CN112971809A - Brain rhythm information detection method and device and electronic equipment - Google Patents
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Abstract
The application discloses a method and a device for detecting brain rhythm information and electronic equipment, wherein the method comprises the following steps: acquiring a first electroencephalogram signal to be detected; performing synchronous compression wavelet transformation on the first electroencephalogram signal to obtain a second electroencephalogram signal; obtaining a target power threshold value for detecting the brain rhythm information according to the second brain electrical signal; and detecting the brain rhythm information in the first brain electrical signal according to the target power threshold. The method can correctly detect the rhythm information in the electroencephalogram signal, and further improve the accuracy of the power spectrum analysis result.
Description
Technical Field
The present disclosure relates to the field of neural engineering technologies, and in particular, to a method and an apparatus for detecting brain rhythm information, and an electronic device.
Background
Electroencephalogram signals, i.e., Electroencephalogram (EEG) signals, can be used to reflect neurophysiological activities on nerve cells on the surface of the cerebral cortex or scalp, contain abundant physiological, psychological and case information, and play an important role in fields such as neuroscience, clinical science, and rehabilitation engineering.
Among electroencephalogram signal analyses, Power Spectrum (PS) analysis is widely used. In the process of implementing the present invention, the inventors found that, when performing power Spectrum analysis, a Fourier Transform (FT) and a Wavelet Transform (WT) are generally used to calculate a Spectrum (Spectrum) at present, however, when performing power Spectrum analysis based on this type of method, different or even quite opposite results are often obtained for the same electroencephalogram signal, and therefore, this type of method may have a problem that it is not possible to correctly detect the brain rhythm information in the electroencephalogram signal.
Disclosure of Invention
It is an object of the embodiments of the present disclosure to provide a new technical solution for detecting brain rhythm information.
According to a first aspect of the present disclosure, there is provided a method of detecting brain rhythm information, the method including:
acquiring a first electroencephalogram signal to be detected;
performing synchronous compression wavelet transformation on the first electroencephalogram signal to obtain a second electroencephalogram signal;
obtaining a target power threshold value for detecting the brain rhythm information according to the second brain electrical signal;
and detecting the brain rhythm information in the first brain electrical signal according to the target power threshold.
Optionally, the obtaining a target power threshold for detecting the brain rhythm information according to the second electroencephalogram signal includes:
acquiring a background spectrum of colored noise in the first electroencephalogram signal;
and obtaining the target power threshold according to the background spectrum.
Optionally, the acquiring a background spectrum of colored noise in the first electroencephalogram signal includes:
and fitting the power spectrum of the second electroencephalogram signal through linear regression under a set logarithmic-logarithmic coordinate to obtain the background spectrum.
Optionally, the obtaining the target power threshold according to the background spectrum includes:
estimating a target chi-square probability distribution function according to the background spectrum;
and selecting a power value corresponding to the probability meeting a preset condition as the target power threshold according to the target chi-square probability distribution function.
Optionally, the acquiring, according to the target power threshold, the brain rhythm information in the first electroencephalogram signal includes:
acquiring a preset time length threshold;
and acquiring the brain rhythm information in the first electroencephalogram signal according to the preset duration threshold and the target power threshold.
Optionally, the detecting the brain rhythm information in the first electroencephalogram signal according to the preset duration threshold and the target power threshold includes:
and selecting a signal with the duration as the preset duration threshold value and the power peak value of each signal period in the duration not less than the target power threshold value from the first electroencephalogram signal as the electroencephalogram rhythm information.
Optionally, the preset time threshold is 3 complete signal cycles
Optionally, the probability of meeting the preset condition includes: a 95% probability in probability distribution data obtained based on the target chi-squared probability distribution function.
According to a second aspect of the present disclosure, the present disclosure also provides a device for detecting brain rhythm information, including:
the first electroencephalogram signal acquisition module is used for acquiring a first electroencephalogram signal to be detected;
the second electroencephalogram signal acquisition module is used for carrying out synchronous compression wavelet transformation on the first electroencephalogram signal to obtain a second electroencephalogram signal;
a power threshold obtaining module, configured to obtain a target power threshold for detecting the information of the brain rhythm according to the second electroencephalogram signal;
and the brain rhythm information detection module is used for detecting the brain rhythm information in the first brain electrical signal according to the target power threshold.
According to a third aspect of the present disclosure, there is also provided an electronic device comprising the apparatus according to the second aspect of the present disclosure; alternatively, it comprises:
a memory for storing executable instructions;
a processor for operating the electronic device to perform the method according to the first aspect of the disclosure, according to the control of the executable instructions.
The method has the beneficial effects that according to the embodiment of the disclosure, for the first electroencephalogram to be detected, the second electroencephalogram with higher accuracy of time-frequency distribution can be obtained by performing synchronous compressed Wavelet Transform (SWT) on the first electroencephalogram; and then, acquiring the brain rhythm information for detecting the brain rhythm information according to the second brain electricity, namely, a target power threshold value reflecting the brain rhythm activity, and detecting the brain rhythm information in the first brain electricity signal according to the target power threshold value. Compared with the existing power spectrum analysis method based on Fourier transform, wavelet transform and the like, the method can correctly detect the rhythm information in the electroencephalogram signal and further improve the accuracy of the power spectrum analysis result.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic block diagram showing a hardware configuration of a server that can be used to implement the method of detecting brain rhythm information of one embodiment.
Fig. 2 is a schematic flow chart of a method for detecting brain rhythm information according to an embodiment of the present disclosure.
FIG. 3 is a schematic diagram of a second brain electrical signal provided by an embodiment of the present disclosure.
Fig. 4 is a schematic diagram of obtaining a background spectrum of colored noise provided by an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of obtaining a target power threshold according to an embodiment of the disclosure.
Fig. 6 is a schematic diagram of a preset time threshold provided by the embodiment of the present disclosure.
Fig. 7 is a schematic diagram of rhythm information obtained by detection provided by the embodiment of the disclosure.
Fig. 8 is a functional block diagram of an apparatus for detecting brain rhythm information according to an embodiment of the present disclosure.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
Fig. 1 is a block diagram of a hardware configuration of a server that can be used to implement the method of detecting brain rhythm information according to one embodiment.
As shown in fig. 1, the server 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, and an input device 1600. The processor 1100 may be, for example, a central processing unit CPU or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1130 includes, for example, a USB interface, a serial interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display panel. The input device 1600 may include, for example, a touch screen, a keyboard, and the like.
In this embodiment, the server 1000 may be used to participate in implementing a method according to any embodiment of the present disclosure.
As applied to any embodiment of the present disclosure, the memory 1200 of the server 1000 is configured to store instructions for controlling the processor 1100 to operate in support of implementing a method according to any embodiment of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
It should be understood by those skilled in the art that although a plurality of devices of the server 1000 are shown in fig. 1, the server 1000 of the disclosed embodiments may refer to only some of the devices therein, for example, only the processor 1110 and the memory 1120. This is well known in the art and will not be described in further detail herein.
< method examples >
Brain rhythm information, i.e., information reflecting the rhythmic activity of the cranial nerves. However, the presence of a spectral peak at a given frequency does not necessarily imply potential oscillatory activity at that frequency, since non-oscillatory, large amplitude artifacts and transient signals can produce power variations at that frequency.
In the process of implementing the application, in order to correctly detect the brain rhythm information in the electroencephalogram signal, the inventor finds that the power value of each frequency can be solved by utilizing wavelet transformation; and calculating a power threshold value through the power value obtained by solving, so as to detect the brain rhythm information in the electroencephalogram signal to a certain extent according to the power threshold value.
In specific implementation, compared with the prior art, although the method can already detect and obtain the brain rhythm information in the electroencephalogram signal, the inventor finds that, because the Wavelet transform adopts the transformed Wavelet Mother waves, i.e. Mother wavelets (MW, motherwavelet) as the time-frequency atoms thereof, each time-frequency atom is related to some time-frequency expansion, which may affect the definition of electroencephalogram signal analysis, and further, when the method is used for detecting the brain rhythm information in the electroencephalogram signal, the frequency range of the detected and obtained brain rhythm information reflecting rhythmic activity may be larger than the frequency range of the actual brain rhythmic activity.
In order to correctly detect the brain rhythm information in the electroencephalogram signal, an embodiment of the present disclosure provides a method for detecting brain rhythm information, please refer to fig. 2, which is a schematic flow diagram of the method for detecting brain rhythm information provided by the embodiment of the present disclosure, and the method may be implemented by a server, for example, the server 1000 in fig. 1; of course, the method may also be implemented by other electronic devices, for example, a terminal device, and is not limited herein.
Referring to fig. 2, the method of the present embodiment may include the following steps S2100-S2400, which are described in detail below.
Step S2100, a first electroencephalogram signal to be detected is obtained.
In specific implementation, the first electroencephalogram signal to be detected may be single-channel data or multi-channel data, and the embodiment does not specially limit the electroencephalogram signal to be detected.
Step S2200, performing synchronous compression wavelet transform on the first electroencephalogram signal to obtain a second electroencephalogram signal.
Specifically, in order to correctly detect the brain rhythm information in the electroencephalogram signal, in this embodiment, the expansion effect of the mother wavelet is compensated by performing synchronous compression wavelet transform on the first electroencephalogram signal to be detected, so as to solve the problem that the frequency range of the brain rhythm information of the detected brain rhythm information is larger than the frequency range of the actual brain rhythmic activity, wherein the synchronous compression wavelet transform is self-adaptive and reversible time-frequency distribution, and can compress the frequency spectrum along the frequency axis by reallocating the frequency, so as to compensate the expansion effect of the mother wavelet.
Please refer to fig. 3, which is a schematic diagram of a second electroencephalogram signal provided by an embodiment of the present disclosure. Specifically, the synchronous compression wavelet transform performed on the first electroencephalogram signal may be: firstly, performing Continuous Wavelet Transform (CWT) on a first electroencephalogram signal, wherein in order to obtain instantaneous frequency information, when performing Continuous Wavelet Transform on the first electroencephalogram signal, analytic Morlet Wavelet can be used for performing Continuous Wavelet Transform on the first electroencephalogram signal so as to obtain a first Transform result; then, extracting an instantaneous frequency from the first conversion result by using phase conversion; then, by compressing the continuous wavelet transform to a region where the phase transform is constant, the second electroencephalogram signal as shown in fig. 3 is obtained.
It should be noted that, since there is a detailed description of how to perform synchronous compression wavelet transform in the prior art, only a brief description is made in this embodiment, and detailed processing procedures thereof are not described herein again.
And step S2300, obtaining a target power threshold value for detecting the brain rhythm information according to the second brain electrical signal.
In the present embodiment, the target power threshold is a threshold that reflects an upper power limit or an upper amplitude limit of the actual brain rhythm activity.
Specifically, after the second electroencephalogram signal with a frequency not being excessively expanded is obtained through step S2200, the present embodiment obtains a target power threshold capable of correctly detecting the brain rhythm information in the electroencephalogram signal by analyzing the second electroencephalogram signal.
In a specific implementation, the obtaining a target power threshold for detecting the information of the brain rhythm according to the second electroencephalogram signal includes: acquiring a background spectrum of colored noise in the first electroencephalogram signal; and obtaining the target power threshold according to the background spectrum.
Wherein, Colored Noise (CN), also called color Noise, is Noise with uneven power spectral density function.
In this embodiment, the background spectrum of the electroencephalogram signal is set, that is, the background spectrum is colored noise, so when the target power threshold is solved, the background spectrum of the colored noise can be solved first, and then the target power threshold is obtained according to the background spectrum.
Please refer to fig. 4, which is a schematic diagram of obtaining a background spectrum of colored noise according to an embodiment of the disclosure. As shown in fig. 4, in the present embodiment, after the synchronous compressed wavelet transform is performed on the first brain electrical signal to obtain the second brain electrical signal, the background spectrum of the colored noise can be estimated by fitting the power spectrum of the synchronous compressed wavelet through linear regression.
After the above steps are performed to obtain the background spectrum of the colored noise in the first electroencephalogram signal, a target power threshold for detecting the information of the rhythm of the brain can be obtained according to the background spectrum, and in a specific implementation, obtaining the target power threshold according to the background spectrum includes: estimating a target chi-square probability distribution function according to the background spectrum; and selecting a power value corresponding to the probability meeting a preset condition as the target power threshold according to the target chi-square probability distribution function.
Chi-squared Distribution (Chi-squared Distribution) specifically means that if n mutually independent random variables xi, ξ n are all in accordance with standard normal Distribution (also called independent and equally distributed in standard normal Distribution), the sum of squares of the n random variables in accordance with standard normal Distribution forms a new random variable, and the Distribution rule is called Chi-squared Distribution.
Please refer to fig. 5, which is a schematic diagram of obtaining a target power threshold according to an embodiment of the disclosure. That is, in this embodiment, after the background spectrum of the colored noise in the electroencephalogram signal is obtained by fitting, according to the background spectrum, the average value of the target chi-square probability distribution function corresponding to the background spectrum can be estimated by using the background at a certain point frequency to obtain the target chi-square probability distribution function of the chi-square distribution corresponding to the background spectrum, and the power value corresponding to the probability that satisfies the preset condition is selected from the theoretical probability distribution data as the target power threshold.
As shown in fig. 5, in one embodiment, the probability of meeting the preset condition may be: the 95% probability in the probability distribution data obtained based on the target chi-square probability distribution function, i.e., the probability at the 95 th percentile.
It should be noted that, in specific implementation, the probability of the preset condition may be set as needed; alternatively, other methods may be used to obtain the target power threshold, and are not limited herein.
And step S2400, detecting the brain rhythm information in the first electroencephalogram signal according to the target power threshold.
In one embodiment, the detecting the brain rhythm information in the first brain electrical signal according to the target power threshold includes: acquiring a preset time length threshold; and acquiring the brain rhythm information in the first electroencephalogram signal according to the preset duration threshold and the target power threshold.
Specifically, in practice, the signal period corresponding to the brain rhythm activity usually lasts for a certain time, so that when detecting the brain rhythm information in the first electroencephalogram signal, after obtaining the target power threshold, based on the preset time threshold, a signal with the duration satisfying the preset time threshold is screened from the first electroencephalogram signal as the brain rhythm information in the first electroencephalogram signal.
In specific implementation, through repeated experiments, the inventor found that, when the preset duration threshold is set to 3 complete signal cycles, the accuracy of the finally detected information on the rhythm of the brain is relatively high, and therefore, in this embodiment, as shown in fig. 6, the preset duration threshold is set to 3 complete signal cycles.
In specific implementation, the detecting the brain rhythm information in the first electroencephalogram signal according to the preset duration threshold and the target power threshold includes: and selecting a signal with the duration as the preset duration threshold value and the power peak value of each signal period in the duration not less than the target power threshold value from the first electroencephalogram signal as the electroencephalogram rhythm information.
Please refer to fig. 7, which is a schematic diagram of the brain rhythm information provided by the embodiment of the disclosure. As shown in fig. 7, in this embodiment, a signal that simultaneously satisfies the preset time threshold and the target power threshold in the first electroencephalogram signal is taken as the electroencephalogram rhythm information in the first electroencephalogram signal.
In summary, the method for detecting brain rhythm information provided in this embodiment can obtain a second electroencephalogram signal with higher accuracy of time-frequency distribution by performing synchronous compressed wavelet transform on a first electroencephalogram signal to be detected; and then, acquiring the brain rhythm information for detecting the brain rhythm information according to the second brain electricity, namely, a target power threshold value reflecting the brain rhythm activity, and detecting the brain rhythm information in the first brain electricity signal according to the target power threshold value. Compared with the existing power spectrum analysis method based on Fourier transform, wavelet transform and the like, the method can correctly detect the rhythm information in the electroencephalogram signal and further improve the accuracy of the power spectrum analysis result.
< apparatus embodiment >
Corresponding to the above method embodiment, in this embodiment, a device for detecting brain rhythm information is further provided, as shown in fig. 8, the device 8000 may include a first brain electrical signal obtaining module 8100, a second brain electrical signal obtaining module 8200, a power threshold obtaining module 8300, and a brain rhythm information detecting module 8400.
The first electroencephalogram signal acquisition module 8100 is used for acquiring a first electroencephalogram signal to be detected.
The second electroencephalogram signal acquisition module 8200 is used for performing synchronous compressed wavelet transformation on the first electroencephalogram signal to obtain a second electroencephalogram signal.
The power threshold obtaining module 8300 is configured to obtain a target power threshold for detecting the information of the brain rhythm according to the second electroencephalogram signal.
In one embodiment, the power threshold obtaining module 8300, when obtaining the target power threshold for detecting the brain rhythm information according to the second brain electrical signal, may be configured to: acquiring a background spectrum of colored noise in the first electroencephalogram signal; and obtaining the target power threshold according to the background spectrum.
In this embodiment, the power threshold obtaining module 8300, in obtaining the background spectrum of the colored noise in the first brain electrical signal, may be configured to: and fitting the power spectrum of the second electroencephalogram signal through linear regression under a set logarithmic-logarithmic coordinate to obtain the background spectrum.
In this embodiment, the power threshold obtaining module 8300, when obtaining the target power threshold according to the background spectrum, may be configured to: estimating a target chi-square probability distribution function according to the background spectrum; and selecting a power value corresponding to the probability meeting a preset condition as the target power threshold according to the target chi-square probability distribution function.
The brain rhythm information detection module 8400 is configured to detect brain rhythm information in the first electroencephalogram signal according to the target power threshold.
In one embodiment, the module 8400 for detecting the brain rhythm information may be configured to, when acquiring the brain rhythm information in the first electroencephalogram signal according to the target power threshold: acquiring a preset time length threshold; and acquiring the brain rhythm information in the first electroencephalogram signal according to the preset duration threshold and the target power threshold.
In this embodiment, when the module 8400 detects the brain rhythm information in the first electroencephalogram signal according to the preset time threshold and the target power threshold, it may be configured to: and selecting a signal with the duration as the preset duration threshold value and the power peak value of each signal period in the duration not less than the target power threshold value from the first electroencephalogram signal as the electroencephalogram rhythm information.
< apparatus embodiment >
Corresponding to the above method embodiments and apparatus embodiments, in this embodiment, an electronic device is further provided, which may include the apparatus 8000 for detecting the cardioversion information according to any embodiment of the present disclosure, and is configured to implement the method for detecting the cardioversion information according to any embodiment of the present disclosure.
As shown in fig. 9, the electronic device 9000 can further comprise a processor 9200 and a memory 9100, the memory 9100 being configured to store executable instructions; the processor 9200 is configured to operate the electronic device according to the control of the instruction to perform the method for detecting the brain rhythm information according to any embodiment of the present disclosure.
The above modules of the apparatus 8000 may be implemented by the processor 9200 executing the instructions to execute the method for detecting the brain rhythm information according to any embodiment of the present disclosure.
The electronic device 9000 may be a server, or may be another type of device, such as a terminal device, and the like, but is not limited thereto, and for example, the electronic device 9000 may be the server 1000 in fig. 1, and the like.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114020148A (en) * | 2021-10-12 | 2022-02-08 | 北京师范大学 | Attention-improving training method and device and electronic equipment |
| CN114169366A (en) * | 2021-11-19 | 2022-03-11 | 北京师范大学 | Neurofeedback training system and method |
| CN115105095A (en) * | 2022-08-29 | 2022-09-27 | 成都体育学院 | A method, system and device for motion intent recognition based on EEG signals |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010045724A1 (en) * | 2008-10-20 | 2010-04-29 | Neurochip Corporation | Method and rhythm extractor for detecting and isolating rhythmic signal features from an input signal using the wavelet packet transform |
| WO2013043157A2 (en) * | 2011-09-20 | 2013-03-28 | Vivaquant Llc | Physiological signal denoising |
| CN107729861A (en) * | 2017-10-30 | 2018-02-23 | 太原理工大学 | J wave detecting methods based on random forest |
-
2021
- 2021-02-09 CN CN202110176124.5A patent/CN112971809A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010045724A1 (en) * | 2008-10-20 | 2010-04-29 | Neurochip Corporation | Method and rhythm extractor for detecting and isolating rhythmic signal features from an input signal using the wavelet packet transform |
| WO2013043157A2 (en) * | 2011-09-20 | 2013-03-28 | Vivaquant Llc | Physiological signal denoising |
| CN107729861A (en) * | 2017-10-30 | 2018-02-23 | 太原理工大学 | J wave detecting methods based on random forest |
Non-Patent Citations (2)
| Title |
|---|
| TAKAFUMI KINOSHITA等: "Sleep Spindle Detection using RUSBoost and Synchrosqueezed Wavelet Transform", 《《IEEE TRANSACTIONS ON NEURAL SYSTEMS & REHABILITATION ENGINEERING》 * |
| TARA A. WHITTE: "A better oscillation detection method robustly extracts EEG rhythms across brain state changes: The human alpha rhythm as a test case", 《NEUROIMAGE》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114020148A (en) * | 2021-10-12 | 2022-02-08 | 北京师范大学 | Attention-improving training method and device and electronic equipment |
| CN114020148B (en) * | 2021-10-12 | 2023-06-16 | 北京师范大学 | Training method and device for improving attention and electronic equipment |
| CN114169366A (en) * | 2021-11-19 | 2022-03-11 | 北京师范大学 | Neurofeedback training system and method |
| CN114169366B (en) * | 2021-11-19 | 2023-10-20 | 北京师范大学 | Neural feedback training system and method |
| CN115105095A (en) * | 2022-08-29 | 2022-09-27 | 成都体育学院 | A method, system and device for motion intent recognition based on EEG signals |
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