CN108144291A - Game control method and Related product based on brain wave - Google Patents
Game control method and Related product based on brain wave Download PDFInfo
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Classifications
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/20—Input arrangements for video game devices
- A63F13/21—Input arrangements for video game devices characterised by their sensors, purposes or types
- A63F13/212—Input arrangements for video game devices characterised by their sensors, purposes or types using sensors worn by the player, e.g. for measuring heart beat or leg activity
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/40—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment
- A63F13/42—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2250/00—Miscellaneous game characteristics
- A63F2250/26—Miscellaneous game characteristics the game being influenced by physiological parameters
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/10—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals
- A63F2300/1012—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals involving biosensors worn by the player, e.g. for measuring heart beat, limb activity
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/30—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by output arrangements for receiving control signals generated by the game device
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Abstract
This application provides a kind of game control method based on brain wave, the method is applied in electronic device, and described method includes following steps：Obtain brain wave data；When the electronic device is in scene of game, determine the corresponding multiple control operations of the scene of game, the brain wave data is input to calculating model of neural networks as input data, result of calculation is calculated, determine that the result of calculation operates corresponding at least one operation in the multiple control；At least one operation is performed to leading role in the play scene.The technical solution that the application provides has the advantages that user experience is high.
Description
Technical Field
The application relates to the technical field of terminal equipment and games, in particular to a game control method based on brain waves and a related product.
Background
In the prior art, mobile terminals (such as mobile phones, tablet computers, etc.) have become electronic devices preferred and most frequently used by users. Along with the popularization of smart phones, the interaction between people and the smart phones is more and more diversified, such as voice, fingerprints, irises, human faces, images and the like, but the information sent by the engine brain of a human body is not related at present. The existing game scene realizes the control of the game based on the press control of the user on the touch screen, and the non-touch operation of the game cannot be realized, so that the experience degree of the user on the game is influenced.
Content of application
The embodiment of the application provides a game control method based on brain waves and a related product, which can realize the non-touch operation of the brain waves on games and improve the user experience.
In a first aspect, an embodiment of the present application provides an electronic device, including: an application processor AP; the electronic device further includes: a brain wave part connected with the AP through at least one circuit;
the brain wave component is used for acquiring brain wave data;
the AP is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene, inputting the electroencephalogram data serving as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
In a second aspect, a brain wave-based game control method is provided, which is applied in an electronic device, and includes the following steps:
acquiring electroencephalogram data;
when the electronic device is in a game scene, determining a plurality of control operations corresponding to the game scene, inputting the electroencephalogram data as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
In a third aspect, an electronic device is provided, the electronic device comprising: a processing unit, a brain wave component, and a circuit,
the brain wave component is used for acquiring brain wave data;
the processing unit is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene, inputting the electroencephalogram data serving as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the plurality of control operations of the calculation result; and executing the at least one operation on the chief character in the game scene.
In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method provided in the second aspect.
In a fifth aspect, there is provided a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform the method provided by the second aspect.
The embodiment of the application has the following beneficial effects:
according to the technical scheme, when the game scene where the user is located is determined, the corresponding control operations are determined, then the brain wave data are input into the neural network calculation model as input data to be calculated to obtain a calculation result, and the corresponding at least one operation is determined, so that the game pivot is operated according to the brain wave data, the non-contact control is achieved, and the experience degree of the user is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 1a is a waveform diagram of a delta wave.
Fig. 1b is a waveform diagram of a θ wave.
fig. 1c is a waveform diagram of an α -wave.
fig. 1d is a waveform diagram of the β wave.
Fig. 2 is a schematic view of an electronic device disclosed in an embodiment of the present application.
Fig. 3 is a schematic diagram of insertion of input data according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating a game control method based on brain waves according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a mobile phone disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device in the present application may include a smart phone (e.g., an Android phone, an iOS phone, a windows phone, etc.), a tablet computer, a palm computer, a notebook computer, a Mobile internet device (MID, Mobile internet devices), or a wearable device, and the electronic devices are merely examples, but not exhaustive, and include but are not limited to the electronic devices, and for convenience of description, the electronic devices are referred to as User Equipment (UE) in the following embodiments. Of course, in practical applications, the user equipment is not limited to the above presentation form, and may also include: intelligent vehicle-mounted terminal, computer equipment and the like.
In an electronic apparatus provided in a first aspect, the neural network computational model includes: a mobile neural network calculation model, a skill neural network calculation model and an auxiliary neural network calculation model;
the AP is specifically configured to input data to a mobile neural network computation model to perform a multi-layer forward operation to obtain a first forward operation result, input data to a skill neural network computation model to perform a multi-layer forward operation to obtain a second forward operation result, input data to an auxiliary neural network computation model to perform a multi-layer forward operation to obtain a third forward operation result, determine a first mobile operation corresponding to the first forward operation result, determine a second skill operation corresponding to the second forward operation result, determine a third auxiliary operation corresponding to the third forward operation result, detect whether the first mobile operation, the second skill operation, and the third auxiliary operation conflict with each other, and determine at least one operation as the first mobile operation, the second skill operation, and the third auxiliary operation when the first mobile operation, the second skill operation, and the third auxiliary operation do not conflict with each other, And a third auxiliary operation.
In the electronic device provided in the first aspect, the AP is specifically configured to, when determining that the first mobile operation, the second skill operation, and the third auxiliary operation conflict, determine a neural network model calculation error, update a training template of the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model, and perform a training operation on the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model using the updated training module to obtain an error-corrected mobile neural network calculation model, skill neural network calculation model, and auxiliary neural network calculation model.
In the electronic device provided in the first aspect, the AP is specifically configured to input the first movement operation, the second skill operation, and the third auxiliary operation into the game for execution at the same time, and if the game can execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, determine that the first movement operation, the second skill operation, and the third auxiliary operation do not conflict, and if the game cannot execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, determine that the first movement operation, the second skill operation, and the third auxiliary operation conflict.
In the electronic apparatus provided in the first aspect, the AP is specifically configured to extract a maximum value of a plurality of elements in the first forward operation result matrix, extract a position P1 of the forward operation result matrix corresponding to the maximum value, if the maximum value is larger than a set threshold value, determining that the operation corresponding to the position P1 is a first moving operation, extracting the maximum value of a plurality of elements in the second forward operation result matrix, extracting the position P2 of the forward operation result matrix corresponding to the maximum value, if the maximum value is larger than a set threshold value, determining the operation corresponding to the position P2 as a second skill operation, extracting the maximum value of a plurality of elements in the third forward calculation result matrix, extracting the position P3 of the forward calculation result matrix corresponding to the maximum value, if the maximum value is greater than the set threshold value, the operation corresponding to the position P3 is determined as the third assist operation.
In a method provided in a second aspect, the neural network computational model includes: a mobile neural network calculation model, a skill neural network calculation model and an auxiliary neural network calculation model; the step of inputting the electroencephalogram data as input data into a neural network computational model for computation to obtain a computation result, and determining at least one operation corresponding to the computation result in the plurality of control operations includes:
inputting input data into a mobile neural network computation model to execute multilayer forward computation to obtain a first forward computation result, inputting the input data into a skill neural network computation model to execute multilayer forward computation to obtain a second forward computation result, inputting the input data into an auxiliary neural network computation model to execute multilayer forward computation to obtain a third forward computation result, determining a first mobile operation corresponding to the first forward computation result, determining a second skill operation corresponding to the second forward computation result, determining a third auxiliary operation corresponding to the third forward computation result, and detecting whether the first mobile operation, the second skill operation and the third auxiliary operation conflict or not, and if the first movement operation, the second skill operation and the third auxiliary operation are not in conflict, determining at least one operation as the first movement operation, the second skill operation and the third auxiliary operation.
In a second aspect, there is provided a method further comprising:
and if the conflict of the first mobile operation, the second skill operation and the third auxiliary operation is determined, determining a calculation error of the neural network model, updating training templates of the mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model, and executing training operation on the mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model by using the updated training module to obtain an error-corrected mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model.
In a method provided by the second aspect, the detecting whether the first moving operation, the second skill operation, and the third auxiliary operation conflict includes:
and simultaneously inputting the first movement operation, the second skill operation and the third auxiliary operation into the game for execution, if the game can simultaneously execute the first movement operation, the second skill operation and the third auxiliary operation, determining that the first movement operation, the second skill operation and the third auxiliary operation are not in conflict, and if the game cannot simultaneously execute the first movement operation, the second skill operation and the third auxiliary operation, determining that the first movement operation, the second skill operation and the third auxiliary operation are in conflict.
Referring to fig. 1, fig. 1 is a schematic view of an electronic device according to an embodiment of the present disclosure, fig. 1 is a schematic view of an electronic device 100 according to an embodiment of the present disclosure, where the electronic device 100 includes: the brain wave touch screen comprises a shell 110, a circuit board 120, a battery 130, a cover plate 140, a touch control display screen 150 and a brain wave part 170, wherein the circuit board 120; the circuit board 120 may further include: the application processor AP190, the brain wave section 170. The above-mentioned brain wave part 170 may be different devices according to different apparatuses for collecting brain waves, for example, if brain waves are collected by electronic devices, the brain wave part 170 may be a brain wave sensor or a brain wave collector. The brain wave part 170 may be a brain wave transceiver if brain waves are collected through peripheral devices. Of course, in practical applications, other brain wave devices may be used, and the embodiments of the present invention are not limited to the specific expression of the brain wave components.
The touch Display screen may be a Thin Film Transistor-Liquid Crystal Display (TFT-LCD), a Light Emitting Diode (LED) Display screen, an Organic Light Emitting Diode (OLED) Display screen, or the like.
Different neural activity produces different brain wave patterns and thus presents different brain states. Different brain wave patterns emit brain waves with different amplitudes and frequencies, and besides the brain waves, contraction of muscles also generates different patterns of fluctuation, which is called electromyography. The intelligent device can detect muscle movement such as blinking and the like, so that electric waves generated by the muscles can be filtered out when electroencephalogram is measured.
Brain wave (Brain wave) is data obtained by recording Brain activity using electrophysiological indicators, and is formed by summing the postsynaptic potentials generated synchronously by a large number of neurons during Brain activity. It records the electrical wave changes during brain activity, which is a general reflection of the electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp.
the brain waves are spontaneous rhythmic nerve electrical activities, the frequency variation range of the brain waves is 1-30 times per second, the brain waves can be generally divided into four wave bands according to the frequency, namely delta (1-3 Hz), theta (4-7 Hz), α (8-13 Hz) and β (14-30 Hz), besides, when a certain event is focused, gamma waves with higher frequency than β waves are often seen, the frequency is 30-80 Hz, the wave amplitude range is not fixed, and other normal brain waves with special waveforms, such as camel peak waves, sigma waves, lambda waves, kappa-complex waves, mu waves and the like can also appear during sleep.
FIG. 1a shows a waveform of a delta wave, with a frequency of 1 to 3Hz and an amplitude of 20 to 200 μ V. This band is recorded in the temporal and apical lobes when a person is immature during infancy or mental development, and an adult is under extreme fatigue, lethargy or anesthesia.
FIG. 1b shows a waveform of a θ wave, with a frequency of 4 to 7Hz and an amplitude of 5 to 20 μ V. This wave is extremely pronounced in adults who are willing to suffer from frustration or depression, as well as in psychiatric patients.
FIG. 1c shows the waveform of the alpha wave with a frequency of 8 to 13Hz (average 10Hz) and an amplitude of 20 to 100 μ V, which is the basic rhythm of the normal human brain wave and is fairly constant if no external stimulus is applied.
FIG. 1d shows β wave with frequency of 14-30 Hz and amplitude of 100-150 μ V, which appears when people are nervous and emotional agitation or excited, when people wake from shocking dream, the original slow wave rhythm can be replaced by the rhythm immediately.
Referring to fig. 2, fig. 2 is an electronic device provided in the present application, and as shown in fig. 2, the electronic device may include: a touch display screen, an application processor AP202, a brain wave component 203; the touch display screen and brain wave component 203 is connected with the AP202 through at least one circuit 204; optionally, other sensors may be disposed within the electronic device, including but not limited to: cameras, gravity sensors, distance sensors, speakers, etc.
A brain wave section 203 for acquiring brain wave data;
the AP202 is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene;
the above-mentioned various control operations include, but are not limited to: left movement, right movement, up movement, down movement, attack, defense, killing, enriching blood, opening menu and the like.
The AP202 is configured to input the brain wave data as input data to a neural network computational model to obtain a computation result, and determine at least one operation corresponding to the computation result in the plurality of control operations; the at least one operation is performed on the hero in the game scenario.
According to the technical scheme, when the game scene where the user is located is determined, the corresponding control operations are determined, then the brain wave data are used as input data and input into the neural network calculation model to calculate to obtain a calculation result, and the corresponding at least one operation is determined, so that the game pivot is operated according to the brain wave data, non-contact control is achieved, and the experience degree of the user is improved.
The at least one operation may be any one or more of the operations described above.
A plurality of control operations can generally be distinguished into a plurality of categories, one category being mobile operations, for example: left movement, right movement, up movement, down movement, stop. Another category is skill manipulation, such as: attack, defense, must kill skill, standby, etc.; yet another category is ancillary operations such as enriching the blood, gathering gas, opening menus, changing equipment, etc.
The neural network computational model may include: a mobile neural network computational model, a skill neural network computational model and an auxiliary neural network computational model.
The AP202 is specifically configured to input data to the mobile neural network computation model to perform a multi-layer forward operation to obtain a first forward operation result, input the input data to the skill neural network computation model to perform a multi-layer forward operation to obtain a second forward operation result, input the input data to the auxiliary neural network computation model to perform a multi-layer forward operation to obtain a third forward operation result, determine a first mobile operation corresponding to the first forward operation result, determine a second skill operation corresponding to the second forward operation result, determine a third auxiliary operation corresponding to the third forward operation result, and detect whether the first mobile operation, the second skill operation, and the third auxiliary operation conflict, if the first movement operation, the second skill operation, and the third auxiliary operation do not conflict with each other, at least one of the operations is determined to be the first movement operation, the second skill operation, and the third auxiliary operation.
The AP202 is specifically configured to input the first movement operation, the second skill operation, and the third auxiliary operation into the game for execution at the same time, for example, the game can execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, and determine that the first movement operation, the second skill operation, and the third auxiliary operation do not conflict, for example, the game cannot execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, and determine that the first movement operation, the second skill operation, and the third auxiliary operation conflict.
Specifically, the determining the first moving operation corresponding to the first forward operation result may specifically include: extracting the maximum value of a plurality of elements in the first forward operation result matrix, extracting the position P1 (i.e. the values of CI, H, W) of the forward operation result matrix corresponding to the maximum value, and if the maximum value is larger than a set threshold value, determining to start the first moving operation corresponding to the position P1. For example, if CI ═ 1, H ═ 1, and W ═ 1 (i.e., the first position in the first row of the forward direction operation result matrix) correspond to the first shift operation. For the determination mode of the second skill operation and the third auxiliary operation, the determination mode of the first movement operation can be referred to.
Optionally, when determining that the first mobile operation, the second skill operation, and the third auxiliary operation conflict, the AP202 determines a calculation error of the neural network model, updates the training templates of the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model, and performs a training operation on the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model by using the updated training module to obtain an error-corrected mobile neural network calculation model, skill neural network calculation model, and auxiliary neural network calculation model.
The AP202 is specifically configured to extract G amplitudes and G frequencies in the electroencephalogram data, such as 2 × G ═ CI0*H0*W0In the case of/2, G amplitudes and G frequencies are combined into a matrix CI0*H0/2*W0Will matrix CI0*H0/2*W0Inserting data CI for every other row in H direction0*W0Obtaining an input matrix
CI0*H0*W0The insertion data is the average value of two adjacent elements in the H direction, and is input into a matrix CI0*H0*W0Input to a mobile neural network computational model, a skills neural network computational model, and an auxiliary neural network computationThe model obtains a first forward operation result, a second forward operation result and a third forward operation result. Specifically, if the data of the H-direction 2 nd line is inserted, the inserted data is an average value of the H-direction 1 st line and the H-direction 3 rd line. The above CI0*H0*W0The value may specifically be a preset value of the neural network model (the preset value may be determined by sample training or may be set by a user), and specifically, the value of the CI corresponding to the neural network model is0*H0*W0Can be as follows: CI0＝16；H0＝32；W010. Of course, in practical application, CI can also be adopted0、H0、W0Other values are also possible.
This solution increases the number of elements of the input data matrix by inserting data, specifically, as shown in fig. 3, the inserted data is shown in fig. 3. The inserted data is the average value of the adjacent rows, as shown by the arrows in fig. 3, and if the inserted data is the last row of data, the inserted data may be the values of the adjacent rows. As shown in fig. 3, wherein (H1+ H2)/2 represents an average value between the first row and the second row in the H-insertion direction. Where H1 denotes the value of the first row in the H direction, H2 denotes the value of the second row, and the arrow in fig. 3 denotes the insertion of data directly into the corresponding row. As shown in fig. 3, the manner of inserting data is exemplified by the data of row 2 and the data of the last row, and the manner of inserting data for the middle row can be implemented by referring to the manner of inserting data of row 2. This enables the size of the input data to be limited to CI0*H0*W0(ii) a The saturation of the input data can be improved by multiple times of calculation of the neural network, and the higher the saturation of the input data is, namely, the closer the size of the input data is to the CI0*H0*W0The higher the accuracy of the calculation result.
Referring to fig. 4, fig. 4 provides a brain wave-based game control method applied to an electronic device having a structure as shown in fig. 1 or fig. 2, the method including the steps of:
s401, acquiring electroencephalogram data;
step S402, when the electronic device is in a game scene, determining a plurality of control operations corresponding to the game scene, inputting the electroencephalogram data as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
According to the technical scheme, when the game scene where the user is located is determined, the corresponding control operations are determined, then the brain wave data are used as input data and input into the neural network calculation model to calculate to obtain a calculation result, and the corresponding at least one operation is determined, so that the game pivot is operated according to the brain wave data, non-contact control is achieved, and the experience degree of the user is improved.
Specifically, inputting the electroencephalogram data as input data into a neural network computational model for computation to obtain a computation result, wherein the computation result comprises:
extracting G amplitudes and G frequencies, such as 2G-CI, from the electroencephalogram data0*H0*W0In the case of/2, G amplitudes and G frequencies are combined into a matrix CI0*H0/2*W0Will matrix CI0*H0/2*W0Inserting data CI for every other row in H direction0*W0Obtaining an input matrix CI0*H0*W0The insertion data is the average value of two adjacent elements in the H direction, and is input into a matrix CI0*H0*W0And inputting the data into a mobile neural network calculation model, a skill neural network calculation model and an auxiliary neural network calculation model to obtain a first forward calculation result, a second forward calculation result and a third forward calculation result.
Referring to fig. 5, fig. 5 provides an electronic device, including: a processing unit 501, a touch display screen 502, a brain wave component 503 and a circuit,
a brain wave component 503 for acquiring brain wave data;
the processing unit 501 is configured to, when the electronic apparatus is in a game scene, determine a plurality of control operations corresponding to the game scene, input the electroencephalogram data as input data to a neural network computational model for computation to obtain a computation result, and determine at least one operation corresponding to the plurality of control operations of the computation result; and executing the at least one operation on the chief character in the game scene.
According to the technical scheme, when the game scene where the user is located is determined, the corresponding control operations are determined, then the brain wave data are used as input data and input into the neural network calculation model to calculate to obtain a calculation result, and the corresponding at least one operation is determined, so that the game pivot is operated according to the brain wave data, non-contact control is achieved, and the experience degree of the user is improved.
Fig. 6 is a block diagram illustrating a partial structure of a mobile phone related to a mobile terminal according to an embodiment of the present disclosure. Referring to fig. 6, the handset includes: a Radio Frequency (RF) circuit 910, a memory 920, an input unit 930, a sensor 950, an audio circuit 960, a Wireless Fidelity (WiFi) module 970, an application processor AP980, and a power supply 990, a brain wave unit 999, and the like. Those skilled in the art will appreciate that the handset configuration shown in fig. 6 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile phone in detail with reference to fig. 6:
the input unit 930 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the cellular phone. Specifically, the input unit 930 may include a touch display screen 933, a fingerprint recognition apparatus 931, a face recognition apparatus 936, an iris recognition apparatus 937, and other input devices 932. The input unit 930 may also include other input devices 932. In particular, other input devices 932 may include, but are not limited to, one or more of physical keys, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. Wherein,
the brain wave component 999 is used for acquiring brain wave data and transmitting the brain wave data to the AP 980.
The AP980 is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene, inputting the electroencephalogram data serving as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
Optionally, the AP980 is specifically configured to input data to the mobile neural network computation model to perform a multi-layer forward operation to obtain a first forward operation result, input data to the skill neural network computation model to perform a multi-layer forward operation to obtain a second forward operation result, input data to the auxiliary neural network computation model to perform a multi-layer forward operation to obtain a third forward operation result, determine a first mobile operation corresponding to the first forward operation result, determine a second skill operation corresponding to the second forward operation result, determine a third auxiliary operation corresponding to the third forward operation result, detect whether the first mobile operation, the second skill operation, and the third auxiliary operation conflict, and if the first mobile operation, the second skill operation, and the third auxiliary operation do not conflict, determine at least one operation as the first mobile operation, the second skill operation, and the third auxiliary operation, And a third auxiliary operation.
Optionally, the AP980 is further configured to, when determining that the first mobile operation, the second skill operation, and the third auxiliary operation conflict, determine a neural network model calculation error, update a training template of the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model, and perform a training operation on the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model using the updated training module to obtain an error-corrected mobile neural network calculation model, skill neural network calculation model, and auxiliary neural network calculation model.
Optionally, the AP980 is specifically configured to input the first movement operation, the second skill operation, and the third auxiliary operation into the game for execution at the same time, and if the game can execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, determine that the first movement operation, the second skill operation, and the third auxiliary operation do not conflict, and if the game cannot execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, determine that the first movement operation, the second skill operation, and the third auxiliary operation conflict.
Optionally, the AP980 is specifically configured to extract a maximum value of a plurality of elements in the first forward operation result matrix, extract a position P1 of the forward operation result matrix corresponding to the maximum value, determine that the operation corresponding to the position P1 is a first moving operation, extract a maximum value of a plurality of elements in the second forward operation result matrix, extract a position P2 of the forward operation result matrix corresponding to the maximum value, determine that the operation corresponding to the position P2 is a second skill operation, extract a maximum value of a plurality of elements in the third forward operation result matrix, extract a position P3 of the forward operation result matrix corresponding to the maximum value, and determine that the operation corresponding to the position P3 is a third auxiliary operation if the maximum value is greater than a set threshold.
The AP980 is a control center of the mobile phone, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions and processes of the mobile phone by operating or executing software programs and/or modules stored in the memory 920 and calling data stored in the memory 920, thereby integrally monitoring the mobile phone. Optionally, AP980 may include one or more processing units; alternatively, the AP980 may integrate an application processor that handles primarily the operating system, user interface, and applications, etc., and a modem processor that handles primarily wireless communications. It will be appreciated that the modem processor described above may not be integrated into the AP 980.
Further, the memory 920 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
RF circuitry 910 may be used for the reception and transmission of information. In general, the RF circuit 910 includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuit 910 may also communicate with networks and other devices via wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to Global System for mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), and the like.
The handset may also include at least one sensor 950, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the touch display screen according to the brightness of ambient light, and the proximity sensor may turn off the touch display screen and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
Audio circuitry 960, speaker 961, microphone 962 may provide an audio interface between a user and a cell phone. The audio circuit 960 may transmit the electrical signal converted from the received audio data to the speaker 961, and the audio signal is converted by the speaker 961 to be played; on the other hand, the microphone 962 converts the collected sound signal into an electrical signal, and the electrical signal is received by the audio circuit 960 and converted into audio data, and the audio data is processed by the audio playing AP980, and then sent to another mobile phone via the RF circuit 910, or played to the memory 920 for further processing.
WiFi belongs to short-distance wireless transmission technology, and the mobile phone can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 970, and provides wireless broadband Internet access for the user. Although fig. 6 shows the WiFi module 970, it is understood that it does not belong to the essential constitution of the handset, and can be omitted entirely as needed within the scope of not changing the essence of the application.
The handset also includes a power supply 990 (e.g., a battery) for supplying power to various components, and optionally, the power supply may be logically connected to the AP980 via a power management system, so that functions of managing charging, discharging, and power consumption are implemented via the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, a light supplement device, a light sensor, and the like, which are not described herein again.
It can be seen that, through this application embodiment, after the acceleration data is gathered, the state of electron device is confirmed according to the acceleration data, when confirming for falling the state, gather the first picture on ground through the camera, then obtain the distance on electron device's ground according to acceleration value and acquisition time, extract electron device's second picture (specifically can be the appearance picture), just so can generate and have electron device fall the 3D animation on ground, improved user's experience degree.
Embodiments of the present application also provide a computer storage medium storing a computer program for electronic data exchange, the computer program causing a computer to execute a part or all of the steps of any one of the brain wave-based game control methods as set forth in the above method embodiments.
Embodiments of the present application also provide a computer program product including a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the brain wave-based game control methods as set forth in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (12)
1. An electronic device, the electronic device comprising: an application processor AP; characterized in that, the electronic device further comprises: a brain wave part connected with the AP through at least one circuit;
the brain wave component is used for acquiring brain wave data;
the AP is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene, inputting the electroencephalogram data serving as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
2. The electronic device of claim 1, wherein the neural network computational model comprises: a mobile neural network calculation model, a skill neural network calculation model and an auxiliary neural network calculation model;
the AP is specifically configured to input data to a mobile neural network computation model to perform a multi-layer forward operation to obtain a first forward operation result, input data to a skill neural network computation model to perform a multi-layer forward operation to obtain a second forward operation result, input data to an auxiliary neural network computation model to perform a multi-layer forward operation to obtain a third forward operation result, determine a first mobile operation corresponding to the first forward operation result, determine a second skill operation corresponding to the second forward operation result, determine a third auxiliary operation corresponding to the third forward operation result, detect whether the first mobile operation, the second skill operation, and the third auxiliary operation conflict with each other, and determine at least one operation as the first mobile operation, the second skill operation, and the third auxiliary operation when the first mobile operation, the second skill operation, and the third auxiliary operation do not conflict with each other, And a third auxiliary operation.
3. The electronic device of claim 2,
the AP is specifically configured to, when determining that the first mobile operation, the second skill operation, and the third auxiliary operation conflict, determine a neural network model calculation error, update a training template of the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model, and perform a training operation on the mobile neural network calculation model, the skill neural network calculation model, and the auxiliary neural network calculation model using the updated training module to obtain an error-corrected mobile neural network calculation model, skill neural network calculation model, and auxiliary neural network calculation model.
4. The electronic device of claim 2 or 3,
the AP is specifically configured to input the first movement operation, the second skill operation, and the third auxiliary operation into the game for execution at the same time, and determine that the first movement operation, the second skill operation, and the third auxiliary operation do not conflict if the game can execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time, and determine that the first movement operation, the second skill operation, and the third auxiliary operation conflict if the game cannot execute the first movement operation, the second skill operation, and the third auxiliary operation at the same time.
5. The electronic device of claim 3,
the AP is specifically configured to extract a maximum value of a plurality of elements in the first forward operation result matrix, extract a position P1 of the forward operation result matrix corresponding to the maximum value, determine that the operation corresponding to the position P1 is a first move operation if the maximum value is greater than a set threshold, extract a maximum value of a plurality of elements in the second forward operation result matrix, extract a position P2 of the forward operation result matrix corresponding to the maximum value, determine that the operation corresponding to the position P2 is a second skill operation if the maximum value is greater than the set threshold, extract a maximum value of a plurality of elements in the third forward operation result matrix, extract a position P3 of the forward operation result matrix corresponding to the maximum value, and determine that the operation corresponding to the position P3 is a third assist operation if the maximum value is greater than the set threshold.
6. A game control method based on brain waves is characterized in that the method is applied to an electronic device and comprises the following steps:
acquiring electroencephalogram data;
when the electronic device is in a game scene, determining a plurality of control operations corresponding to the game scene, inputting the electroencephalogram data as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the calculation result in the plurality of control operations; and executing the at least one operation on the chief character in the game scene.
7. The method of claim 5, wherein the neural network computational model comprises: a mobile neural network calculation model, a skill neural network calculation model and an auxiliary neural network calculation model; the step of inputting the electroencephalogram data as input data into a neural network computational model for computation to obtain a computation result, and determining at least one operation corresponding to the computation result in the plurality of control operations includes:
inputting input data into a mobile neural network computation model to execute multilayer forward computation to obtain a first forward computation result, inputting the input data into a skill neural network computation model to execute multilayer forward computation to obtain a second forward computation result, inputting the input data into an auxiliary neural network computation model to execute multilayer forward computation to obtain a third forward computation result, determining a first mobile operation corresponding to the first forward computation result, determining a second skill operation corresponding to the second forward computation result, determining a third auxiliary operation corresponding to the third forward computation result, and detecting whether the first mobile operation, the second skill operation and the third auxiliary operation conflict or not, and if the first movement operation, the second skill operation and the third auxiliary operation are not in conflict, determining at least one operation as the first movement operation, the second skill operation and the third auxiliary operation.
8. The method of claim 7, further comprising:
and if the conflict of the first mobile operation, the second skill operation and the third auxiliary operation is determined, determining a calculation error of the neural network model, updating training templates of the mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model, and executing training operation on the mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model by using the updated training module to obtain an error-corrected mobile neural network calculation model, the skill neural network calculation model and the auxiliary neural network calculation model.
9. The method according to claim 7 or 8, wherein said detecting whether said first movement operation, said second skill operation, said third assistance operation conflict comprises:
and simultaneously inputting the first movement operation, the second skill operation and the third auxiliary operation into the game for execution, if the game can simultaneously execute the first movement operation, the second skill operation and the third auxiliary operation, determining that the first movement operation, the second skill operation and the third auxiliary operation are not in conflict, and if the game cannot simultaneously execute the first movement operation, the second skill operation and the third auxiliary operation, determining that the first movement operation, the second skill operation and the third auxiliary operation are in conflict.
10. An electronic device, the electronic device comprising: a processing unit, a brain wave component, and a circuit,
the brain wave component is used for acquiring brain wave data;
the processing unit is used for determining a plurality of control operations corresponding to a game scene when the electronic device is in the game scene, inputting the electroencephalogram data serving as input data into a neural network calculation model for calculation to obtain a calculation result, and determining at least one operation corresponding to the plurality of control operations of the calculation result; and executing the at least one operation on the chief character in the game scene.
11. A computer-readable storage medium, characterized in that it stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method according to any one of claims 6-9.
12. A computer program product, characterized in that the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform the method according to any of claims 6-9.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165731A (en) * | 2018-08-09 | 2019-01-08 | 清华大学 | Electronic neuron network and its parameter setting method |
| CN109646943A (en) * | 2018-12-26 | 2019-04-19 | 网易（杭州）网络有限公司 | A kind for the treatment of method and apparatus being displaced technical ability |
| CN109821234A (en) * | 2019-03-05 | 2019-05-31 | 浙江强脑科技有限公司 | Game control method, mobile terminal and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1719385A (en) * | 2005-07-21 | 2006-01-11 | 高春平 | Physiological signal control device of computer game |
| JP3779150B2 (en) * | 2000-12-05 | 2006-05-24 | 株式会社テクノスジャパン | Computer game device controlled by EEG biofeedback |
| CN101430600A (en) * | 2008-11-05 | 2009-05-13 | 江西蓝天学院 | Game auxiliary control method based on imagination electroencephalogram |
| CN107049308A (en) * | 2017-06-05 | 2017-08-18 | 湖北民族学院 | A kind of idea control system based on deep neural network |
| CN107247893A (en) * | 2017-05-10 | 2017-10-13 | 广东工业大学 | A kind of electric personal identification method of the brain based on genetic algorithm dimensionality reduction |
-
2018
- 2018-02-11 CN CN201810142443.2A patent/CN108144291A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3779150B2 (en) * | 2000-12-05 | 2006-05-24 | 株式会社テクノスジャパン | Computer game device controlled by EEG biofeedback |
| CN1719385A (en) * | 2005-07-21 | 2006-01-11 | 高春平 | Physiological signal control device of computer game |
| CN101430600A (en) * | 2008-11-05 | 2009-05-13 | 江西蓝天学院 | Game auxiliary control method based on imagination electroencephalogram |
| CN107247893A (en) * | 2017-05-10 | 2017-10-13 | 广东工业大学 | A kind of electric personal identification method of the brain based on genetic algorithm dimensionality reduction |
| CN107049308A (en) * | 2017-06-05 | 2017-08-18 | 湖北民族学院 | A kind of idea control system based on deep neural network |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165731A (en) * | 2018-08-09 | 2019-01-08 | 清华大学 | Electronic neuron network and its parameter setting method |
| CN109165731B (en) * | 2018-08-09 | 2020-06-30 | 清华大学 | Electronic neural network and parameter setting method thereof |
| CN109646943A (en) * | 2018-12-26 | 2019-04-19 | 网易（杭州）网络有限公司 | A kind for the treatment of method and apparatus being displaced technical ability |
| CN109646943B (en) * | 2018-12-26 | 2022-11-04 | 网易（杭州）网络有限公司 | Displacement technology processing method and device |
| CN109821234A (en) * | 2019-03-05 | 2019-05-31 | 浙江强脑科技有限公司 | Game control method, mobile terminal and computer readable storage medium |
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