CN108903937B - Mental parameter acquisition method, device and system - Google Patents
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Abstract
The present disclosure relates to a mental parameter acquisition method, apparatus and system, the method comprising: receiving brain wave signals of a first frequency band; acquiring the energy density of each frequency of a plurality of sub-frequency bands in the first frequency band brain wave signal; for each of the plurality of sub-bands, calculating a rhythmic energy of the each sub-band from the energy density of the respective frequency in the each sub-band; calculating the mental parameter according to the rhythm energies of the plurality of sub-frequency bands. Through the implementation of the method, the mental parameters of the user can be quickly and conveniently obtained.
Description
Technical Field
The present disclosure relates to the field of terminal devices, and in particular, to a method, an apparatus, and a system for acquiring mental parameters.
Background
Brain Force (Nerve Force) generally refers to the ability and energy of memory, thinking, emotion, spirit, etc. mainly produced by the brain, including many aspects of intelligence, psychology, experience, knowledge, skill, etc. The abnormal brain function can refer to weak brain function, and usually shows symptoms of low mood, insomnia, low thinking power, inattention, memory decline and the like.
The mental parameters are generally used for measuring mental power, so that the acquisition of the mental parameters has great significance.
Disclosure of Invention
In view of this, the present disclosure provides a mental parameter obtaining method, device and system to obtain mental parameters of a user.
According to an aspect of the present disclosure, there is provided an mental parameter acquisition method, the method including:
receiving brain wave signals of a first frequency band;
acquiring the energy density of each frequency of a plurality of sub-frequency bands in the first frequency band brain wave signal;
for each of the plurality of sub-bands, calculating a rhythmic energy of the each sub-band from the energy density of the respective frequency in the each sub-band; and
calculating the mental parameter according to the rhythm energies of the plurality of sub-frequency bands.
In one possible embodiment, acquiring the energy density of each frequency of a plurality of sub-bands in the first-band brain wave signal includes:
performing discrete Fourier transform on the first frequency band brain wave signal to obtain a converted brain wave signal;
calculating a plurality of initial energy densities for each of the respective frequencies according to a first formula:
wherein X represents the first frequency band brain wave signal, the length of the signal is N, X [ N ] represents the value of the signal at N points, the value range of N is 1-N, X [ k ] represents each initial energy density of each frequency, and k is the frequency of the first frequency band brain wave signal;
obtaining a stable value of each initial energy density according to a stable formula, wherein the stable formula is as follows:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of said initial energy densities; and
calculating an average value of the plurality of initial energy densities according to a moving average formula, and taking the average value as the energy density of each frequency, wherein the moving average formula is as follows:
In one possible embodiment, for each of the plurality of sub-bands, calculating the rhythm energy of each sub-band according to the energy density of each frequency in the each sub-band comprises:
for each sub-frequency band, calculating rhythm energy corresponding to each sub-frequency band according to a second formula, wherein the second formula is as follows:
wherein Q (x) is the rhythm energy corresponding to each sub-frequency band, k1Is the lower frequency limit, k, of each sub-band2For said upper frequency limit of each sub-band, P (k) for said eachThe energy density for each frequency in a sub-band.
In one possible embodiment, the method further comprises:
storing the first frequency band brain wave signals, the energy densities of the plurality of sub-bands, the rhythm energies of the plurality of sub-bands and the mental parameters generated in the execution of the method;
displaying the first frequency band brain wave signals, the energy densities of the plurality of sub-bands, the rhythm energies of the plurality of sub-bands and the mental parameters generated in the execution of the method.
According to another aspect of the present disclosure, the present disclosure provides an mental parameter acquisition apparatus, including:
the receiving module is used for receiving brain wave signals of a first frequency range;
the acquisition module is used for acquiring the energy density of each frequency of a plurality of sub-frequency bands in the first frequency band brain wave signal;
and the calculation module is used for calculating the rhythm energy of each sub-frequency band according to the energy density of each frequency in each sub-frequency band and calculating the mental parameter according to the rhythm energy of the sub-frequency bands.
In one possible implementation, the obtaining module includes:
the transformation submodule is used for carrying out discrete Fourier transformation on the first frequency band brain wave signal to obtain a transformed brain wave signal;
a determination sub-module for calculating, for the converted brain wave signals, a plurality of initial energy densities for each of the respective frequencies according to a first formula:
wherein X represents the first frequency band brain wave signal, the length of the signal is N, X [ N ] represents the value of the signal at N points, the value range of N is 1-N, X [ k ] represents each initial energy density of each frequency, and k is the frequency of the first frequency band brain wave signal;
the stationary submodule is used for obtaining a stationary value of each initial energy density according to a stationary formula, wherein the stationary formula is as follows:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of said initial energy densities; and
a moving average submodule, configured to calculate an average value of the plurality of initial energy densities according to a moving average formula, where the average value is used as the energy density of each frequency, and the moving average formula is:
In one possible implementation, the calculation module includes:
a calculating submodule, configured to, for each sub-band, obtain rhythm energy corresponding to each sub-band according to a second formula,
the second formula is:
In a possible embodiment, the apparatus further comprises:
the storage module is used for storing the first frequency band brain wave signals, the energy densities of the multiple sub-frequency bands, the rhythm energies of the multiple sub-frequency bands and the mental parameters generated in each module;
and the display module is used for displaying the first frequency band brain wave signals, the energy densities of the multiple sub-frequency bands, the rhythm energies of the multiple sub-frequency bands and the mental parameters generated in each module.
According to another aspect of the present disclosure, the present disclosure provides an mental parameter acquisition system, the system comprising: a signal acquisition device, a filtering device and a terminal,
the signal acquisition equipment is used for acquiring brain wave signals of a user and sending the brain wave signals to the filtering equipment;
the filtering equipment processes the brain wave signals to obtain first frequency range brain wave signals and sends the first frequency range brain wave signals to a terminal;
the terminal is configured to perform the method of any of claims 1-4.
In one possible embodiment, the filtering device performs butterworth band-pass filtering on the brain wave signals to obtain the first frequency band brain wave signals, the frequency band of which includes 0.5-40 Hz.
According to the method, the device or the system for acquiring the mental parameters, the rhythm energy of each sub-frequency band is calculated according to the energy density of each frequency of each sub-frequency band aiming at each sub-frequency band in the first frequency band by acquiring the energy density of each frequency of each sub-frequency band in the brain wave signals of the first frequency band, and finally, the mental parameters are calculated according to the rhythm energy of each sub-frequency band. Through the implementation of the method, the device or the system, the mental parameters of the user can be quickly and conveniently obtained.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flow chart of a method of brain parameter acquisition according to an embodiment of the present disclosure.
Fig. 2 shows a flow chart of a method of obtaining energy density according to an embodiment of the present disclosure.
Fig. 3a is a schematic waveform diagram illustrating first-frequency-band electroencephalogram signals in an embodiment of the present disclosure, and fig. 3b is a schematic frequency-energy density diagram illustrating the first-frequency-band electroencephalogram signals in an embodiment of the present disclosure.
FIG. 4 is a schematic diagram illustrating a position relationship between a signal acquisition point of a signal acquisition device and a brain according to an embodiment of the present disclosure
Fig. 5 shows a flow chart of a method of brain parameter acquisition according to yet another embodiment of the present disclosure.
Fig. 6 shows a block diagram of an apparatus for brain parameter acquisition according to an embodiment of the present disclosure.
Fig. 7 shows a block diagram of an apparatus for brain parameter acquisition according to yet another embodiment of the present disclosure.
Fig. 8 shows a block diagram of an apparatus for brain parameter acquisition according to yet another embodiment of the present disclosure.
Fig. 9 shows a schematic diagram of a mental parameter acquisition system according to an embodiment of the present disclosure.
Fig. 10 shows a block diagram of an apparatus for mental parameter acquisition according to an embodiment of the present disclosure.
Fig. 11 shows a block diagram of an apparatus for mental parameter acquisition according to yet another embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Referring to fig. 1, fig. 1 is a flowchart illustrating a mental parameter obtaining method according to an embodiment of the present disclosure.
As shown in fig. 1, the exemplary embodiment may be applied to a terminal device or a server, where the terminal device may be a terminal device such as a smart phone, a tablet computer, or a wearable device, and the present embodiment is not limited thereto. The mental parameter acquisition method comprises the following steps:
in step S110, a first frequency band electroencephalogram signal is received.
Electroencephalography (EEG) is a method of recording brain activity using electrophysiological indices, in which the postsynaptic potentials generated in synchronization with a large number of neurons sum up 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 first frequency band brain wave signal may be obtained by acquiring a brain wave signal by signal acquisition equipment and filtering the brain wave signal by filtering equipment.
In one possible embodiment, the signal collecting apparatus for collecting the brain wave signals may include a dry electrode, for example, wearable glasses, which may be integrated with a plurality of miniaturized dry electrodes. The dry electrode can carry out harmless real-time monitoring on electroencephalogram signals of a wearer. Of course, the signal collecting device for collecting the brain wave signals may be other devices (for example, a wearable helmet, on which a plurality of miniaturized dry electrodes may be included), and the embodiment is not limited as long as the device can collect the brain wave signals of the user.
In one possible embodiment, the first frequency band brain wave signals may be 0.5-40Hz brain wave signals. The filtering device may be a butterworth band-pass filter, and the butterworth band-pass filter may be used to perform band-pass filtering on the brain wave signals to obtain the brain wave signals of the first frequency band. A butterworth filter is one type of electronic filter. The butterworth filter is characterized by a frequency response curve that is smoothest, maximally flat, with no fluctuations in the passband, and gradually drops to zero in the stopband. In other embodiments, the first-frequency-band brain wave signals may be acquired in other manners, for example, using other types of band-pass filters, which is not limited in this disclosure.
Step S120, obtaining energy density of each frequency of a plurality of sub-bands in the first frequency band electroencephalogram signal.
Brain waves are spontaneous rhythmic neuroelectrical activity with frequencies ranging from 1-30 times per second, and are divided into four bands, i.e., δ (1-4 Hz), θ (4-7 Hz), α (8-12 Hz), β (12-32 Hz). In addition, when the person is awake and focuses on a certain event, a gamma wave with a frequency higher than that of a beta wave is often seen, the frequency is 32-80 Hz, and the amplitude range is indefinite; while other normal brain waves with special waveforms, such as hump wave, sigma wave, lambda wave, kappa-complex wave, mu wave, etc., can appear during sleep.
Referring to Table 1, α -waves can be further classified into Lower Alpha (8-10Hz) and Higher Alpha (10-12Hz), and β -waves can be further classified into SMR (12-15Hz), β 1(16-21Hz) and β 2(20-32 Hz).
| EEG (brain wave) | Frequency HZ |
| Delta(δ) | 1-4 |
| Theta(θ) | 4-7 |
| Alpha(α) | 8-12 |
| Lower Alpha | 8-10 |
| Higher Alpha | 10-12 |
| SMR(low Beta) | 12-15 |
| Beta1(β1) | 16-21 |
| High Beta(β2) | 20-32 |
TABLE 1
In one possible embodiment, the plurality of frequency bands may include δ (1-4 Hz), θ (4-7 Hz), α (8-12 Hz), β (12-32 Hz), and Lower Alpha (8-10Hz), Higher Alpha (10-12Hz), SMR (12-15Hz), β 1(16-21Hz), and β 2(20-32Hz), but of course, the plurality of frequency bands may include brain wave signals of different frequency bands from the above frequency bands in the first brain wave signals according to different segmentation manners.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for obtaining energy density according to an embodiment of the disclosure.
As shown in fig. 2, step S120 may include:
step S1201: and performing discrete Fourier transform on the first frequency range brain wave signal to obtain a converted brain wave signal.
In one possible implementation, the data length for the discrete fourier transform may be N, which may be a number closest to a data length of 500 milliseconds to an integer power of 2. For example, when the sampling rate is 220Hz, N may be 128 (about 581 milliseconds of data). Two discrete fourier transforms may be performed, which may be 100 milliseconds apart.
Step S1202: calculating a plurality of initial energy densities for each of the respective frequencies according to a first formula for the converted brain wave signals, the first formula being:
wherein X represents the first frequency band electroencephalogram signal, the length of the signal is N, X [ N ] represents the value of the signal at N points, the value range of N is 1-N, X [ k ] represents each initial energy density of each frequency, and k is the frequency of the first frequency band electroencephalogram signal.
For example, when x is a sine wave with a length N of 100 and a frequency of 10Hz, the initial energy density of the signal with a frequency of 10Hz can be calculated according to the following formula:
as can be seen from the above formula, the initial energy density X10 at a frequency of 10Hz is 0.5.
Referring to fig. 3a and 3b together, fig. 3a illustrates a waveform diagram of the first frequency band electroencephalogram signal in an embodiment of the present disclosure, and fig. 3b illustrates a frequency-energy density diagram corresponding to the first frequency band electroencephalogram signal in an embodiment of the present disclosure.
As can be seen from fig. 3b, the initial energy density is 0.5 at a frequency of 10 Hz.
In one possible embodiment, the initial energy density of each frequency may be taken as the energy density of each frequency.
In one possible embodiment, the initial energy density of each frequency may be smoothed or moving averaged, and the average obtained after the smoothing is taken as the energy density of each frequency.
In a possible embodiment, the method for obtaining the energy density may further include:
step S1203, obtaining a stationary value of X [ k ] according to a stationary formula, wherein the stationary formula is as follows:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of the initial energy densities.
Step S1204, calculating an average value of the plurality of initial energy densities according to a moving average formula, and taking the average value as the energy density of each frequency, where the moving average formula is:
For example, m initial energy densities of a certain frequency may be obtained by a first formula (the initial energy densities are obtained m times by the first formula), a stationary value is obtained by a stationary formula for each of the m initial energy densities, a moving average of the m initial energy densities is obtained by performing a moving average on each stationary value by a moving average formula, and the moving average is used as the energy density of the frequency, where m may be, for example, 6.
By the method, the time for acquiring the electroencephalogram signals can be greatly shortened (to millisecond level), and the accuracy of the acquired energy density is improved.
Step S130, calculating, for each of the multiple sub-bands, a rhythm energy of each of the sub-bands according to an energy density of each frequency of each of the sub-bands.
In a possible implementation manner, for each sub-band, the rhythm energy corresponding to each sub-band may be calculated according to a second formula, where the second formula is:
For example, θ (4-7 Hz) may be used as a sub-band, in which case k is1Can be 4Hz, k2The frequency density corresponding to each frequency in the theta (4-7 Hz) frequency band can be 7Hz, and X (k) is the frequency density corresponding to each frequency in the theta (4-7 Hz) frequency band, the frequency density corresponding to each frequency in the theta (4-7 Hz) frequency band can be obtained according to the first formula, and the rhythm energy Q (x) of the theta (4-7 Hz) frequency band can be obtained according to the second formula.
Step S140, calculating the mental parameter according to the rhythm energies of the plurality of sub-bands.
In one possible embodiment, the mental parameters may include brain depression (RATIOh/i), brain concentration (RATIOat), brain control (RATIOat-h/i), brain effort (RATIOan/in), brain mood (RATIOva), brain relaxation (RATIOe-mn).
The smaller the value of the brain inhibitory ability, the stronger the inhibition of impulsive hyperactivity;
the smaller the value of the brain concentration and the brain control force is, the stronger the concentration is;
for the brain efforts, smaller numbers indicate better mood;
the brain emotional power is larger, and the larger the numerical value is, the more positive the emotion is;
the brain relaxation force, a smaller value indicates more relaxation.
In a possible embodiment, the mental parameter may be defined as the RATIO of the energies of different wave-band rhythms, such as the cerebral inhibitory power (RATIOh/i), the cerebral specific attention power (RATIO)at) Brain control ability (RATIO)at-h/i) Brain effort (RATIO)an/in) Brain Relaxivity (RATIO)re-mn) May be defined as:
RATIOh/i＝Theta/SMR(12-13Hz)，RATIOat＝Theta/β1(16-21Hz)，RATIOat-h/i＝Theta/(SMR+β1,13-21Hz)，RATIOan/in＝High Beta(20-32Hz)/Alpha，RATIOre-mn＝Theta/Alpha。
referring to fig. 4, fig. 4 is a schematic diagram illustrating a position relationship between a signal acquisition point of a signal acquisition device and a brain according to an embodiment of the present disclosure.
In one possible embodiment, the signal acquisition device may be a wearable helmet, which may include a plurality of signal acquisition points (e.g., F3, F4, AF7, AF8, etc.). As shown in fig. 4, the signal acquisition site is used for acquiring brain wave signals of a user, and the signal acquisition site may include several integrated miniaturized dry electrodes.
The brain emotional force (RATIO)va) Can be defined as: RATIOva(Alpha @ (F4 or AF8) -Alpha @ (F3 or AF7))/(Alpha @ (F4 or AF8) + Alpha @ (F3 or AF7)), wherein,
alpha @ (F4 or AF8) may represent: alpha rhythm energy calculated through brain wave signals collected by signal collection points F4 or AF 8;
alpha @ (F3 or AF7) may represent: and Alpha rhythm energy calculated through brain wave signals collected by the signal collection points F3 or AF 7.
Thus, the mental parameter may be calculated using the rhythmic energies of the plurality of sub-bands as defined above for the mental parameter.
It should be understood that the execution of the above flowchart is not limited to the above description, the execution order of the steps may be changed, and the actions executed by the respective steps may be increased or decreased, as long as the mental parameter acquisition can be realized through the cooperation of the respective steps.
According to the above mental parameter acquisition method, the present disclosure calculates, for each of a plurality of sub-bands, rhythm energy of each sub-band according to an energy density of each frequency of each sub-band by acquiring the energy density of each frequency of the plurality of sub-bands in a first frequency band electroencephalogram signal, and finally calculates the mental parameter according to the rhythm energy of the plurality of sub-bands. Through the implementation of the method, the mental parameters of the user can be quickly and conveniently obtained.
Referring to fig. 5, fig. 5 is a flowchart illustrating a mental parameter obtaining method according to an embodiment of the present disclosure.
As shown in fig. 5, the method for acquiring mental parameters may further include the steps of:
step S210, storing the first frequency band electroencephalogram signals, the energy density of each frequency of the plurality of sub-bands, the rhythm energy of the plurality of sub-bands, and the mental parameters generated during the execution of the method.
In one possible embodiment, the signal acquisition device may also directly (e.g., by wire or wirelessly, etc.) transmit the acquired brain wave signals to the terminal, and the terminal may store the brain wave signals after acquiring the brain wave signals; after acquiring the first frequency band brain wave signal, the terminal may store the first frequency band brain wave signal; after acquiring the energy density of each frequency of the multiple sub-bands, the terminal can store the energy density of each frequency of the multiple sub-bands; after acquiring the rhythm energy of the sub-frequency bands, the terminal can store the rhythm energy; and after the terminal acquires the mental parameters, the terminal can store the mental parameters.
In one possible implementation, the storage means may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
Step S220, displaying the first frequency band electroencephalogram signals, the energy density of each frequency of the plurality of sub-bands, the rhythm energy of the plurality of sub-bands, and the mental parameters generated during the execution of the method.
In one possible embodiment, the signal acquisition device may also directly (e.g., by wire or wirelessly, etc.) transmit the acquired brain wave signals to the terminal, and the terminal may display the brain wave signals after acquiring the brain wave signals; after acquiring the first frequency band brain wave signal, the terminal may display the first frequency band brain wave signal; after the energy density of each frequency of the multiple sub-bands is obtained, the terminal can display the energy density of each frequency of the multiple sub-bands; after acquiring the rhythm energy of the plurality of sub-frequency bands, the terminal can display the rhythm energy; after the mental parameters are acquired, the mental parameters can be stored.
In one possible embodiment, the terminal may display the data through a Liquid Crystal Display (LCD) or other device or instrument for implementing the display function.
It should be understood that the execution of the above flowchart is not limited to the above description, the execution order of the steps may be changed, and the actions executed by the respective steps may be increased or decreased, as long as the storage and display of data can be realized through the cooperation of the respective steps.
Through the implementation of the method, the brain wave signals generated in each step, the first frequency band brain wave signals, the energy densities of the multiple sub-frequency bands, the rhythm energies of the multiple sub-frequency bands and the mental parameters can be stored and displayed, and the calling and observation of the data are facilitated.
Referring to fig. 6, fig. 6 is a block diagram of a mental parameter obtaining apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the mental parameter acquisition device may include:
the receiving module 200, the obtaining module 210 and the calculating module 220, wherein the receiving module 200 is electrically connected to the obtaining module 210, and the obtaining module 210 is electrically connected to the calculating module 220.
The receiving module 200 receives the first frequency band brain wave signal.
In one possible embodiment, the first frequency band may be obtained by acquiring brain wave signals by a signal acquisition device and filtering the brain wave signals by a filtering device. The first frequency band brain wave signal may be a 0.5-40Hz brain wave signal.
The obtaining module 210, electrically connected to the receiving module 200, may be configured to obtain energy densities of multiple sub-bands of each frequency in the first frequency band brain wave signal.
The calculating module 220 calculates, for each sub-band of the plurality of sub-bands, the rhythm energy of each sub-band according to the energy density of each frequency of each sub-band, and calculates the mental parameter according to the rhythm energy of the plurality of sub-bands.
In one possible embodiment, the computing module 220 may be a device or an apparatus with computing and Processing functions, for example, a device or an apparatus including a Central Processing Unit (CPU), a Microcontroller (MCU), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), and the like.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
It should be noted that, although the present disclosure is described by taking a block diagram of a mental parameter acquisition apparatus as an example, those skilled in the art will understand that the present disclosure should not be limited thereto. In fact, the user can flexibly set each module according to personal preference and/or actual application scene, and the modules in the module diagram can increase, decrease and change the execution steps of each module as long as the coordination among the modules can complete the acquisition of mental parameters.
Through the cooperation of the modules, the mental parameters of the user can be quickly and conveniently acquired.
Referring to fig. 7, fig. 7 is a block diagram of a mental parameter acquisition device according to an embodiment of the present disclosure.
As shown in fig. 7, the mental parameter acquisition device may include: the receiving module 200, the obtaining module 210 and the calculating module 220, wherein the receiving module 200 is electrically connected to the obtaining module 210, and the obtaining module 210 is electrically connected to the calculating module 220.
In one possible implementation, the obtaining module 210 may include a transformation sub-module 2101 and a determination sub-module 2102.
The transform submodule 2101 is configured to perform discrete fourier transform on the first frequency band brain wave signal to obtain a transformed brain wave signal.
A determination submodule 2102 for calculating a plurality of initial energy densities for each of the respective frequencies according to a first formula, the first formula being:
In one possible implementation, the acquisition module 210 may further include a smoothing sub-module 2103 and a moving average sub-module 2104.
A stationary sub-module 2103 may be configured to obtain a stationary value for X [ k ] according to a stationary formula:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of the initial energy densities.
A moving average sub-module 2104, which may be configured to calculate an average value of the plurality of initial energy densities according to a moving average formula, which may be taken as the energy density of each frequency, the moving average formula being:
In one possible implementation, the calculation module 220 may include a calculation submodule 2201.
The calculating submodule 2201 is configured to obtain the rhythm energy corresponding to each sub-band according to a second formula, where the second formula is:
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
In a possible embodiment, the apparatus/device with control function may control the mental parameter obtaining system to obtain the mental parameter of the user, for example, wearable glasses may have control function, and the server may also have control function.
It should be noted that, although the present disclosure is described by taking a block diagram of an acquiring apparatus of mental parameters as an example, those skilled in the art can understand that the present disclosure should not be limited thereto. In fact, the user can flexibly set each module according to personal preference and/or actual application scene, and the modules in the module diagram can increase, decrease and change the execution steps of each module as long as the coordination among the modules can complete the acquisition of mental parameters.
Through the cooperation of the modules, the mental parameters of the user can be quickly and conveniently acquired.
Referring to fig. 8, fig. 8 is a block diagram of a mental parameter obtaining apparatus according to an embodiment of the present disclosure.
As shown in fig. 8, the mental parameter acquisition device may further include a storage module 260 and a display module 270.
The storage module 260 is electrically connected to the receiving module 200, the obtaining module 210 and the calculating module 220, respectively, and the storage module 260 may be configured to store the first frequency band electroencephalogram signals generated in each of the modules, the energy density of each frequency in the plurality of frequency sub-bands, the rhythm energy of the plurality of frequency sub-bands, and the mental parameters. In a possible implementation manner, the storage module 260 may be further configured to store the brain wave signals after the brain wave signals are collected by the signal collecting apparatus.
The display module 270 is electrically connected to the receiving module 200, the obtaining module 210 and the calculating module 220, respectively, and the display module 270 may be configured to display the first frequency band electroencephalogram signals, the energy densities of the multiple frequency bands, the rhythm energies of the multiple frequency bands and the mental parameters generated in each of the modules. In a possible implementation manner, the display module 270 may be further configured to display the brain wave signals after the brain wave signals are acquired by the signal acquisition equipment.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Therefore, through the matching of the modules, the mental parameter acquisition device according to the embodiment of the disclosure can quickly and conveniently acquire the mental parameters of the user, and store and display the data generated by the modules.
It should be understood that the receiving module 200, the obtaining module 210 and the calculating module 220 may be integrated into a device or apparatus capable of receiving and calculating signals, for example, a wearable glasses having signal receiving and calculating functions, and may implement the functions of the receiving module 200, the obtaining module 210 and the calculating module 220 to perform the mental parameter obtaining method. In other embodiments, the receiving module 200, the obtaining module 210, and the calculating module 220 may also be separate components, and they may transmit data through a communication device/protocol, for example, after the receiving module 200 obtains the first frequency band brain wave signal, the data transmission module (bluetooth/wifi/4G/5G/ZigBee, etc.) transmits the brain wave signal to the obtaining module 210, the obtaining module 210 processes the received data/information and transmits the processed data/information to the calculating module 220 through a data transmission module (not shown in the figure), and the calculating module 220 calculates the brain parameters according to the obtained data/information, in this case, the obtaining module 210 and the calculating module 220 may be separate devices, or may be an integrated device or apparatus, for example, a server.
Referring to fig. 9, fig. 9 is a schematic diagram illustrating a mental parameter acquisition system according to an embodiment of the present disclosure.
As shown in fig. 9, the mental parameter system includes a signal acquisition device 300, a filter device 310, and a terminal 320, and the signal acquisition device 300, the filter device 310, and the terminal 320 are electrically connected to each other to communicate.
The signal collecting apparatus 300 may be used to collect brain wave signals of the user and transmit the signals to the filtering apparatus 310.
In one possible embodiment, the signal acquisition device 300 may include a dry electrode, which may be, for example, wearable glasses that may integrate a plurality of miniaturized dry electrodes. The dry electrode can carry out harmless real-time monitoring on brain wave signals of a wearer. Of course, other signal collecting devices for collecting the brain wave signals may be used, and the present embodiment is not limited as long as the device can collect the brain wave signals of the user.
In one possible embodiment, the brain parameter system may include a transmission device (not shown), such as a wireless transmission device or a wired transmission device, for transmitting the collected brain wave signals to the filtering apparatus 310.
The filtering device 310 may be configured to process the brain wave signals to obtain first-band brain wave signals and transmit the first-band brain wave signals to the terminal 320 through the transmission apparatus (not shown).
In one possible embodiment, the filtering device 310 may perform butterworth band-pass filtering on the brain wave signals to obtain the first frequency band brain wave signals, and the frequency band of the first frequency band brain wave signals may include 0.5-40 Hz.
The terminal 320 may be configured to perform the above-described mental parameter acquisition method.
In one possible implementation, the terminal 320 may include a storage module 3201 and a display module 3202.
For a detailed description of the mental parameter obtaining method, please refer to the foregoing description, which is not repeated herein.
It should be noted that, although the present disclosure is described by taking the block diagram of the mental parameter acquisition system as an example, those skilled in the art will understand that the present disclosure should not be limited thereto. In fact, the user can flexibly set the devices according to personal preferences and/or actual application scenarios, and the devices in the block diagram can increase, decrease, and change the execution steps of each device, as long as the coordination among the devices can complete the acquisition of the mental parameters of the user.
Through the cooperation of the modules, the method and the system can quickly and conveniently acquire the mental parameters of the user, and realize the mental monitoring of the user.
Fig. 10 shows a block diagram of an apparatus 800 for mental parameter acquisition according to an example embodiment. For example, the apparatus 800 may be a wearable device, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and so forth.
Referring to fig. 10, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
Fig. 11 shows a block diagram of an apparatus 1900 for mental parameter acquisition according to an example embodiment. For example, the apparatus 1900 may be provided as a server.
Referring to FIG. 11, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
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 the 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 circuits, such as programmable logic circuits, Field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), can execute computer-readable program instructions to implement various aspects of the present disclosure by utilizing state information of the computer-readable program instructions to personalize the electronic circuits.
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.
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 terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A mental parameter acquisition method is characterized by comprising the following steps:
receiving brain wave signals of a first frequency band;
acquiring the energy density of each frequency of a plurality of sub-frequency bands in the first frequency band brain wave signal;
for each of the plurality of sub-bands, calculating a rhythmic energy of the each sub-band from the energy density of the respective frequency in the each sub-band; and
calculating the mental parameter according to the rhythm energy of the plurality of sub-frequency bands;
acquiring the energy density of each frequency of a plurality of sub-bands in the first frequency band brain wave signal, wherein the acquiring comprises the following steps:
performing discrete Fourier transform on the first frequency band brain wave signal to obtain a converted brain wave signal;
calculating a plurality of initial energy densities for each of the respective frequencies according to a first formula:
obtaining a stable value of each initial energy density according to a stable formula, wherein the stable formula is as follows:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of said initial energy densities; and
calculating an average value of the plurality of initial energy densities according to a moving average formula, and taking the average value as the energy density of each frequency, wherein the moving average formula is as follows:
2. The mental parameter acquisition method according to claim 1, wherein calculating, for each of the plurality of sub-bands, a rhythm energy of the each sub-band from the energy density of the respective frequency in the each sub-band, includes:
for each sub-frequency band, calculating rhythm energy corresponding to each sub-frequency band according to a second formula, wherein the second formula is as follows:
wherein Q (x) is the rhythm energy corresponding to each sub-frequency band, k1Is the lower frequency limit, k, of each sub-band2P (k) is the energy density corresponding to each frequency in each frequency sub-band.
3. The brain parameter acquisition method according to any one of claims 1-2, further comprising:
storing the first frequency band brain wave signals, the energy densities of the plurality of sub-bands, the rhythm energies of the plurality of sub-bands and the mental parameters generated in the execution of the method;
displaying the first frequency band brain wave signals, the energy densities of the plurality of sub-bands, the rhythm energies of the plurality of sub-bands and the mental parameters generated in the execution of the method.
4. An mental parameter acquisition apparatus, comprising:
the receiving module is used for receiving brain wave signals of a first frequency range;
the acquisition module is used for acquiring the energy density of each frequency of a plurality of sub-frequency bands in the first frequency band brain wave signal;
a calculation module, configured to calculate, for each of multiple frequency sub-bands, a rhythm energy of each frequency sub-band according to the energy density of each frequency in the frequency sub-band, and calculate the mental parameter according to the rhythm energy of the frequency sub-bands;
the acquisition module includes:
the transformation submodule is used for carrying out discrete Fourier transformation on the first frequency band brain wave signal to obtain a transformed brain wave signal;
a determination sub-module for calculating, for the converted brain wave signals, a plurality of initial energy densities for each of the respective frequencies according to a first formula:
a stationary sub-module, configured to obtain a stationary value of each initial energy density according to a stationary formula, where the stationary formula is:
R(k)＝10×log10[X(k)]wherein R (k) is a plateau value for each of said initial energy densities; and
a moving average submodule, configured to calculate an average value of the plurality of initial energy densities according to a moving average formula, where the average value is used as the energy density of each frequency, and the moving average formula is:
5. The mental parameter acquisition device according to claim 4, wherein the calculation module comprises:
a calculating submodule, configured to, for each sub-band, obtain rhythm energy corresponding to each sub-band according to a second formula,
the second formula is:
wherein Q (x) is the rhythm energy corresponding to each sub-frequency band, k1Is the lower frequency limit, k, of each sub-band2P (k) is the energy density corresponding to each frequency in each frequency sub-band.
6. The brain parameter acquisition device according to any one of claims 4-5, wherein the device further comprises:
the storage module is used for storing the first frequency band brain wave signals, the energy densities of the multiple sub-frequency bands, the rhythm energies of the multiple sub-frequency bands and the mental parameters generated in each module;
and the display module is used for displaying the first frequency band brain wave signals, the energy densities of the multiple sub-frequency bands, the rhythm energies of the multiple sub-frequency bands and the mental parameters generated in each module.
7. An mental parameter acquisition system, the system comprising: a signal acquisition device, a filtering device and a terminal,
the signal acquisition equipment is used for acquiring brain wave signals of a user and sending the brain wave signals to the filtering equipment;
the filtering equipment processes the brain wave signals to obtain first frequency range brain wave signals and sends the first frequency range brain wave signals to a terminal;
the terminal is configured to perform the method of any of claims 1-3.
8. The system as claimed in claim 7, wherein the filtering device performs Butterworth band-pass filtering on the brain wave signals to obtain the first frequency band brain wave signals, the frequency band of which includes 0.5-40 Hz.
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