A (en) * | 2016-08-31 | 2017-01-18 | 博睿康科技（常州）股份有限公司 | Augmented reality interaction method based on brain-computer interface wearing system |
| US20180104482A1 (en) * | 2016-10-14 | 2018-04-19 | Boston Scientific Neuromodulation Corporation | Systems and methods for determining orientation of an implanted lead |
| CN106859645A (en) * | 2017-03-06 | 2017-06-20 | 广东工业大学 | Wearable device and eeg collection system based on VR technologies and SSVEP |
| US20190369727A1 (en) * | 2017-06-29 | 2019-12-05 | South China University Of Technology | Human-machine interaction method based on visual stimulation |
| CN107346179A (en) * | 2017-09-11 | 2017-11-14 | 中国人民解放军国防科技大学 | Multi-moving-target selection method based on evoked brain-computer interface |
| CN107748622A (en) * | 2017-11-08 | 2018-03-02 | 中国医学科学院生物医学工程研究所 | A kind of Steady State Visual Evoked Potential brain-machine interface method based on face perception |
Non-Patent Citations (1)
| Title |
|---|
| 刘亚茹: "《基于脑机接口的多移动目标选择技术研究》", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113138668A (en) * | 2021-04-25 | 2021-07-20 | 清华大学 | Method, device and system for selecting destination of automatic wheelchair driving |
| CN113377212A (en) * | 2021-08-16 | 2021-09-10 | 南京中谷芯信息科技有限公司 | Eye movement tracking AR interface navigation system and method based on electroencephalogram detection |
| CN113377212B (en) * | 2021-08-16 | 2021-11-16 | 南京中谷芯信息科技有限公司 | Eye movement tracking AR interface navigation system and method based on electroencephalogram detection |
| CN114138108A (en) * | 2021-10-19 | 2022-03-04 | 杭州回车电子科技有限公司 | Brain-computer interaction device, system and method |
| CN114138107A (en) * | 2021-10-19 | 2022-03-04 | 杭州回车电子科技有限公司 | Brain-computer interaction device, system and method |
| GR1010460B (en) * | 2022-05-30 | 2023-05-16 | Ιδρυμα Τεχνολογιας Και Ερευνας, | A mobility system and a related controller, method, software and computer-readable medium |
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