A multi-person collaborative control device based on brain wave signals

Abstract

The present invention relates to the technical field of multi-person brain wave collaborative control, and specifically discloses a multi-person collaborative control device based on brain wave signals, the device comprising: an acquisition module, used to acquire the EEG information of the user to be acquired; a processing module, electrically connected to the acquisition module, the processing module is used to convert the EEG information acquired by the acquisition module into concentration data, and send it; an instruction module, used to receive the concentration data sent by several processing modules, the instruction module is also used to convert the concentration data sent by several processing modules into control instructions. The user's EEG information is acquired by the acquisition module, and converted into concentration data by the processing module for transmission. The concentration data sent by multiple processing modules is then received by the instruction module, and converted into control instructions. The user's concentration level can be monitored in real time, and corresponding control instructions can be generated according to the concentration data. Thus, multi-person collaborative control based on EEG signals is realized.

Description

Background Art

[0002] As a cutting-edge field of neuroengineering, brain-computer interface (BCI) technology aims to achieve direct communication and control between the human brain and external devices by monitoring and interpreting brain activity. In the research of brain-computer interface technology, alpha waves are considered to be one of the main components of spontaneous EEG, with a frequency between 8 and 13 Hz, corresponding to the idle rhythm of the visual cortex, and one of the most distinctive components of EEG.

[0003] According to the existing literature "Research on Brain-Computer Interface Control System Based on EEG Alpha Wave, Zhang Qian", it is recorded: "Alpha waves are one of the main components of spontaneous EEG, corresponding to the idle rhythm of the visual cortex with a frequency of 8~13Hz, and are the most obvious component of rhythmic EEG waves. When a normal person is awake and closes his eyes, the wave is enhanced. When he opens his eyes, thinks about a problem, or is stimulated by other things, the electrophysiological phenomenon of a wave weakening or disappearing is called a wave blocking; if the subject closes his eyes quietly again, the wave reappears34. Based on the universal feature that more than 90% of people with normal cranial nerve function can significantly enhance the alpha wave amplitude in their EEG after closing their eyes for a short period of time, the control of a single switch is achieved by extracting the changing characteristics of the EEG a wave amplitude when the subject opens and closes his eyes as a control signal." It can be seen that brain-computer interface technology has been widely used, allowing people to control external devices through brain wave signals.

[0004] However, existing brain-computer interface devices are mainly for single users and cannot effectively meet the needs of collaborative control among multiple people. This limits the application of this technology in multi-person collaborative scenarios, such as teamwork and joint operation of external devices.

[0005] In view of this, there is an urgent need to invent a multi-person collaborative brainwave control device to solve the technical defect that the existing technology cannot realize multi-person collaborative control based on brainwaves. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-person collaborative control device based on brain wave signals, which aims to solve the technical defect that the prior art cannot realize multi-person collaborative control based on brain waves.

[0007] In one aspect, an embodiment of the present invention provides a multi-person collaborative control device based on brain wave signals, comprising:

[0008] A collection module, used to collect EEG information of the user to be collected;

[0009] A processing module, electrically connected to the acquisition module, and configured to convert the EEG information acquired by the acquisition module into concentration data and send the data;

[0010] The instruction module is used to receive the concentration data sent by the processing modules, and the instruction module is also used to convert the concentration data sent by the processing modules into control instructions.

[0011] Furthermore, the processing module includes:

[0012] An acquisition unit, electrically connected to the acquisition module, and configured to acquire the EEG information acquired by the acquisition module;

[0013] A processing unit, used for acquiring brain wave type information in the brain wave information, and converting the brain wave type information into concentration data;

[0014] A sending unit is used to send the concentration information converted by the processing unit.

[0015] Furthermore, the processing unit is further used to obtain the real-time beta wave frequency L in the brain wave type information, and determine whether the brain wave state of the user to be collected is in a focused state according to the relationship between the real-time beta wave frequency L and the preset beta wave frequency L1;

[0016] When L<L1, the processing unit determines that the EEG state of the user to be collected is not in a focused state;

[0017] When L≥L1, the processing unit determines that the EEG state of the user to be collected is in a focused state, and adjusts the converted concentration data according to the relationship between the real-time β wave frequency L and the preset β wave frequency L1.

[0018] Furthermore, when the processing unit determines that the EEG state of the user to be collected is not in a focused state and adjusts the converted concentration data according to the relationship between the real-time β wave frequency L and the preset β wave frequency L1, it includes:

[0019] The processing unit is further used to obtain a beta wave frequency difference △L between the real-time beta wave frequency L and the preset beta wave frequency L1, and set △L=L-L1; the processing unit is further used to compare the beta wave frequency difference △L with the preset beta wave frequency difference, and select a corresponding adjustment coefficient according to the comparison result to adjust the converted concentration data;

[0020] Wherein, a first β wave frequency difference △L1 is preset, a second β wave frequency difference △L2 is preset, a first adjustment coefficient X1 is set, a second adjustment coefficient X2 is set, a third adjustment coefficient X3 is set, and △L1<△L2, 1<X1<X2<X3<2;

[0021] When △L<△L1, the first adjustment coefficient X1 is selected to adjust the converted concentration data;

[0022] When △L1≤△L<△L2, the second adjustment coefficient X2 is selected to adjust the converted concentration data;

[0023] When △L≥△L2, the third adjustment coefficient X3 is selected to adjust the converted concentration data;

[0024] The converted concentration data is adjusted by selecting the i-th adjustment coefficient Xi, and the adjusted concentration data is set to F1, and F1=F×Xi is set, wherein F is the initial data of the converted concentration data, Xi is the adjustment coefficient of the converted concentration data, and i=1, 2 or 3.

[0025] Furthermore, when the processing unit selects the i-th adjustment coefficient Xi to adjust the converted concentration data and obtains the adjusted concentration data F1, it includes:

[0026] The processing unit is also used to obtain the real-time α wave frequency Y in the brain wave type information, and determine whether the brain wave state of the user to be collected is in a relaxed state according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1;

[0027] When Y<Y1, the processing unit determines that the EEG state of the user to be collected is not in a relaxed state;

[0028] When Y≥Y1, the processing unit determines that the EEG state of the user to be collected is in a relaxed state, and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1.

[0029] Furthermore, when the processing unit determines that the EEG state of the user to be collected is in a relaxed state and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1, it includes:

[0030] The processing unit is further used to obtain the α wave frequency difference △Y between the real-time α wave frequency Y and the preset α wave frequency Y1, and set △Y=Y-Y1. The processing unit is also used to select a corresponding correction coefficient to correct the adjusted concentration data F1 according to the comparison between the α wave frequency difference △Y and the preset α wave frequency difference;

[0031] The first α wave frequency difference △Y1 is preset, the second α wave frequency difference △Y2 is preset, the first correction coefficient G1, the second correction coefficient G2, and the third correction coefficient G3 are set, and △Y1<△Y2, 0<G1<G2<G3<1;

[0032] When △Y<△Y1, the third correction coefficient G3 is selected to correct the adjusted concentration data F1;

[0033] When △Y1≤△Y<△Y2, the second correction coefficient G2 is selected to correct the adjusted concentration data F1;

[0034] When △Y≥△Y2, the first correction coefficient G1 is selected to correct the adjusted concentration data F1;

[0035] When the i-th correction coefficient Gi is selected to correct the adjusted concentration data F1, and the corrected concentration data is set to F2, F2=F1×Gi is set, where Gi is the correction coefficient of the adjusted concentration data F1, and i=1, 2, or 3.

[0036] Furthermore, the instruction module includes:

[0037] A receiving unit, configured to receive the corrected concentration data F2 sent by the plurality of sending units;

[0038] The instruction unit is used to convert the corrected concentration data F2 sent by the sending units into the control instruction.

[0039] Further, the instruction unit is further used to obtain a sum Q of the corrected concentration data F2 sent by a plurality of the sending units, and set Q=((1F2)+(2F2)+(3F2)+…+(nF2)), where n is the sending unit number;

[0040] The instruction unit is also used to convert the sum value Q into a control instruction.

[0041] Furthermore, the receiving unit and the sending unit are respectively provided with a Bluetooth subunit or a 4G, 5G communication subunit.

[0042] Compared with the prior art, the beneficial effect of the multi-person collaborative control device based on brain wave signals in the embodiment of the present invention is that the user's EEG information is obtained through the acquisition module, and it is converted into concentration data through the processing module and transmitted. Then, the instruction module receives the concentration data sent by multiple processing modules and converts it into control instructions. It is possible to monitor the user's concentration level in real time and generate corresponding control instructions based on the concentration data. In this way, multi-person collaborative control based on EEG signals can be achieved, thereby effectively improving the efficiency and accuracy of task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a functional block diagram of a multi-person collaborative control device based on brain wave signals according to an embodiment of the present invention.

[0044] Figure 2 It is a structural block diagram of a processing module in an embodiment of the present invention.

[0045] Figure 3 It is a structural block diagram of the instruction module in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0048] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0049] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0050] As a cutting-edge field of neuroengineering, brain-computer interface (BCI) technology aims to achieve direct communication and control between the human brain and external devices by monitoring and interpreting brain activity. In the research of brain-computer interface technology, alpha waves are considered to be one of the main components of spontaneous EEG, with a frequency between 8 and 13 Hz, corresponding to the idle rhythm of the visual cortex, and one of the most distinctive components of EEG.

[0051] According to the existing literature "Research on Brain-Computer Interface Control System Based on EEG Alpha Wave, Zhang Qian", it is recorded: "Alpha waves are one of the main components of spontaneous EEG, corresponding to the idle rhythm of the visual cortex with a frequency of 8~13Hz, and are the most obvious component of rhythmic EEG waves. When a normal person is awake and closes his eyes, the wave is enhanced. When he opens his eyes, thinks about a problem, or is stimulated by other things, the electrophysiological phenomenon of a wave weakening or disappearing is called a wave blocking; if the subject closes his eyes quietly again, the wave reappears34. Based on the universal feature that more than 90% of people with normal cranial nerve function can significantly enhance the alpha wave amplitude in their EEG after closing their eyes for a short period of time, the control of a single switch is achieved by extracting the changing characteristics of the EEG a wave amplitude when the subject opens and closes his eyes as a control signal." It can be seen that brain-computer interface technology has been widely used, allowing people to control external devices through brain wave signals.

[0052] However, existing brain-computer interface devices are mainly for single users and cannot effectively meet the needs of collaborative control among multiple people. This limits the application of this technology in multi-person collaborative scenarios, such as teamwork and joint operation of external devices.

[0053] In view of this, the present invention provides a multi-person collaborative control device based on brain wave signals to solve the technical defect that multi-person collaborative control based on brain waves cannot be realized in the prior art.

[0054] like Figure 1 As shown, a multi-person collaborative control device based on brain wave signals according to a preferred embodiment of the present invention includes: a collection module, a processing module and an instruction module.

[0055] Specifically, the acquisition module is used to collect the EEG information of the user to be collected; the processing module is electrically connected to the acquisition module, and the processing module is used to convert the EEG information collected by the acquisition module into concentration data and send it; the instruction module is used to receive the concentration data sent by several processing modules, and the instruction module is also used to convert the concentration data sent by several processing modules into control instructions.

[0056] It can be seen that a multi-person collaborative control device based on brain wave signals in an embodiment of the present invention is composed of an acquisition module, a processing module and an instruction module. The acquisition module obtains the EEG information of the user to be collected, and the processing module converts the EEG information into concentration data and sends it. This enables the device to monitor the user's concentration level in real time, while receiving the concentration data sent from multiple processing modules through the instruction module, and generating appropriate control instructions according to the user's concentration level.

[0057] It is understandable that the command module converts the concentration data of multiple people into corresponding control commands, thereby realizing multi-person collaborative control. This enables more efficient collaborative operation and interactive experience, and further improves the accuracy of multi-person collaborative control.

[0058] Specifically, see Figure 2 In some embodiments of the present invention, the processing module includes: an acquisition unit, a processing unit and a sending unit; the acquisition unit is electrically connected to the acquisition module, and the acquisition unit is used to acquire the EEG information acquired by the acquisition module; the processing unit is used to acquire the EEG wave type information in the EEG information, and convert it into concentration data according to the EEG wave type information; the sending unit is used to send the concentration information converted by the processing unit.

[0059] Specifically, in some embodiments of the present invention, the processing unit is also used to obtain the real-time beta wave frequency L in the brain wave type information, and judge whether the brain wave state of the user to be collected is in a focused state according to the relationship between the real-time beta wave frequency L and the preset beta wave frequency L1: when L<L1, the processing unit judges that the brain wave state of the user to be collected is not in a focused state. When L≥L1, the processing unit judges that the brain wave state of the user to be collected is in a focused state, and adjusts the converted concentration data according to the relationship between the real-time beta wave frequency L and the preset beta wave frequency L1.

[0060] It is understandable that an increase in the frequency of beta waves in EEG signals is associated with alertness and concentration. When a person is in a task or activity that requires concentration, the frequency of beta waves usually increases. This suggests that beta waves may be related to the activation state of the brain and high concentration. By monitoring the beta wave frequency in real time, the user's state of concentration can be judged more accurately. Based on the judgment results of the state of concentration, the system can provide accurate feedback, thereby more effectively obtaining the user's EEG state, thereby helping the user to better control and improve concentration.

[0061] Specifically, in some embodiments of the present invention, when the processing unit determines that the EEG state of the user to be collected is not in a focused state, and adjusts the converted concentration data according to the relationship between the real-time β wave frequency L and the preset β wave frequency L1, it includes: the processing unit is also used to obtain the β wave frequency difference △L between the real-time β wave frequency L and the preset β wave frequency L1, and sets △L=L-L1; the processing unit is also used to compare the β wave frequency difference △L with the preset β wave frequency difference, and select the corresponding adjustment coefficient according to the comparison result to adjust the converted concentration data: wherein, a first β wave frequency difference △L1 is preset, a second β wave frequency difference △L2 is preset, a first adjustment coefficient X1 is set, a second adjustment coefficient X2 is set, a third adjustment coefficient X3 is set, and △L1<△L2, 1<X1<X2<X3<2.

[0062] When △L<△L1, the first adjustment coefficient X1 is selected to adjust the converted concentration data.

[0063] When △L1≤△L<△L2, the second adjustment coefficient X2 is selected to adjust the converted concentration data.

[0064] When △L≥△L2, the third adjustment coefficient X3 is selected to adjust the converted concentration data.

[0065] After selecting the i-th adjustment coefficient Xi, the converted concentration data is adjusted, and the adjusted concentration data is set to F1, and F1=F×Xi is set, wherein F is the initial data of the converted concentration data, Xi is the adjustment coefficient of the converted concentration data, and i=1, 2 or 3.

[0066] It can be understood that by selecting different adjustment coefficients, the processing unit can convert the concentration data more accurately. The specific analysis is as follows: For a smaller difference (△L<△L1), the first adjustment coefficient X1 is selected for adjustment. This means that when the beta wave frequency of the user to be collected is close to the preset frequency, the converted concentration data will be adjusted slightly. This can carefully reflect the user's concentration state and avoid excessive adjustment and distortion. When the difference is between the preset difference range (△L1≤△L<△L2), the second adjustment coefficient X2 is selected for adjustment. This shows that when the beta wave frequency of the user to be collected deviates from the preset frequency by a certain range, the converted concentration data will be adjusted appropriately. This helps to flexibly adapt to changes in the user's concentration state and provide more accurate concentration data. When the difference is greater than or equal to the preset difference (△L≥△L2), the third adjustment coefficient X3 is selected for adjustment. This means that when the beta wave frequency of the user to be collected deviates significantly from the preset frequency, the converted concentration data will be adjusted significantly. This helps to capture large changes in user concentration in a timely manner and provide corresponding accurate concentration data.

[0067] Specifically, in some embodiments of the present invention, when the processing unit selects the i-th adjustment coefficient Xi to adjust the converted concentration data and obtains the adjusted concentration data F1, it includes: the processing unit is also used to obtain the real-time α wave frequency Y in the brain wave type information, and judge whether the brain wave state of the user to be collected is in a relaxed state according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1: when Y<Y1, the processing unit judges that the brain wave state of the user to be collected is not in a relaxed state. When Y≥Y1, the processing unit judges that the brain wave state of the user to be collected is in a relaxed state, and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1.

[0068] It is understandable that the coherence of alpha waves in EEG signals can reflect the synchronization and communication efficiency between different brain regions, and lower alpha wave frequencies may mean weaker inhibitory activity. Inhibitory alpha wave activity can help suppress external interference and irrelevant information, thereby improving the brain's state of concentration. By judging the user's state of relaxation based on the alpha wave frequency in the EEG signal, the converted concentration data can be corrected. By monitoring the alpha wave frequency in real time and adjusting the concentration data accordingly, the system can more accurately reflect the user's level of concentration. This personalized concentration adjustment helps improve the user's ability to relax and helps the user better manage and adjust the state of attention. At the same time, correcting the concentration data according to the state of relaxation can provide more accurate and reliable concentration feedback.

[0069] Specifically, in some embodiments of the present invention, when the processing unit determines that the EEG state of the user to be collected is in a relaxed state, and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1, it includes: the processing unit is also used to obtain the α wave frequency difference △Y between the real-time α wave frequency Y and the preset α wave frequency Y1, and sets △Y=Y-Y1; the processing unit is also used to select a corresponding correction coefficient to correct the adjusted concentration data F1 according to the comparison between the α wave frequency difference △Y and the preset α wave frequency difference: wherein, a first α wave frequency difference △Y1 is preset, a second α wave frequency difference △Y2 is preset, a first correction coefficient G1, a second correction coefficient G2, and a third correction coefficient G3 are set, and △Y1<△Y2, 0<G1<G2<G3<1.

[0070] When ΔY<ΔY1, the third correction coefficient G3 is selected to correct the adjusted concentration data F1.

[0071] When △Y1≤△Y<△Y2, the second correction coefficient G2 is selected to correct the adjusted concentration data F1.

[0072] When △Y≥△Y2, the first correction coefficient G1 is selected to correct the adjusted concentration data F1.

[0073] When the i-th correction coefficient Gi is selected to correct the adjusted concentration data F1, and the corrected concentration data is set to F2, F2=F1×Gi is set, where Gi is the correction coefficient of the adjusted concentration data F1, and i=1, 2, or 3.

[0074] It is understandable that by comparing the relationship between the real-time alpha wave frequency difference and the preset difference, the processing unit can select an appropriate correction coefficient to fine-tune the adjusted concentration data. By corresponding different alpha wave frequency difference ranges to different correction coefficients, the user's concentration level can be more accurately reflected.

[0075] Specifically, see Figure 3 In some embodiments of the present invention, the instruction module includes: a receiving unit and an instruction unit; the receiving unit is used to receive the corrected concentration data F2 sent by several sending units; the instruction unit is used to convert the corrected concentration data F2 sent by several sending units into a control instruction.

[0076] Specifically, in some embodiments of the present invention, the instruction unit is further used to obtain the sum Q of the corrected concentration data F2 sent by several sending units, and set Q=((1F2)+(2F2)+(3F2)+…+(nF2)), where n is the sending unit number. The instruction unit is also used to convert the sum Q into a control instruction.

[0077] It can be understood that by converting the sum Q into control instructions through the instruction unit, the system can achieve multi-person collaborative control. Different sending units represent different users, and the concentration data of different users will affect and accumulate each other. The instruction unit converts this data into control instructions, which can achieve collaborative control among multiple people and promote collaborative work or collaborative training of teams or groups.

[0078] Specifically, in some embodiments of the present invention, the receiving unit and the sending unit are respectively provided with a Bluetooth subunit or a 4G, 5G communication subunit.

[0079] It is understandable that wireless data transmission can be carried out between the receiving unit and the sending unit through the Bluetooth subunit or the 4G, 5G communication subunit. The limitations of traditional wired connections are eliminated, making it more convenient and flexible. Users are not restricted by physical connections and can move and operate freely, thereby increasing the convenience and comfort of use. At the same time, the Bluetooth subunit or the 4G, 5G communication subunit can also enable the receiving unit to be connected to multiple sending units at the same time, thereby ensuring a fast response speed during multi-person collaborative control.

[0080] To sum up, the working process of the present invention is: by wearing the acquisition module on the user to be collected, the EEG information of the user to be collected is obtained, and the EEG information is converted into concentration data by the processing module, and sent to the instruction module. When the instruction module obtains the concentration data sent by several processing modules, and calculates the concentration data and values ​​sent by several processing modules, the concentration data and values ​​sent by several processing modules are converted into control instructions, thereby performing collaborative control of multiple people.

[0081] In summary, an embodiment of the present invention provides a multi-person collaborative control device based on brain wave signals, which. The user's EEG information is acquired through an acquisition module, and converted into concentration data through a processing module, and transmitted. Then, the instruction module receives the concentration data sent by multiple processing modules and converts it into control instructions. It is capable of monitoring the user's concentration level in real time and generating corresponding control instructions based on the concentration data. In this way, multi-person collaborative control based on EEG signals can be achieved, thereby effectively improving the efficiency and accuracy of task execution.

[0082] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention should be regarded as falling within the protection scope of the present invention and subject to restrictions as long as they do not lose the essence of the present invention.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0084] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0085] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0086] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0087] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0088] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention.

Claims

1. A multi-person collaborative control device based on brain wave signals, characterized in that: include: A collection module, used to collect EEG information of the user to be collected; A processing module, electrically connected to the acquisition module, and configured to convert the EEG information acquired by the acquisition module into concentration data and send the data; An instruction module, configured to receive the concentration data sent by the processing modules, and to convert the concentration data sent by the processing modules into control instructions; The processing module comprises: An acquisition unit, electrically connected to the acquisition module, and configured to acquire the EEG information acquired by the acquisition module; A processing unit, used for acquiring brain wave type information in the brain wave information, and converting the brain wave type information into concentration data; A sending unit, configured to send the concentration information converted by the processing unit; The processing unit is also used to obtain the real-time beta wave frequency L in the brain wave type information, and determine whether the brain wave state of the user to be collected is in a focused state according to the relationship between the real-time beta wave frequency L and the preset beta wave frequency L1; When L<L1, the processing unit determines that the EEG state of the user to be collected is not in a focused state; When L≥L1, the processing unit determines that the EEG state of the user to be collected is in a focused state, and adjusts the converted concentration data according to the relationship between the real-time β wave frequency L and the preset β wave frequency L1; When the processing unit determines that the EEG state of the user to be collected is in a focused state and adjusts the converted concentration data according to the relationship between the real-time β wave frequency L and the preset β wave frequency L1, it includes: The processing unit is further used to obtain a β wave frequency difference ΔL between the real-time β wave frequency L and the preset β wave frequency L1, and set ΔL=L-L1; The processing unit is further used to compare the beta wave frequency difference ΔL with a preset beta wave frequency difference, and select a corresponding adjustment coefficient according to the comparison result to adjust the converted concentration data; Wherein, a first β wave frequency difference △L1 is preset, a second β wave frequency difference △L2 is preset, a first adjustment coefficient X1 is set, a second adjustment coefficient X2 is set, a third adjustment coefficient X3 is set, and △L1<△L2, 1<X1<X2<X3<2; When △L<△L1, the first adjustment coefficient X1 is selected to adjust the converted concentration data; When △L1≤△L<△L2, the second adjustment coefficient X2 is selected to adjust the converted concentration data; When △L≥△L2, the third adjustment coefficient X3 is selected to adjust the converted concentration data; The converted concentration data is adjusted by selecting the i-th adjustment coefficient Xi, and the adjusted concentration data is set to F1, and F1=F×Xi is set, wherein F is the initial data of the converted concentration data, Xi is the adjustment coefficient of the converted concentration data, and i=1, 2 or 3.

2. The multi-person collaborative control device based on brain wave signals according to claim 1, characterized in that: When the processing unit selects the i-th adjustment coefficient Xi to adjust the converted concentration data and obtains the adjusted concentration data F1, it includes: The processing unit is also used to obtain the real-time α wave frequency Y in the brain wave type information, and determine whether the brain wave state of the user to be collected is in a relaxed state according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1; When Y<Y1, the processing unit determines that the EEG state of the user to be collected is not in a relaxed state; When Y≥Y1, the processing unit determines that the EEG state of the user to be collected is in a relaxed state, and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1.

3. The multi-person collaborative control device based on brain wave signals as claimed in claim 2, characterized in that: When the processing unit determines that the EEG state of the user to be collected is in a relaxed state and corrects the adjusted concentration data F1 according to the relationship between the real-time α wave frequency Y and the preset α wave frequency Y1, it includes: The processing unit is further used to obtain the α wave frequency difference △Y between the real-time α wave frequency Y and the preset α wave frequency Y1, and set △Y=Y-Y1. The processing unit is also used to select a corresponding correction coefficient to correct the adjusted concentration data F1 according to the comparison between the α wave frequency difference △Y and the preset α wave frequency difference; The first α wave frequency difference △Y1 is preset, the second α wave frequency difference △Y2 is preset, the first correction coefficient G1, the second correction coefficient G2, and the third correction coefficient G3 are set, and △Y1<△Y2, 0<G1<G2<G3<1; When △Y<△Y1, the third correction coefficient G3 is selected to correct the adjusted concentration data F1; When △Y1≤△Y<△Y2, the second correction coefficient G2 is selected to correct the adjusted concentration data F1; When △Y≥△Y2, the first correction coefficient G1 is selected to correct the adjusted concentration data F1; when the i-th correction coefficient Gi is selected to correct the adjusted concentration data F1, and the corrected concentration data is set to F2, F2=F1×Gi, where Gi is the correction coefficient of the adjusted concentration data F1, i=1, 2, or 3.

4. The multi-person collaborative control device based on brain wave signals as claimed in claim 3, characterized in that: The instruction module comprises: A receiving unit, configured to receive the corrected concentration data F2 sent by the plurality of sending units; The instruction unit is used to convert the corrected concentration data F2 sent by the sending units into the control instruction.

5. The multi-person collaborative control device based on brain wave signals as claimed in claim 4, characterized in that: The instruction unit is also used to obtain the sum Q of the corrected concentration data F2 sent by several sending units, and set Q=((1F2)+(2F2)+(3F2)+…+(nF2)), where n is the sending unit number; the instruction unit is also used to convert the sum Q into a control instruction.

6. The multi-person collaborative control device based on brain wave signals according to claim 5, characterized in that: The receiving unit and the sending unit are respectively provided with a Bluetooth subunit or a 4G, 5G communication subunit.

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