CN219681647U - Game device based on brain-computer interface - Google Patents
Game device based on brain-computer interface Download PDFInfo
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- CN219681647U CN219681647U CN202320017968.XU CN202320017968U CN219681647U CN 219681647 U CN219681647 U CN 219681647U CN 202320017968 U CN202320017968 U CN 202320017968U CN 219681647 U CN219681647 U CN 219681647U
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Abstract
The utility model discloses a game device based on brain-computer interface in the technical field of game devices, which comprises an electroencephalogram cap, wherein the electroencephalogram cap is connected with a brain signal amplifier through a wire, a data acquisition module, a data processing module, an analysis judging module and a control module are arranged on the electroencephalogram cap, and the data acquisition module, the data processing module, the analysis judging module and the control module are in signal connection with each other, wherein the data processing module comprises a filtering processing module and a wavelet smoothing processing module. The utility model has simple structure, and can reduce the possibility of misjudgment through the design of various data processing modes.
Description
Technical Field
The utility model belongs to the technical field of game devices, and particularly relates to a game device based on a brain-computer interface.
Background
Brain waves (EEG) are the sum of postsynaptic potentials generated synchronously by a large number of neurons during brain activity, and are a method of recording brain activity by using electrophysiological indicators. The brain wave can record potential signal changes in the brain activity process, and is a representation of nerve cell electrophysiological activity of the cerebral cortex and the scalp surface.
In daily life, ordinary people can send out signals from the brain to control limbs, and a series of actions are completed through neuromuscular coordination to achieve a specific action purpose. But some special people lose exercise ability due to nerve diseases or accidents, etc., which brings about many inconveniences for their daily lives. Taking driving as an example, an ordinary person can control a steering wheel and a gear level by hand and can control an accelerator, a brake and a clutch by foot, so that the control of the automobile is realized. And the disabled cannot complete the series of actions to realize the control of the automobile due to the loss of the movement capability. In addition to driving, handicapped people have many things that cannot be done in daily life, and their ability to self-care is therefore greatly limited. By 2020, according to the data of the Chinese disability statistics, the total number of various disabled people in China reaches 8500 ten thousand, and how to help the disabled people to improve the life self-care ability is a problem with practical significance at present. Since 1857 first discovered brain waves by Richard Caton, a physicist in England, scientists around the world have developed research into brain waves. With the continuous and deep research and the rapid development of subjects such as computers, automatic control, electronic communication and the like, a new research field of multidisciplinary fusion, namely brain-computer interface technology (Brain Computer Interface, BCI), is created. The brain-computer interface is a novel man-machine interaction mode, and can output control signals through electronic equipment such as a computer and the like, so that the communication interaction between a person and external environment and between the person and equipment can be realized, and the participation of a peripheral nervous system and muscle tissues is not needed.
The blink brain wave signal is used herein, specifically, the slow potential duration of the cerebral cortex is about 300 milliseconds when the duration is short, and a plurality of seconds can be possibly reached when the duration is long, so that the blink brain wave signal is a relatively slow frequency component in the cerebral cortex electric signal. The slow potential of the cerebral cortex has a great correlation with the excitation degree of the cerebral cortex, and can present different potential situations according to the excitation difference of the cerebral cortex. When brain thinking is active, the excitation of the cerebral cortex is enhanced, and the slow potential of the cerebral cortex can be negatively transformed; when brain thinking is not very active, the excitability of the cerebral cortex is reduced, and the slow potential of the cerebral cortex correspondingly changes forward. Research shows that the slow potential of the cerebral cortex is controllable to a certain extent, and after a certain time of special training, most of experimental staff can realize the automatic control of the amplitude change of the slow potential of the cerebral cortex, so that the control signal output of a brain-computer interface can be realized through the slow potential of the cerebral cortex.
For this purpose, the chinese patent publication No. CN102500105B discloses a brain-computer interface-based game device, which includes an electrode, an electroencephalogram signal acquisition box, and a PC. The electrode is used for collecting original analog brain electrical signals generated when an operator opens and closes eyes; the electroencephalogram signal acquisition box is used for amplifying, filtering and analog-to-digital converting the acquired original analog electroencephalogram signal and transmitting the amplified, filtered and analog-to-digital converted electroencephalogram signal to the PC; and the PC receives the digital signals sent by the electroencephalogram signal acquisition box, performs filtering and energy average processing, compares the digital signals with a threshold voltage, and generates a control pulse instruction for controlling the game target operation. The operator can adjust the brain electrical signals by opening and closing eyes, realize the rapid and reliable real-time operation control of the game, and the game is simple to operate, and the brain electrical signal acquisition has the characteristics of high input impedance, high common mode rejection ratio, low noise, low drift and the like.
However, the device only adopts a single filtering mode when processing the data, the processing mode is single, and the possibility of misjudgment is high.
Disclosure of Invention
In order to solve the above problems, the present utility model provides a game device based on a brain-computer interface, which can reduce the possibility of erroneous judgment by designing in various data processing modes.
In order to achieve the above object, the technical scheme of the present utility model is as follows: the game device based on the brain-computer interface comprises an electroencephalogram cap, wherein the electroencephalogram cap is connected with a brain signal amplifier through a wire, a data acquisition module, a data processing module, an analysis judging module and a control module are arranged on the electroencephalogram cap, the data acquisition module, the data processing module, the analysis judging module and the control module are mutually in signal connection, and the data processing module comprises a filtering processing module and a wavelet smoothing processing module.
The basic scheme has the following principle and beneficial effects: when the motorcycle game is operated, the electroencephalogram cap is firstly put on an experimenter, the electroencephalogram signal received by the electroencephalogram cap can be amplified through the action of the electroencephalogram signal amplifier, the data acquisition module acquires signal information, then the acquired data is processed by the data processing module, the acquired data is subjected to multidimensional processing by the filtering processing module and the wavelet smoothing processing module, and therefore the processed data can be suitable for various application environments by only changing part of codes.
The filtering processing module processes the data by adopting FFT filtering, the wavelet smoothing processing module processes the data on the basis of the data processed by the filtering processing module, and the processing module realizes curve smoothing by utilizing a Savitzky-Golay filter, and the processing module can be directly called in a scipy library without redefining a function; therefore, two different data processing ideas are adopted, and the influence of different processing methods on the accuracy is found by transverse comparison, so that a more proper mode is selected according to the situation, and the possibility of misjudgment is reduced.
Then the analysis judging module analyzes and judges the processed data peak value, firstly judges whether to trigger the peak value judging mechanism, if yes, judges that the peak value judging mechanism is triggered, then judges the peak value number, if yes, judges that the peak value judging mechanism is not triggered, then continues to judge, then starts according to the judged peak value control module, the control module mainly utilizes the writing of python codes when realizing, then realizes keyboard control through a pyautotugui third party library, then realizes different drifting problems through the set peak value when controlling the motorcycle, and simultaneously sends out control instructions through the blinking force of a driver, thereby realizing the control of the motorcycle.
Therefore, the device has flexible use scenes, particularly, the disabled people have various unchanged life and actions due to self reasons, the code can be transplanted into equipment which needs to be controlled, the equipment is controlled through the cooperation of the brain-computer interface and the code, the activity range of the disabled people is expanded, the living capacity of the disabled people is improved, the brain-computer interface controls different equipment, the main problem is data acquisition and data processing, and the core algorithm of the project is data processing, so that only part of codes can be changed to be suitable for various application environments. Meanwhile, compared with the traditional control mode, the brain control mode is the biggest difference in the man-machine interaction mode and the control method of the equipment; moreover, the research can be further expanded into man-machine intelligent interaction, and the interaction is performed by combining artificial intelligence.
Further, the filtering processing module comprises a normalization unit and a filtering unit.
The basic scheme has the following principle and beneficial effects: the normalization means performs normalization processing on the raw data, and keeps the data on the ordinate of the image drawn from the data in the 0-1 section, so that the parameters given to the peak judgment can be roughly determined. The peak value processed by the normalization unit still has a large amount of clutter, the clutter is huge in quantity, the accurate peak value position is interfered, the running time of a program is slowed down, and therefore, the filtering unit is required to be used for further processing, and when the filtering unit is used for processing, a signal can be transformed into a frequency domain by adopting a fast algorithm of discrete Fourier transform, so that a plurality of useless waves are filtered, a main body is reserved, and the peak value characteristics are obvious.
Further, the wavelet smoothing processing module comprises a wavelet filtering unit and an assignment processing unit.
The basic scheme has the following principle and beneficial effects: the ordinate of the peak value processed by the normalization unit and the filtering unit can still only determine a range, and the determined parameters can be suitable for most situations, but partial misjudgment exists, so that the wavelet filtering unit is utilized to process, and a baseline is envisaged during the processing to judge the fluctuation of the peak value. The peak value can be judged by the wavelet filtering unit after processing, but the peak value can be judged by reading each plurality of data and then comparing monotonicity, so that a critical condition can occasionally appear, when accidental fluctuation appears, error occurs in the judgment of monotonicity, the peak value is wrongly judged, when the assignment processing unit works, the top of the peak value is assigned to be 1, and the rest is assigned to be 0, so that the peak value can be accurately identified, and only 0 and 1 are needed to be identified through optimization, thereby greatly improving the accuracy, improving the running speed of a program, and being faster in responding to brain waves.
Further, a binding belt and a locking buckle are arranged on the electroencephalogram cap.
The basic scheme has the following principle and beneficial effects: after the electroencephalogram cap is taken, the binding band is locked and locked, so that the electroencephalogram cap is fixed on the head of an operator, accidental dropping of the electroencephalogram cap is reduced, and subsequent detection of electroencephalogram signals is affected.
Further, the strap is an elastic strap.
The basic scheme has the following principle and beneficial effects: the elastic bandage design can make the range of the device suitable for the object comparatively big to the nimble application of the device of being convenient for.
Further, the locking buckle is provided with anti-skid patterns.
The basic scheme has the following principle and beneficial effects: the design of the anti-skid lines can increase the friction force when the hand contacts with the locking buckle, thereby being convenient for binding the binding belt.
Drawings
Fig. 1 is a schematic diagram of an electroencephalogram cap of a game device based on a brain-computer interface in an embodiment of the utility model.
Fig. 2 is a schematic block diagram of a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 3 is a schematic operation flow chart of a game device based on a brain-computer interface according to an embodiment of the utility model.
Fig. 4 is a schematic diagram illustrating the operation of a filtering processing module in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 5 is a schematic diagram illustrating the operation of a wavelet smoothing module in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 6 is a schematic diagram of collected data in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 7 is a schematic diagram of collected data in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 8 is a schematic diagram of peaks in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 9 is a schematic diagram of peaks in a game device based on a brain-computer interface according to an embodiment of the present utility model.
Fig. 10 is a schematic diagram illustrating an operation of a game device based on a brain-computer interface according to an embodiment of the present utility model.
Detailed Description
The following is a further detailed description of the embodiments:
reference numerals in the drawings of the specification include: an electroencephalogram cap 1, a locking buckle 2, a binding band 3 and a brain signal amplifier 4.
An embodiment is substantially as shown in figures 1, 2 and 3 of the accompanying drawings: game device based on brain-computer interface, including the brain-computer cap, be equipped with elastic bandage and locking knot on the brain-computer cap, be equipped with anti-skidding line on the locking knot, the brain-computer cap is connected with brain signal amplifier through the wire, install data acquisition module on the brain-computer cap, data processing module, analysis judgement module and control module, data acquisition module, data processing module, analysis judgement module and control module are all mutually signal connection, wherein data processing module includes filtering processing module and wavelet smooth processing module, filtering processing module includes normalization unit and filtering unit, wavelet smooth processing module includes wavelet filtering unit and assigned processing unit.
The specific implementation process is as follows: when the motorcycle game is operated, the electroencephalogram cap is firstly put on an experimenter, the electroencephalogram signal received by the electroencephalogram cap can be amplified through the action of the electroencephalogram signal amplifier, the data acquisition module acquires signal information, then the acquired data is processed by the data processing module, the acquired data is subjected to multidimensional processing by the filtering processing module and the wavelet smoothing processing module, and therefore the processed data can be suitable for various application environments by only changing part of codes. As shown in fig. 6, the data wave image and the electrode position can be seen when the data is acquired.
When the filtering processing module processes data, the normalization unit and the filtering unit are used for processing the original data, the normalization unit is used for normalizing the original data, and the data of the ordinate of the image drawn by the data is kept in a range of 0-1, so that the parameters given by peak judgment can be approximately determined in a range. The peak value processed by the normalization unit still has a large amount of clutter, the clutter is huge in amount, the accurate peak value position is interfered, the running time of a program is slowed down, therefore, the filter unit is required to be used for further processing, and when the filter unit is used for processing, a signal can be transformed into a frequency domain by adopting a fast algorithm of discrete Fourier transform, so that a plurality of useless waves are filtered, a main body is reserved, and the peak value characteristics are obvious; wherein the computation of the fast algorithm of the discrete fourier transform is as follows:
polynomial a (x) =a 0 +a 1 x+a 2 x 2 +...+a n-1 x n-1
Each item of a (x) is divided into two parts according to the parity of the subscript:
A(x)＝a 0 +a 2 x 2 +...+a n-2 x n-2 +x*(a 1 +a 3 x 2 +...+a n-1 x n-2 )
two polynomials A are provided 0 (x) And A 1 (x) And (3) making:
A 0 (x)＝a 0 x 0 +a 2 x 1 +...+a n-2 x n/2-1
A 1 (x)＝a 1 x 0 +a 3 x 1 +...+a n-1 x n/2-1
thus, a (x) =a 0 (x 2 )+x*A 1 (x 2 )
Let k < n, the generationInto (I)
As shown in fig. 4, the left side is the data image processed by the normalization unit, and the right side is the data image processed by the filtering unit.
The wavelet smoothing processing module processes the data, the wavelet filtering unit processes the data processed by the filtering processing module, the Savitzky-Golay filter is utilized to realize curve smoothing during processing, and the processing module can be directly called in the scipy library during processing, and no function is required to be defined; meanwhile, the reference of the peak value after processing through the wavelet filtering unit can be judged, but the peak value is judged by reading each plurality of data and then comparing monotonicity, so that a critical situation can occasionally appear, when accidental fluctuation occurs, error judgment can occur in the judgment of monotonicity, the peak value is wrongly judged, when the assignment processing unit works, the top of the peak value is assigned to be 1, and the rest is assigned to be 0, so that the peak value can be accurately identified, and only 0 and 1 are needed to be identified through optimization, thereby greatly improving the accuracy, improving the running speed of a program, and being faster in response to brain waves. Therefore, two different data processing ideas are adopted, and the transverse comparison is beneficial to finding out the influence of different processing methods on the accuracy, so that a more proper mode is selected according to the situation. As shown in fig. 5, the left image is a data image processed by the wavelet filtering unit, and the right image is a data image processed by the assignment processing unit.
Then the analysis judging module analyzes and judges the processed data peak value, firstly judges whether to trigger the peak value judging mechanism, if yes, judges that the peak value judging mechanism is triggered, then judges the peak value number, if yes, judges that the peak value judging mechanism is not triggered, then continues to judge, then starts according to the judged peak value control module, the control module mainly utilizes the writing of python codes when realizing, then realizes keyboard control through a pyautotugui third party library, then realizes different drifting problems through the set peak value when controlling the motorcycle, and simultaneously sends out control instructions through the blinking force of a driver, thereby realizing the control of the motorcycle. As shown in fig. 7, the motorcycle game situation on the left upper part can be observed, the code running result at the time is shown on the left lower part, and the data collected in real time by the software is shown on the right side. As shown in fig. 8, a case of the data image when the peak is not reached can be observed, and as shown in fig. 9, a case of the data image when the peak is reached can be observed. As shown in fig. 10, observations can be made of the runtime situation.
Therefore, the device has flexible use scenes, particularly, the disabled people have various unchanged life and actions due to self reasons, the code can be transplanted into equipment which needs to be controlled, the equipment is controlled through the cooperation of the brain-computer interface and the code, the activity range of the disabled people is expanded, the living capacity of the disabled people is improved, the brain-computer interface controls different equipment, the main problem is data acquisition and data processing, and the core algorithm of the project is data processing, so that only part of codes can be changed to be suitable for various application environments. Meanwhile, compared with the traditional control mode, the brain control mode is the biggest difference in the man-machine interaction mode and the control method of the equipment; moreover, the research can be further expanded into man-machine intelligent interaction, and the interaction is performed by combining artificial intelligence.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing is merely an embodiment of the present utility model, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application date or before the priority date, can know all the prior art in the field, and has the capability of applying the conventional experimental means before the date, and a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present utility model, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present utility model. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present utility model, and these should also be considered as the scope of the present utility model, which does not affect the effect of the implementation of the present utility model and the utility of the patent. The protection scope of the present utility model is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. Game device based on brain-computer interface, its characterized in that: the electroencephalogram cap is connected with a brain signal amplifier through a wire, a data acquisition module, a data processing module, an analysis judging module and a control module are arranged on the electroencephalogram cap, the data acquisition module, the data processing module, the analysis judging module and the control module are in signal connection with each other, and the data processing module comprises a filtering processing module and a wavelet smoothing processing module.
2. The brain-computer interface based game device according to claim 1, wherein: the filtering processing module comprises a normalization unit and a filtering unit.
3. The brain-computer interface based game device according to claim 2, wherein: the wavelet smoothing processing module comprises a wavelet filtering unit and an assignment processing unit.
4. A brain-computer interface based game device according to claim 3, wherein: the electroencephalogram cap is provided with a binding band and a locking buckle.
5. The brain-computer interface based game device according to claim 4, wherein: the binding band is an elastic binding band.
6. The brain-computer interface based game device according to claim 5, wherein: the locking buckle is provided with anti-slip patterns.
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