CN115373307A - Embedded device control method and device based on electroencephalogram signals - Google Patents
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Abstract
The application relates to an embedded device control method, device and system based on electroencephalogram signals, computer equipment and a storage medium. The method comprises the following steps: receiving an electroencephalogram signal sent by brain-computer interface equipment; the brain-computer interface equipment analyzes and processes the acquired biological signals of the user to obtain the brain-computer signals; acquiring user-defined electroencephalogram band weight information; converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information; and controlling the embedded equipment according to the control signal. The embodiment of the application can enable the user to trigger different control signals under different brain states, and in addition, the weight of part of wave bands is limited, so that the user can control the embedded equipment more flexibly, and the user can better experience the process of utilizing brain waves to perform idea control.
Description
Technical Field
The application relates to the field of entertainment and demonstration of brain-computer interface technology, in particular to an embedded device control method, device, system, computer device and storage medium based on electroencephalogram signals.
Background
The following statements are merely provided to provide background information related to the present application and may not necessarily constitute prior art.
The portable brain-computer interface equipment has wide application scenes in the aspects of health monitoring, education and entertainment due to the characteristics of small size, portability, safety and the like.
The inventor finds that the portable computer interface equipment has certain consumer-grade application in China at present, the matched products are mostly health detection application or idea games of a mobile terminal, and the portable computer interface equipment is less applied to the aspects of embedded wearing equipment, toys, intelligent household products and the like. In addition, the inventor also notes that most users have little knowledge about brain-computer interface technology, and the use mode of the portable brain-computer interface device is relatively strange for the users at present, and the difficulty of starting hands is relatively high.
Disclosure of Invention
In view of the above-mentioned shortcomings or drawbacks, the present application provides an embedded device control method, apparatus, system, computer device and storage medium based on electroencephalogram signals.
The application provides an embedded device control method based on electroencephalogram signals according to a first aspect, and in one embodiment, the method comprises the following steps:
receiving an electroencephalogram signal sent by brain-computer interface equipment; the brain-computer interface equipment analyzes and processes the acquired biological signals of the user to obtain the brain-computer signals;
acquiring user-defined electroencephalogram band weight information;
converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information;
and controlling the embedded equipment according to the control signal.
In one embodiment, converting the brain electrical signals to control signals according to the brain electrical band weight information comprises:
preprocessing the electroencephalogram signals, and extracting band signals of a plurality of bands from the preprocessed electroencephalogram signals;
and obtaining a control signal according to the plurality of band signals and the weight of each band included in the electroencephalogram band weight information.
In one embodiment, obtaining the control signal according to the weight of each band included in the plurality of band signals and the electroencephalogram band weight information includes:
the control signal is obtained based on the following formula:
where value denotes the control signal, δ op Representing the weight of the delta band, delta adaptive The adaptive adjustment coefficient represents a delta wave band, and delta represents the signal intensity of a wave band signal of the delta wave band; theta op Weight representing the theta band, theta adaptive The self-adaptive adjustment coefficient represents a theta waveband, and theta represents the signal intensity of a waveband signal of the theta waveband; alpha 1 op Represents the weight of the alpha 1 wave band, and alpha 1 represents the signal intensity of the wave band signal of the alpha 1 wave band; alpha 2 op Represents the weight of the alpha 2 wave band, and alpha 2 represents the signal intensity of the wave band signal of the alpha 2 wave band; beta 1 op Represents the weight of the β 1 band, β 1 represents the signal intensity of the band signal of the β 1 band; beta 2 op Represents the weight of the beta 2 band, and beta 2 represents the signal intensity of the band signal of the beta 2 band; gamma 1 op Represents the weight of the gamma 1 wave band, and gamma 1 represents the signal intensity of the wave band signal of the gamma 1 wave band; gamma 2 op Represents the weight of the gamma 2 wave band, and gamma 2 represents the signal intensity of the wave band signal of the gamma 2 wave band; eegsum represents the accumulation of the wave band signals of each wave band; adapt _ num represents an adaptive adjustment parameter.
In one embodiment, the method further comprises:
and presenting waveforms and/or quantized values of the electroencephalogram signals and the wave band signals of all wave bands in real time in a visual interface.
In one embodiment, obtaining user-defined electroencephalogram band weight information includes:
displaying an operation interface;
detecting weight setting operation performed by a user on an operation interface;
and responding to the weight setting operation, and acquiring user-defined electroencephalogram band weight information.
In one embodiment, controlling an embedded device according to a control signal includes:
and sending the control signal to the embedded equipment, wherein the control signal is used for indicating the embedded equipment to convert the control signal into a corresponding control command according to a preset communication protocol and executing the control command.
The present application provides an embedded device control apparatus based on electroencephalogram signals according to a second aspect, in an embodiment, the apparatus includes:
the brain-computer signal receiving module is used for receiving the brain-computer signals sent by the brain-computer interface equipment; the brain-computer interface equipment analyzes and processes the acquired biological signals of the user to obtain the brain-computer signals;
the weight information acquisition module is used for acquiring user-defined electroencephalogram band weight information;
the signal conversion module is used for converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information;
and the control module is used for controlling the embedded equipment according to the control signal.
According to a third aspect, the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the embedded device control method based on electroencephalogram signals provided in any one of the above embodiments are implemented.
The present application provides, according to a fourth aspect, a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the embedded device control method based on electroencephalogram signals provided in any of the above embodiments.
The application provides an embedded device control system based on electroencephalogram signals according to a fifth aspect, and the system comprises a brain-computer interface device, a control device and an embedded device; the control device is used for realizing the steps of the embedded device control method based on the electroencephalogram signals provided by any embodiment.
In the embodiment of the application, the brain-computer interface device analyzes the acquired biological signals of the user to obtain electroencephalogram signals and sends the electroencephalogram signals to the control device; the control equipment receives the electroencephalogram signals sent by the brain-computer interface equipment and obtains electroencephalogram band weight information defined by a user, then the electroencephalogram signals are converted into control signals according to the electroencephalogram band weight information, and the embedded equipment is controlled according to the control signals. In the embodiment of the application, the electroencephalogram band weight information is flexibly adjusted by a user based on self conditions in consideration of possible differences of different brain states, the electroencephalogram signals are converted into the control signals based on the electroencephalogram band weight information defined by the user, and the control signals triggered in different brain states can be autonomously adjusted by the user. Furthermore, the above embodiment of the present application further limits the weight of a part of wave bands, so that the use difficulty can be reduced, the user can switch the control signal more flexibly, the user can better experience the process of performing idea control by using brain waves, and the method is favorable for exciting the curiosity of the user and the exploration desire of leading-edge technologies such as brain-computer interface technologies and the like.
Drawings
FIG. 1 is a diagram of an application environment of an embedded device control method based on electroencephalogram signals in one or more embodiments;
FIG. 2 is a diagram of a software architecture in one or more embodiments;
FIG. 3 is a schematic diagram of a task control flow of a single chip microcomputer in one or more embodiments;
FIG. 4 is a schematic flow diagram of a method for embedded device control based on electroencephalogram signals in one or more embodiments;
FIG. 5 is a block diagram of an embedded device control apparatus based on electroencephalogram signals in one or more embodiments;
FIG. 6 is a diagram of an internal structure of a computer device in one or more embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The application provides an embedded device control method based on electroencephalogram signals. In this embodiment, the embedded device control method based on electroencephalogram signals can be applied to the application environment shown in fig. 1. The computer interface device 10 is a portable computer interface device, and may be a non-invasive single computer interface device, and optionally, the computer interface device 10 may be collocated with a TGAM (ThinkGear Asic Module) chip (a main control chip produced by NeuroSky corporation); further, the modality of the brain-computer interface device 10 may include, but is not limited to, the following modalities: hair bands, helmets, headphones, hats; the control device 20 is installed with pre-developed upper computer software, and the upper computer software can realize an embedded device control method based on electroencephalogram signals when running, that is, user-defined electroencephalogram band weight information can be obtained, electroencephalogram signals sent by the brain-computer interface device 10 can be converted into corresponding control signals based on the electroencephalogram band weight information, and the embedded device 30 is controlled based on the control signals, wherein the control device 20 can be various personal computers, notebook computers, tablet computers, desktop computers and the like, and in addition, optionally, the control device 20 can also be a smart phone; the embedded device 30 may be various wearable devices (such as a watch and a bracelet), toys (such as a car and a gyroscope), smart home products (such as a smart desk lamp and a sound box) and the like which can be controlled based on a single chip microcomputer. The brain-computer interface device 10 and the control device 20, and the control device 20 and the embedded device 30 can communicate by using wireless communication methods including but not limited to bluetooth, RS-485, zigbee (Zigbee), wiFi, and the like. Taking bluetooth as an example, the brain-computer interface device 10, the control device 20, and the embedded device 30 may be equipped with a bluetooth module, and then the brain-computer interface device 10 and the control device 20 may communicate with each other through bluetooth, and the control device 20 and the embedded device 30 may communicate with each other through bluetooth.
Illustratively, in one example, the brain-Computer interface device and the control device, and the control device and the embedded device are communicated through bluetooth, wherein the control device is a PC (Personal Computer), the embedded device is an embedded car, and the control signal is a motor speed control value. Referring to fig. 2, the software architecture in this embodiment includes four parts, namely, a User Interface (UI) layer facing a User, an information display layer, a logic processing layer, and a data storage layer.
And the front end UI layer is used for the human-computer interaction between the user and the PC end and for information display and task selection.
The show layer mainly carries out mutual show through PC end serial ports and embedded touch screen (carry on embedded dolly), includes: the method comprises the steps of PC end Bluetooth serial port communication, embedded system sensing signal acquisition, operation process key information reading, and input command identification for operation debugging.
And the logic layer completes different logic operations according to the input signal and the interactive command. The input signal refers to the signal intensity of eight waveband signals sent by the brain-computer interface device, and the interactive command refers to a command triggered by a user through an operation interface. Specifically, the logic layer functions of the upper computer software running on the PC end include: calculating a TGAM chip transmission signal processing result, judging an abnormal value, setting electroencephalogram wave band weight information, and carrying out statistical analysis on TGAM data; the logic layer function of the software running in the embedded trolley is dead halt judgment self-check controlled by a watchdog program, and the logic layer function can also comprise the identification and signal processing of a connected sensor.
The data layer finishes the reading and writing of system data in a PC storage system, and comprises the following steps: TGAM dataflow logging, embedded sensor dataflow logging.
The task control flow of the single chip microcomputer in the embedded trolley in the example can be seen in fig. 3.
In the process, firstly, hardware modules such as an IO port of a single chip microcomputer, a Bluetooth module, an LED, a touch screen and the like are initialized; the bare computer system of the single chip microcomputer can be used, and multi-task scheduling is realized based on the timer: performing a task of controlling the advancing state of the embedded trolley based on a Bluetooth serial port command in the main function; and carrying out a sensor signal timing transmission task in the timer interrupt.
And when the self-checking of the embedded system module is completed, starting a timer and a main function cycle.
The task of controlling the advancing state of the embedded trolley based on the Bluetooth serial port command is as follows: and circularly waiting for the control instruction, if a new control signal is received, analyzing the signal based on the communication protocol, judging an abnormal value, finally executing a corresponding control command and returning to the execution condition.
And a sensor signal timing sending task: and interrupting the acquisition of the sensor signal every time, performing update detection and abnormal value detection, and finally sending a sensing signal.
When the main program receives the exit instruction, the main program cycle is stopped and the timer is closed.
The method for controlling an embedded device based on electroencephalogram signals provided by the embodiment includes the steps shown in fig. 4, and the method is applied to the control device in fig. 1 as an example.
S110: receiving an electroencephalogram signal sent by brain-computer interface equipment; the brain electrical signal is obtained by analyzing and processing the acquired user biological signal by the brain-computer interface device.
After the user wears the computer-computer interface device, the computer-computer interface device can acquire a user biological signal, namely a user forehead biological electric signal, by adopting a single electrode, and analyze and process the acquired user biological signal through an internal TGAM chip, so that an electroencephalogram signal is obtained. The brain-computer interface equipment can send the obtained brain electrical signals to the control equipment.
S120: and acquiring user-defined electroencephalogram band weight information.
The electroencephalogram band weight information includes weights of a plurality of bands, wherein the plurality of bands may specifically include a delta band, a theta band, an alpha 1 band, an alpha 2 band, a beta 1 band, a beta 2 band, a gamma 1 band, and a gamma 2 band. The definition of each band is shown in table one.
Table one:
in order to facilitate the user to set corresponding weights for each band, the control device may display an operation interface, and the user may further define electroencephalogram band weight information through the operation interface, and exemplarily, the user may set the electroencephalogram band weight information on the operation interface by adopting a manner of dragging, inputting a weight value, and the like; the control equipment can detect the weight setting operation performed by a user on the operation interface, and respond to the weight setting operation to acquire user-defined electroencephalogram band weight information after detecting the weight setting operation. It should be understood that the user may set the corresponding weights for some or all of the bands as desired.
In one embodiment, the control device may also present waveforms and/or quantized values of the received electroencephalogram signal and band signals of each band in real time in the visualization interface. The method and the device can facilitate the user to see the characteristic value of the observed electroencephalogram signal, and set corresponding weight for each wave band based on the observation result.
S130: and converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information.
The converting of the electroencephalogram signal into the control signal according to the electroencephalogram band weight information may include preprocessing the electroencephalogram signal, extracting band signals of a plurality of bands from the preprocessed electroencephalogram signal, and obtaining the control signal according to the weights of the bands included in the plurality of band signals and the electroencephalogram band weight information. The brain-computer interface device can perform processing such as noise reduction and filtering on the electroencephalogram signals, extract band signals of each band shown in the table I from the electroencephalogram signals, and when each band signal is sent to the control device, the control device obtains the control signals according to the band signals of each band and the weight of each band included in the electroencephalogram band weight information.
Specifically, the control signal is obtained based on the following formula:
in the above formula, value represents the control signal, δ op Representing the weight of the delta band, delta adaptive The adaptive adjustment coefficient represents a delta wave band, and delta represents the signal intensity of a wave band signal of the delta wave band; theta op Weight representing the theta band, theta adaptive The self-adaptive adjustment coefficient represents a theta wave band, and theta represents the signal intensity of a wave band signal of the theta wave band; alpha 1 op Represents the weight of the alpha 1 wave band, and alpha 1 represents the signal strength of the wave band signal of the alpha 1 wave band; alpha 2 op Represents the weight of the alpha 2 wave band, and alpha 2 represents the signal intensity of the wave band signal of the alpha 2 wave band; beta 1 op Represents the weight of the β 1 band, β 1 represents the signal intensity of the band signal of the β 1 band; beta 2 op Represents the weight of the β 2 band, and β 2 represents the signal strength of the band signal of the β 2 band; gamma 1 op Represents the weight of the gamma 1 wave band, and gamma 1 represents the signal intensity of the wave band signal of the gamma 1 wave band; gamma 2 op Represents the weight of the gamma 2 wave band, and gamma 2 represents the signal intensity of the wave band signal of the gamma 2 wave band; eegsum represents the accumulation of band signals of each band; adapt _ num represents an adaptive adjustment parameter.
The weighted value of each wave band can be adjusted between 0.2 and 0.8, and the adjustment mode can comprise manual adjustment and self-adaptive adjustment. The user can change the influence of different brain states on the value by adjusting the weight ratio of each wave band. Considering that the proportion of low-frequency signals in the electroencephalogram signals to full-band (frequency is between 1 and 50 Hz) signals is large, even if the weight configuration of each band is adjusted by a user, the value change of value obtained by calculation based on the electroencephalogram signals in different brain states is not large, so that corresponding adaptive adjustment coefficients are set for the delta band and the theta band, the influence of other band signals on the value of value can be enhanced, and the user can change the brain state more easily and flexibly and further change the triggered control signals. For example, if the user maximizes the weight of the δ band, the value calculated by the control device is larger when the user closes eyes and nourishes spirit and empties himself; if the weight of the β 2 band is maximized, the value calculated by the control device is relatively large when the user tries to stare at something, focusing attention.
The above adapt _ num is equivalent to a normalization coefficient for controlling the value between 0 and 1; also, adapt _ num is not a fixed value, which can be adaptively changed.
Specifically, the value before multiplying adapt _ num may be denoted as vbefore, and after the weight matching is completed, the value of vbefore is often calculated to be smaller than 1. Adaptnum _ now = int (1/vbefore), and if Adaptnum _ now is larger than the recorded adapt _ num, an assignment is made to the adapt _ num. That is, the adapt _ num is determined by the minimum value of vbefore, for example, assuming that the vbefore value varies from 0.2 to 0.9 under a certain weight ratio, and after recording 0.2, the adapt _ num is 5.
The self-adaptive adjustment coefficient delta of the delta wave band adaptive Self-adaptive adjusting coefficient theta of theta wave band adaptive And the adaptive adjustment coefficient adapt _ num may be determined according to a test result at the time of an actual test.
S140: and controlling the embedded equipment according to the control signal.
The controlling the embedded device according to the control signal may include sending the control signal to the embedded device, where the control signal is used to instruct the embedded device to convert the control signal into a corresponding control command according to a predetermined communication protocol, and execute the control command.
In the above embodiment, the brain-computer interface device analyzes the collected biological signals of the user to obtain electroencephalogram signals and sends the electroencephalogram signals to the control device; the control equipment receives the electroencephalogram signals sent by the brain-computer interface equipment and obtains electroencephalogram band weight information defined by a user, then the electroencephalogram signals are converted into control signals according to the electroencephalogram band weight information, and the embedded equipment is controlled according to the control signals. According to the method and the device, the brain states of different people can be different, so that the electroencephalogram signals are converted into the control signals based on the electroencephalogram band weight information defined by the user by supporting the user to flexibly adjust the electroencephalogram band weight information based on the self condition, and the control signals triggered under the different brain states can be autonomously adjusted by the user. Furthermore, the embodiment also limits the weight of a part of wave bands, so that the use difficulty can be reduced, a user can switch control signals more flexibly, and the user can better experience the process of mind control by utilizing brain waves.
It should be noted that, with regard to the steps included in the electroencephalogram-based embedded device control method provided in any one of the above embodiments, unless explicitly stated otherwise herein, the execution of the steps is not strictly limited in order, and the steps may be executed in other orders. Moreover, at least some of the steps may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
Based on the same inventive concept, the application also provides an embedded device control device based on the electroencephalogram signal. In this embodiment, as shown in fig. 5, the embedded device control apparatus based on electroencephalogram signals includes the following modules:
the electroencephalogram signal receiving module 110 is used for receiving an electroencephalogram signal sent by the brain-computer interface device; the brain-computer interface equipment analyzes and processes the acquired biological signals of the user to obtain the brain-computer signals;
a weight information obtaining module 120, configured to obtain user-defined electroencephalogram band weight information;
a signal conversion module 130, configured to convert the electroencephalogram signal into a control signal according to the electroencephalogram band weight information;
and a control module 140 for controlling the embedded device according to the control signal.
In one embodiment, the signal conversion module 130 is configured to pre-process the electroencephalogram signal, and extract band signals of multiple bands from the pre-processed electroencephalogram signal; and obtaining a control signal according to the plurality of band signals and the weight of each band included in the electroencephalogram band weight information.
In one embodiment of the method of manufacturing the optical fiber, the band signals of the plurality of bands are respectively band signals of bands, band signals of bands a band signal of a band, and a band signal of a band.
In one embodiment, the signal conversion module 130 is further configured to obtain the control signal based on the following formula:
where value denotes the control signal, δ op Representing the weight of the delta band, delta adaptive The adaptive adjustment coefficient represents a delta wave band, and delta represents the signal intensity of a wave band signal of the delta wave band; theta op Weight representing the theta band, theta adaptive The self-adaptive adjustment coefficient represents a theta waveband, and theta represents the signal intensity of a waveband signal of the theta waveband; alpha 1 op Represents the weight of the alpha 1 wave band, and alpha 1 represents the signal intensity of the wave band signal of the alpha 1 wave band; alpha 2 op Represents the weight of the alpha 2 wave band, and alpha 2 represents the signal intensity of the wave band signal of the alpha 2 wave band; beta 1 op Represents the weight of the β 1 band, β 1 represents the signal intensity of the band signal of the β 1 band; beta 2 op Represents the weight of the beta 2 band, and beta 2 represents the signal intensity of the band signal of the beta 2 band; gamma 1 op Represents the weight of the gamma 1 wave band, and gamma 1 represents the signal intensity of the wave band signal of the gamma 1 wave band; gamma 2 op Represents the weight of the gamma 2 wave band, and gamma 2 represents the signal intensity of the wave band signal of the gamma 2 wave band; eegsum represents the accumulation of band signals of each band; adapt _ num represents an adaptive adjustment parameter.
In one embodiment, the apparatus further comprises a visualization module (not shown).
And the visualization module is used for presenting waveforms and/or quantized values of the electroencephalogram signals and the wave band signals of all wave bands in real time in a visualization interface.
In one embodiment, the weight information obtaining module 120 is configured to display an operation interface; detecting weight setting operation performed by a user on an operation interface; and responding to the weight setting operation, and acquiring user-defined electroencephalogram band weight information.
In one embodiment, the control module 140 is configured to send a control signal to the embedded device, where the control signal is used to instruct the embedded device to convert the control signal into a corresponding control command according to a predetermined communication protocol and execute the control command.
For the specific limitation of the embedded device control apparatus based on electroencephalogram signals, reference may be made to the above limitation on the embedded device control method based on electroencephalogram signals, and details are not described here. All or part of each module in the embedded equipment control device based on the electroencephalogram signals can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 6.
The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as electroencephalogram band weight information and formulas defined by a user, and the specific stored data can also refer to the limitations in the above method embodiments. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize an embedded device control method based on electroencephalogram signals.
It will be appreciated by those skilled in the art that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment also provides a computer device, which includes a memory, a processor and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps in the embedded device control method based on electroencephalogram signals provided by any of the above embodiments are implemented.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps in the embedded device control method based on electroencephalogram signals provided in any of the above embodiments.
In an embodiment, an embedded device control system based on electroencephalogram signals is provided, and the system includes a brain-computer interface device, a control device, and an embedded device, where the control device is configured to implement the steps in the embedded device control method based on electroencephalogram signals provided in any of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the embodiments of the methods described above may be implemented by hardware that is related to instructions of a computer program, where the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, the computer program may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus), direct RAM (RDRAM), direct bused dynamic RAM (DRDRAM), and bused dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An embedded device control method based on electroencephalogram signals is characterized by comprising the following steps:
receiving an electroencephalogram signal sent by brain-computer interface equipment; the brain electrical signal is obtained by analyzing and processing the acquired user biological signal by the brain-computer interface equipment;
acquiring user-defined electroencephalogram band weight information;
converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information;
and controlling the embedded equipment according to the control signal.
2. The method of claim 1, wherein said converting the brain electrical signal to a control signal according to the brain electrical band weight information comprises:
preprocessing the electroencephalogram signals, and extracting wave band signals of a plurality of wave bands from the preprocessed electroencephalogram signals;
and obtaining a control signal according to the plurality of band signals and the weight of each band included in the electroencephalogram band weight information.
3. The method of claim 2, wherein obtaining the control signal according to the plurality of band signals and the weight of each band included in the electroencephalogram band weight information comprises:
the control signal is obtained based on the following formula:
where value denotes the control signal, δ op Representing the weight of the delta band, delta adaptive The adaptive adjustment coefficient represents a delta wave band, and delta represents the signal strength of wave band signals of the delta wave band; theta op Weight representing the theta band, theta adaptive The self-adaptive adjustment coefficient represents a theta waveband, and theta represents the signal intensity of a waveband signal of the theta waveband; alpha 1 op Represents the weight of the alpha 1 wave band, and alpha 1 represents the signal intensity of the wave band signal of the alpha 1 wave band; alpha 2 op Represents the weight of the alpha 2 wave band, and alpha 2 represents the signal strength of the wave band signal of the alpha 2 wave band; beta 1 op Represents the weight of the β 1 band, β 1 represents the signal intensity of the band signal of the β 1 band; beta 2 op Represents the weight of the beta 2 band, and beta 2 represents the signal intensity of the band signal of the beta 2 band; gamma 1 op Represents the weight of the gamma 1 wave band, and gamma 1 represents the signal intensity of the wave band signal of the gamma 1 wave band; gamma 2 op Represents the weight of the gamma 2 wave band, and gamma 2 represents the signal intensity of the wave band signal of the gamma 2 wave band; eegsum represents the accumulation of band signals of each of the bands; adapt _ num denotes adaptive adjustment parameterAnd (4) counting.
4. The method of claim 2, wherein the method further comprises:
and presenting the waveforms and/or quantized values of the electroencephalogram signals and the wave band signals of all wave bands in real time in a visual interface.
5. The method of claim 1 or 4, wherein the obtaining of the user-defined electroencephalogram band weight information comprises:
displaying an operation interface;
detecting weight setting operation performed by a user on the operation interface;
and responding to the weight setting operation, and acquiring user-defined electroencephalogram band weight information.
6. The method of claim 1, wherein the controlling an embedded device according to the control signal comprises:
and sending the control signal to the embedded equipment, wherein the control signal is used for indicating the embedded equipment to convert the control signal into a corresponding control command according to a preset communication protocol and executing the control command.
7. An embedded device control apparatus based on electroencephalogram signals, the apparatus comprising:
the brain-computer signal receiving module is used for receiving the brain-computer signals sent by the brain-computer interface equipment; the brain electrical signal is obtained by analyzing and processing the acquired user biological signal by the brain-computer interface equipment;
the weight information acquisition module is used for acquiring user-defined electroencephalogram band weight information;
the signal conversion module is used for converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information;
and the control module is used for controlling the embedded equipment according to the control signal.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. An embedded equipment control system based on electroencephalogram signals is characterized by comprising brain-computer interface equipment, control equipment and embedded equipment;
the brain-computer interface device is used for acquiring a user biological signal, analyzing the user biological signal to obtain an electroencephalogram signal, and sending the electroencephalogram signal to the control device;
the control device is used for receiving the electroencephalogram signals sent by the brain-computer interface device, acquiring user-defined electroencephalogram band weight information, converting the electroencephalogram signals into control signals according to the electroencephalogram band weight information, and controlling the embedded device according to the control signals.
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