CN111671399B

CN111671399B - Method and device for measuring noise perception intensity and electronic equipment

Info

Publication number: CN111671399B
Application number: CN202010561637.3A
Authority: CN
Inventors: 陶晓明; 耿冰蕊; 段一平; 刘可; 彭翔; 陆建华
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2020-06-18
Filing date: 2020-06-18
Publication date: 2021-04-27
Anticipated expiration: 2040-06-18
Also published as: CN111671399A

Abstract

The invention provides a method and a device for measuring noise perception intensity and electronic equipment, which relate to the technical field of signal processing and comprise the steps of obtaining an original brain wave signal, wherein the original brain wave signal is a brain wave signal monitored when a testee listens to preset audio, the preset audio is audio obtained by adding target noise intensity noise on the original audio in multiple sections, and the target noise intensity is any one of the preset noise intensities; the noise perception strength of the subject on the original audio is determined based on the original brain wave signal. The method determines the noise perception intensity of the testee to the original audio by analyzing the nerve electric reaction (original brain wave signal) of the primary cognition of the testee to the preset audio, and provides the physiological standard of the noise perception intensity, so that the quality evaluation of the audio to be tested can be calibrated and guided, all the defects of the traditional subjective evaluation are avoided, and the technical problem of low accuracy of the noise perception intensity measuring method in the prior art is effectively solved.

Description

Method and device for measuring noise perception intensity and electronic equipment

Technical Field

The present invention relates to the field of signal processing technologies, and in particular, to a method and an apparatus for measuring noise perception intensity, and an electronic device.

Background

Sound is invaluable to human communication and daily life, hearing is always open, and various audio information from all directions is ready to be received, and unwanted, unpleasant or harmful sound becomes noise, and noise in audio becomes one of important factors affecting the audio perception quality of a user. In the prior art, the international telecommunication union telecommunication standard bureau proposes a method for measuring audio quality by using Mean Opinion Score (MOS), and although this method can obtain the subjective evaluation quality of the user on the audio, it is easily influenced by the expectations, bias and strategy of the user and the external environment, and it is difficult to ensure the accuracy of the audio quality evaluation.

In summary, the method for measuring the noise perception intensity in the prior art has the technical problem of low accuracy.

Disclosure of Invention

The invention aims to provide a method and a device for measuring noise perception intensity and electronic equipment, so as to relieve the technical problem of low accuracy of the method for measuring the noise perception intensity in the prior art.

In a first aspect, an embodiment of the present invention provides a method for measuring noise perception intensity, including: acquiring an original brain wave signal, wherein the original brain wave signal is a brain wave signal monitored by a testee when listening to a preset audio, the preset audio is a plurality of sections of audio with target noise intensity noise added on the original audio, and the target noise intensity is any one of preset noise intensities; determining a noise perception intensity of the subject for the original audio based on the original brain wave signal.

In an alternative embodiment, determining the noise perception intensity of the subject on the original audio based on the original brain wave signals includes: preprocessing the original brain wave signals to obtain preprocessed brain wave signals, wherein the preprocessed brain wave signals do not contain fluctuating signals caused by non-audio stimulation; determining the directional information flow intensity between the brain wave acquisition electrodes under the target noise intensity based on the preprocessed brain wave signals; calculating a comprehensive difference coefficient vector of the target noise intensity full frequency band based on the directed information flow intensity; determining the noise perception intensity of the subject on the original audio based on the integrated difference coefficient vector of all the noise intensities.

In an alternative embodiment, determining the directional information flow strength between the brain wave collecting electrodes under the target noise strength based on the preprocessed brain wave signals includes: determining a target brain wave signal based on the preprocessed brain wave signal, wherein the target brain wave signal is a brain wave signal monitored when the testee listens to an audio frequency added with target noise intensity noise on an original audio frequency; the target brain wave signal and noise are combinedConverting the time domain corresponding relation of the signals into the functional relation of the target brain wave signals and the noise signals in the frequency domain, wherein the time domain corresponding relation is expressed as

A_dA d-th order AR model coefficient matrix representing a x a, p representing the order of the AR model, a representing the number of brain wave collecting electrodes, x_i＝(x_1,i,...,x_a,i) Representing brain wave signal vectors, e_i＝(e_1,i,...,e_a,i) Representing multivariate uncorrelated noise vectors, i represents sampling time points of the brain wave signals, and the functional relationship in the frequency domain is represented by x (f) ═ h (f) e (f), x (f) represents the brain wave signals with the brain wave frequency f in the frequency domain, h (f) represents the system transfer function, and e (f) represents the noise signals with the brain wave frequency f in the frequency domain; and determining the directional information flow intensity between the brain wave acquisition electrodes under the target noise intensity based on the system transfer function of the functional relation.

In an optional embodiment, the preset noise intensity includes a zero-order noise intensity; determining a target brain wave signal based on the preprocessed brain wave signals, including: intercepting a target brain wave signal in the preprocessed brain wave signal by using a contrast audio, wherein the contrast audio is an audio obtained by adding zero-order noise intensity noise to an original audio.

In an alternative embodiment, the directional information flow intensity between the brain wave collecting electrodes at the target noise intensity is represented as F a × a matrices DTF^q(f) Wherein a denotes the number of brain wave collecting electrodes, q denotes the target noise intensity, F denotes the brain wave frequency, and F denotes the total number of brain wave frequencies.

In an alternative embodiment, calculating a comprehensive difference coefficient vector of the target noise intensity in the full frequency band based on the directional information flow intensity includes: respectively constructing weighted directed graphs among the brain wave acquisition electrodes under each brain wave frequency based on the directed information flow strength

Wherein V { (1, 2.·, a } represents a brain wave acquisition electrode, a { (i, j) | i, j ∈ 1.·, a, and i ≠ j } represents a directed edge set between any two brain wave acquisition electrodes,

representing the weighted directed graph

The weight of each directed edge in (i, j), and the weight of the directed edge (i, j)

Computing weighted directed graph

Weighting degree of middle brain wave collecting electrode

And weighted directed graph

Weighting degree of middle brain wave collecting electrode

Wherein, IN (j) { (i, j) ∈ M | i ∈ V } represents the arc-in set of the electroencephalogram acquisition electrode j, M represents the set of the directional edge set of the weighted directed graph and a preset number of maximum weight connected edges in the set,

representing the weighted degree of the brain wave collecting electrode j under the condition that the target noise intensity is q and the brain wave frequency is f; weighting degree of each brain wave collecting electrode

Corresponding degree of tape weight

Comparing to obtain the weighted sequence with the target noise intensity of q and the brain wave frequency of f

Equation of utilization

And calculating the comprehensive difference coefficient vector of the target noise intensity q in the full frequency band.

In an alternative embodiment, the determining the noise perception strength of the subject on the original audio based on the integrated difference coefficient vector of all the noise strengths comprises: and clustering the comprehensive difference coefficient vectors of all the noise intensities by using a preset clustering method to obtain the noise perception intensity of the original audio frequency of the testee.

In a second aspect, an embodiment of the present invention provides a device for measuring noise perception strength, including: the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an original brain wave signal, the original brain wave signal is a brain wave signal monitored by a testee when listening to preset audio, the preset audio is a multi-section audio with target noise intensity noise added on the original audio, and the target noise intensity is any one of preset noise intensities; a determination module for determining the noise perception intensity of the subject on the original audio based on the original brain wave signal.

In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method in any one of the foregoing embodiments.

In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any one of the foregoing embodiments.

The invention provides a method for measuring noise perception intensity, which comprises the following steps: acquiring an original brain wave signal, wherein the original brain wave signal is a brain wave signal monitored by a testee when listening to a preset audio, the preset audio is a multi-section audio with target noise intensity noise added on the original audio, and the target noise intensity is any one of preset noise intensities; the noise perception strength of the subject on the original audio is determined based on the original brain wave signal.

Compared with the prior art, the method for measuring the noise perception intensity provided by the invention determines the noise perception intensity of a testee on an original audio by analyzing the nerve-electric reaction (original brain wave signal) of the primary cognition of the testee on a preset audio, provides the physiological standard of the noise perception intensity, can calibrate and guide the quality evaluation of the audio to be measured, avoids all the defects of the traditional subjective evaluation, and effectively solves the technical problem of low accuracy of the method for measuring the noise perception intensity in the prior art.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a method for measuring noise perception strength according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating a method for determining a noise perception strength of a subject for an original audio based on an original brain wave signal according to an embodiment of the present invention;

fig. 3 is a schematic diagram illustrating a preprocessing process performed on an original electroencephalogram signal according to an embodiment of the present invention;

fig. 4 is a schematic diagram of brain connectivity of a subject, according to an embodiment of the present invention, taking the first 10% of a DTF value with an original audio of sea wave and a noise intensity of 0;

fig. 5 is a schematic diagram of brain connectivity of a subject, according to an embodiment of the present invention, taking the first 10% of a DTF value with an original audio of sea wave and a noise intensity of 1;

fig. 6 is a schematic diagram of brain connectivity of a subject, according to an embodiment of the present invention, taking the first 10% of a DTF value with an original audio of sea wave and a noise intensity of 4;

fig. 7 is a schematic diagram of brain connectivity of a subject, according to an embodiment of the present invention, taking the first 10% of a DTF value with an original audio of sea wave and a noise intensity of 5;

fig. 8 is a schematic diagram illustrating weighting of a subject with an original audio of a sea wave and a noise intensity of 0 according to an embodiment of the present invention;

fig. 9 is a schematic diagram of weighting of original audio by a subject with a noise intensity of 1 according to an embodiment of the present invention;

fig. 10 is a schematic diagram illustrating weighting of original audio of a subject by sea wave with a noise intensity of 4 according to an embodiment of the present invention;

fig. 11 is a schematic diagram illustrating weighting of original audio of a subject by sea wave with a noise intensity of 5 according to an embodiment of the present invention;

fig. 12 is a schematic diagram of the measurement results of the noise perception intensity of the test subjects 1 to 10 with respect to the audios 1 to 4 according to the embodiment of the present invention;

fig. 13 is a diagram illustrating subjective evaluation of audio contents by a subject according to an embodiment of the present invention;

fig. 14 is a schematic diagram of absolute difference integral ratios of noise signals of different levels and an original audio signal according to an embodiment of the present invention;

FIG. 15 is a functional block diagram of an apparatus for measuring the perceived intensity of noise according to an embodiment of the present invention;

fig. 16 is a schematic view of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.

In the prior art, the telecommunication standard bureau of the international telecommunication union provides a method for measuring audio quality by using mean opinion score, although the method can obtain the subjective evaluation quality of a user to audio, the method lacks an information flow related to human perception, and the score of the user is easily disturbed by external factors so as to influence the preference of the user, so that the accuracy of audio quality evaluation is difficult to ensure, namely, the subjective evaluation is easily influenced by the external factors; in addition, the international telecommunication union telecommunication standard bureau also provides proposals such as psqm (perceptual Speech Quality measure) perceptual call Quality measurement and pesq (perceptual Evaluation of Speech Quality measure), and these proposals are operable in practical application based on a certain fully-referenced Evaluation algorithm for audio voice service, but the experience Quality of the user for the audio voice service cannot be completely and truly reflected, that is, objective parameters cannot comprehensively reflect the experience Quality of the user. Embodiments of the present invention provide a method for measuring noise perception strength to alleviate the above-mentioned technical problems.

Example one

Fig. 1 is a flowchart of a method for measuring noise perception intensity according to an embodiment of the present invention, and as shown in fig. 1, the method specifically includes the following steps:

in step S12, an original brain wave signal is acquired.

In order to avoid the disadvantage that subjective evaluation is easily affected by external environment, user preference and social background and solve the problem that objective parameters cannot reflect the real experience quality of a user, in the method for measuring the noise perception intensity provided by the embodiment of the invention, an original brain wave signal is used as an analysis object, wherein the original brain wave signal is a brain wave signal monitored when a testee listens to preset audio, the preset audio is audio obtained by adding target noise intensity noise to the original audio in multiple sections, and the target noise intensity is any one of the preset noise intensities. Because the original brain wave signals can acquire the neuroelectric reaction of primary cognition of human beings, the original brain waves can reflect the real experience of a testee on noise.

For convenience of understanding, the following description illustrates an acquisition process of an original brain wave signal, and it is assumed that a noise perception intensity of a subject on an ocean wave is to be analyzed, it is required to determine what kind of noise is added to the ocean wave and which optional preset noise intensities are, and since an audio quality reduction generated by an actual audio codec may be approximated by a gaussian white noise, in order to ensure an accuracy of the method implementation, the noise added to the original audio may be selected as the gaussian white noise, and the preset noise intensities may be set from high to low, and are all within a tolerable range of the subject.

Assuming that the number of the preset noise intensities is 6, when the audio is processed, gaussian white noise with 6 noise intensities is added to the sea wave (original audio) respectively, so as to obtain 6 segments of processed audio, in order to ensure the accuracy of the test result, firstly, the time length of the noise added to the original audio is shorter than the time length of the original audio, and noise with the same time length is added to each segment of original audio, for example, if the time length of the original audio is 15s and the time length of the added noise is 5s, each segment of the processed 6 segments of 15s audio contains 5s of noise with different noise intensities; furthermore, in order to ensure the reliability and data stability of the original electroencephalogram signal, when the original electroencephalogram signal of the subject is collected, 6 sections of processed audio frequencies are used as units, the audio frequencies are played circularly for multiple times, and the playing sequence of each time is random. The embodiment of the invention does not specifically limit the method and the times of the circular playing, and a user can set the method according to actual requirements.

In the above explanation, the original audio is used as one type of audio, and the method of the present invention is also suitable for simultaneously measuring the noise perception intensity of multiple original audio, so that the tested person should circularly play multiple audio with multiple audio processed by multiple noise intensities respectively as a unit in the acquisition process of the original electroencephalogram signal, and the playing sequence of each time should be random. For example, the original audio types are 4, the number of preset noise intensities is 6, and the audio per playing unit is 4 × 6 — 24 segments. It should be noted that, when there are multiple original audios, it is necessary to ensure that all the original audios have the same quality, and specifically, the sampling frequency and the number of sampling bits may be the same.

In addition, acquiring an original brain wave signal, firstly, constructing an acquisition environment of the brain wave signal, including: the system comprises an audio client computer, an electromagnetic shielding room, a Presentation software system, an electroencephalogram signal acquisition instrument and an electroencephalogram signal recording instrument, wherein the electromagnetic shielding room adopts a GP6 standard electromagnetic shielding room; the Presentation software system is a software system for neuroscience research of the NBS company in America, and can achieve millisecond-level time precision without special hardware; an electroencephalogram signal acquisition instrument can adopt an actiCAP Xpress Twist product of BRAIN PRODUCTS, Germany, wherein the electroencephalogram (electroencephalogram) electrode cap is distributed according to the international 10-20 standard, and a ground electrode is arranged between FP1 and FP 2; the electroencephalogram signal recorder has the sampling frequency of 500 Hz.

Then, the subject listens to the preset audio frequency in the above-mentioned brain wave signal collecting environment, and further obtains the original brain wave signal of the subject, it should be noted that the above-mentioned subject is qualified in terms of physical condition and has no hearing disorder, when the audio is not played and the brain wave is in a calm state, the impedance between the cerebral cortex and the electrode is less than 10K omega, and the original brain wave signals used for analysis should be collected by the testee strictly according to the operation requirements, there is no brain wave signal abnormality caused by misoperation, in order to ensure the availability of the analysis data, can lead a plurality of testees to listen to the preset audio frequency at the same time and collect a plurality of original brain wave signals at the same time, and then, selecting qualified original brain wave signals (without brain wave signal abnormality caused by improper operation), and then analyzing the original brain wave signals of each testee. During the acquisition of the original brain wave signals, the examinee can be optionally filled in an audio experience questionnaire, specifically, whether the examinee can bear the noise intensity in each piece of processed audio and whether the type of the audio to be heard (for example, soothing or impatience) is determined.

In step S14, the noise perception strength of the subject for the original audio is determined based on the original brain wave signal.

Furthermore, because the original brain wave signal can acquire the neuroelectric reaction of human primary cognition, after the original brain wave signal is acquired, the original brain wave signal can be used as a data source to perform related data processing on the original brain wave signal, so that the noise perception intensity of the testee on the original audio frequency is determined, namely, the noise perception intensity of the testee on the original brain wave signal is at which level of the preset noise intensity.

In an alternative embodiment, as shown in fig. 2, the step S14 of determining the noise perception strength of the subject on the original audio based on the original brain wave signal specifically includes the following steps:

step S141, pre-processing the original brain wave signal to obtain a pre-processed brain wave signal.

Specifically, in order to remove signal fluctuation caused by non-audio stimulation as much as possible and improve the accuracy of signal analysis in subsequent steps, after an original brain wave signal is obtained, the original brain wave signal is firstly preprocessed, wherein a flow involved in preprocessing is as shown in fig. 3, and during preprocessing, the positioning of brain wave channels is specifically identified in EEGlab through a data identifier carried by EEG data (original brain wave signal); selecting the reference electrode specifically refers to Re-referencing data by using the Re-reference in EEGlab; the useful channel selection is to remove useless electrodes by utilizing a Select data function, wherein the useless electrodes represent electrodes which are not used in the whole brain wave signal acquisition process, and the useless electrodes are not necessarily used in practical application; the band-pass filtering specifically aims at obtaining full-band original brain wave signals, so that the band-pass filtering range is selected from 2-50 Hz; baseline correction refers to removal of baseline using RemoveBaseline; ICA artifact removal means removing electrooculogram by ICA independent component analysis; the noise component elimination refers to eliminating noise by utilizing a Remove components function. The original brain wave signals are still time domain signals after being preprocessed, and the preprocessed brain wave signals do not contain fluctuating signals caused by non-audio stimulation.

And step S142, determining the directional information flow intensity between the brain wave collecting electrodes under the target noise intensity based on the preprocessed brain wave signals.

After the pre-processed brain wave signals are obtained, since the original brain wave signals are acquired by the testee listening to a plurality of sections of audio frequency added with different noise intensities, in order to analyze the brain wave signals with different noise intensities, the directional information flow intensity between the brain wave acquisition electrodes when the testee listens to the audio frequency with each noise intensity needs to be determined according to the pre-processed brain wave signals. For example, if the preset noise intensity is 6, the directional information flow intensity between the electroencephalogram acquisition electrodes at 6 noise intensities will be obtained in step S142, and the directional information flow intensity between the electroencephalogram acquisition electrodes at each noise intensity can be further subdivided according to the electroencephalogram frequency.

And step S143, calculating a comprehensive difference coefficient vector of the full frequency band of the target noise intensity based on the directed information flow intensity.

After the directional information flow strength between the brain wave acquisition electrodes under the target noise strength is obtained, the weighted sequence of the brain wave acquisition electrodes of each brain wave frequency under the target noise strength can be further determined according to the directional information flow strength between the brain wave acquisition electrodes, the weighted sequences of the brain wave acquisition electrodes of all brain wave frequencies under the target noise strength are summed, and the summed result is used as the comprehensive difference coefficient vector of the full frequency band of the target noise strength.

And step S144, determining the noise perception intensity of the testee to the original audio based on the comprehensive difference coefficient vector of all the noise intensities.

Finally, after obtaining the comprehensive difference coefficient vector under each noise intensity, the noise perception intensity of the testee to the original audio can be further determined. Specifically, a preset clustering method can be used to cluster the comprehensive difference coefficient vectors of all the noise intensities, so as to obtain the noise perception intensity of the original audio frequency of the testee. And optionally, a preset clustering method adopts K-means clustering, the clustering classes are set to be 2 classes, the membership degree is calculated according to the attribution degree of each sample of each class, and the noise perception intensity of the testee to the original audio is determined according to the clustering result.

The process of determining the noise perception strength of the original audio by the subject based on the original brain wave signal is briefly described above, and the specific steps involved therein are described in detail below.

In an optional embodiment, in step S142, determining the directional information flow strength between the electroencephalogram acquisition electrodes under the target noise strength based on the preprocessed electroencephalogram signals specifically includes the following steps:

in step S1421, a target brain wave signal is determined based on the preprocessed brain wave signal.

As can be seen from the above description, the original brain wave signal is acquired after the subject listens to the audio frequency of the noise with various noise intensities, and after the fluctuation signal caused by the non-audio stimulus is removed by the preprocessing, the obtained preprocessed brain wave signal is still the brain wave signal caused by various noise intensities, and the brain wave signal of the subject listening to the audio frequency without noise is included therein, so that the target brain wave signal needs to be separated from the preprocessed brain wave signal, where the target brain wave signal is the brain wave signal monitored by the subject listening to the audio frequency with the target noise intensity noise added to the original audio frequency. If the preset noise intensity is 6, the obtained target brain wave signals are 6.

Step S1422, convert the time domain correspondence relationship between the target brain wave signal and the noise signal into a functional relationship between the target brain wave signal and the noise signal in the frequency domain.

In order to facilitate analysis of brain wave signals, a time domain corresponding relation between a target brain wave signal and a noise signal needs to be converted into a functional relation between the target brain wave signal and the noise signal in a frequency domain, before the conversion, the target brain wave signal in the time domain needs to be determined, then the target brain wave signal is divided by using a preset time step length, and then a brain wave signal vector is obtained.

The user can set the sampling time point of the brain wave signal according to the actual requirement, for example, the highest sampling frequency of the brain wave signal collecting device is 500 sampling points, and then each sampling time point can be used as the unit time step length of the brain wave signal vector.

The time domain correspondence is expressed as

A_dA d-th order AR model coefficient matrix representing a x a, p representing the order of the AR model, a representing the number of brain wave collecting electrodes, x_i＝(x_1,i,...,x_a,i) Representing brain wave signal vectors, e_i＝(e_1,i,...,e_a,i) Denotes a multivariate uncorrelated noise vector, and i denotes a sampling time point of the brain wave signal. In order to obtain the functional relationship in the frequency domain, firstly, the two sides of the expression of the time domain corresponding relationship are multiplied simultaneously

And taking expectation, and then obtaining the equation: r (-s) + A₁R(1-s)+...+A_pR (p-s) ═ 0, where R(s) ═ E [ x_t,x^T _t+s]Is x_tFinally, the above equation is expressed in the frequency domain, so as to obtain the functional relationship between the target electroencephalogram signal and the noise signal in the frequency domain, wherein the functional relationship in the frequency domain is expressed as x (f) ═ h (f) e (f), x (f) represents the electroencephalogram signal with the electroencephalogram frequency f in the frequency domain, h (f) represents the system transfer function, and e (f) represents the noise signal with the electroencephalogram frequency f in the frequency domain.

Step S1423, determining the directional information flow intensity between the electroencephalogram acquisition electrodes under the target noise intensity based on the system transfer function of the functional relationship.

Specifically, the system transfer function h (f) of the functional relationship between the target brain wave signal and the noise signal in the frequency domain may be embodied in a matrix form, and elements in the matrix represent the original directional information flow intensity between the brain wave collecting electrodes, in order to increase the data processing speed and the accuracy of the processing result, the column square normalization of h (f) is performed, so that the normalized matrix satisfies the column square sum of 1, and the result of the normalization processing is taken as the directional information flow intensity between the brain wave collecting electrodes under the target noise intensity.

As can be seen from the above description, when the target brain wave signal and the noise signal are represented in the frequency domain, each brain wave frequency f has a corresponding system transfer function h (f), so that, under the target noise intensity, different brain wave frequencies f have directional information flow intensities between different brain wave collecting electrodes.

In an alternative embodiment, the directional information flow strength between the brain wave acquisition electrodes at the target noise level is represented as F a × a matrices DTF^q(f) Wherein a denotes the number of brain wave collecting electrodes, q denotes a target noise intensity, F denotes a brain wave frequency, F denotes a total number of brain wave frequencies, DTF^q(f) In the matrix, the values of the ith row and the jth column represent the directional information flow strength between the ith brain wave collecting electrode and the jth brain wave collecting electrode at the brain wave frequency f.

In an optional embodiment, the preset noise intensity includes a zero-order noise intensity; in the step S1421, the target brain wave signal is determined based on the preprocessed brain wave signal, which specifically includes the following steps:

intercepting the target brain wave signal in the preprocessed brain wave signal by using a contrast audio, wherein the contrast audio is the audio with zero-order noise intensity noise added on the original audio.

Specifically, in the embodiment of the present invention, in order to facilitate extraction of the target brain wave signal, a zero-order noise intensity is set in the preset noise intensity, and an audio frequency to which the zero-order noise intensity noise is added to the original audio frequency is the same as an original audio frequency to which no noise is added, so that the audio frequency to which the zero-order noise intensity noise is added to the original audio frequency can be used as a comparison audio frequency, and compared with an audio frequency to which other non-zero-order noise intensity noise is added, so that the target brain wave signal can be intercepted from the preprocessed brain wave signal by using the comparison audio frequency.

In the above, a detailed description is given to how to determine the directional information flow strength between the electroencephalogram acquisition electrodes under the target noise strength, and in the following, a detailed description is given to how to calculate the comprehensive difference coefficient vector of the full frequency band of the target noise strength.

In an optional embodiment, in step S143, calculating a comprehensive difference coefficient vector of the target noise intensity in the full frequency band based on the directional information stream intensity specifically includes the following steps:

step S1431 of respectively constructing weighted directed graphs between the electroencephalogram acquisition electrodes at each electroencephalogram frequency based on the directed information flow intensity

representing weighted directed graphs

As can be seen from the above description, after the directional information flow strength between the electroencephalogram acquisition electrodes is obtained for each noise intensity, the directional information flow strength between the electroencephalogram acquisition electrodes can be represented for any noise intensity (target noise intensity)Is F a × a matrices DTF^q(f) Each DTF^q(f) Can construct a weighted directed graph

That is, if the total number F of electroencephalogram frequencies is 44, any one noise intensity corresponds to 44 weighted directed graphs. If there are 6 kinds of preset noise intensities, then 6 × 44 weighted directed graphs will be obtained after the processing of step S1431.

Step S1432, calculating the weighted directed graph

Weighting degree of middle brain wave collecting electrode

And weighted directed graph

Weighting degree of middle brain wave collecting electrode

After obtaining all weighted directed graphs, the weighted degree of each electroencephalogram acquisition electrode can be calculated by using the above formula, wherein in (j) { (i, j) ∈ M | i ∈ V } represents an arc-in set of the electroencephalogram acquisition electrode j, M represents a set of a preset number of maximum weighted continuous edges in a directed edge set of the weighted directed graph,

and representing the weighted degree of the brain wave collecting electrode j under the condition that the target noise intensity is q and the brain wave frequency is f. That is to say, when performing weighted degree calculation, the calculation range does not calculate all the directed edges in the weighted directed graph, but calculates a preset number of maximum weight continuous edges in the directed edge set and the maximum weight continuous edges in the weighted directed graph, and optionally, the calculation range is selected as the maximum weight continuous edges of the top 10% in the weighted directed graph.

Step S1433, weighting degree of each brain wave collecting electrode

Corresponding degree of tape weight

Comparing to obtain a weighted sequence with the target noise intensity of q and the brain wave frequency of f

After the weighted degree of each brain wave collecting electrode under each brain wave frequency of all the noise intensities is obtained, the non-zero-order noise intensity and the weighted degree of the brain wave collecting electrode under the same brain wave frequency f are compared one by one, and then the weighted degree sequence with the target noise intensity of q and the brain wave frequency of f is obtained

Step S1434, using formula

After the weighted sequences of all brain wave frequencies with the target noise intensity q are obtained, summing the weighted sequences of all brain wave frequencies under the target noise intensity, and further obtaining a comprehensive difference coefficient vector of the target noise intensity q in a full frequency band.

Finally, the obtained comprehensive difference coefficient vector lambda of the full frequency band of all the noise intensities^qClustering is performed to obtain the limit value of significant changes in brain response to noise through the difference between brain connectivity and noise-free state.

In summary, the method for measuring noise perception intensity provided by the embodiment of the present invention can determine the noise perception intensity of the subject on the original audio by analyzing the brain wave signal of the subject listening to the preset audio, and further ensure the accuracy of the noise perception intensity result obtained by the method of the present invention in view of that the brain wave signal is the neuroelectric reaction of the human primary cognition.

To verify the effect of the method of the present invention, the inventors conducted the following experiments: firstly, targeted audio stimulation materials are prepared, the sampling frequency of all experimental materials is 22.05kHz, and the sampling digit is 16 bits. Four pieces of original audio, which are flat piano sound, sea wave sound, alarm sound, and mosquito buzzing sound, are used, and gaussian white noise (referred to as

intensity levels

0, 1,2, 3, 4, 5, respectively) having intensities (power spectral densities) of 0, 0.001, 0.005, 0.01, 0.03, and 0.1 for 5 seconds is added to the four pieces of original audio, respectively. Under the condition of four-segment audio environment with the duration of 15s, a testee respectively receives an electroencephalogram experiment of Gaussian white noise interference of six power spectral densities and acquires an original brain wave signal. The experiment included three sections, each Section playing the 24 segments of audio twice in random order, with a three minute rest time between sections.

Through the processing of the original brain wave signals, a brain connectivity diagram of the subject, which is 10% of the DTF value of each original audio under each noise intensity, is obtained, fig. 4 shows a brain connectivity diagram of the subject, which is 10% of the DTF value of the original audio, which is a sea wave sound and has a noise intensity of 0, fig. 5 shows a brain connectivity diagram of the subject, which is 10% of the DTF value of the original audio, which is a sea wave sound and has a noise intensity of 1, fig. 6 shows a brain connectivity diagram of the subject, which is 10% of the DTF value of the original audio, which is a sea wave sound and has a noise intensity of 4, and fig. 7 shows a brain connectivity diagram of the subject, which is 10% of the DTF value of the original audio, which is a sea wave sound and has a noise intensity of 5.

As can be seen from the DTF brain connectivity graph, after the weighted directed graph is constructed, the directed edge connection condition has great similarity in class, and the directed edge weight with the strongest information flow shows gradual change with obvious regularity. Specifically, there are similar directed edge weights between the noise intensity 0 and the noise intensity 1, similar weights between the noise intensity 4 and the noise intensity 5, and obvious differences between the two groups. The difference can be more obviously reflected by calculating the weighted degree of each brain wave acquisition electrode.

Fig. 8 shows a schematic diagram of weighting of the original audio by the subject with a noise intensity of 0, fig. 9 shows a schematic diagram of weighting of the original audio by the subject with a noise intensity of 1, fig. 10 shows a schematic diagram of weighting of the original audio by the subject with a noise intensity of 4, and fig. 11 shows a schematic diagram of weighting of the original audio by the subject with a noise intensity of 5. As is clear from fig. 8 to 11, the overall tendency of the change in the weighting degrees increases as the noise intensity increases, the maximum value of the average weighting degree with a noise intensity of 0 is only 0.076, the maximum value of the average weighting degree with a noise intensity of 1 becomes 0.093, and the maximum values of the average weighting degrees with a noise 5 and a noise 6 are both 0.1.

And finally, adopting K-means clustering, setting the clustering category as 2 categories, and showing the noise perception intensity measurement results of the No. 1 to No. 10 testees on the audios 1 to 4 in the graph 12, wherein the gray value in the graph is the probability sum of all samples which are clustered to be unacceptable. Fig. 13 shows the subjective evaluation of the audio content by the subject, and it can be seen from fig. 12 and 13 that, since the audio 1 is a calm piano curve and the audio 2 is a sound of a relaxed sea wave, the influence of the gaussian white noise added on the basis of the above is very obvious, in the case of the

audio

1 and 2, the noise levels are 1 and 2 respectively, the DTF brain connectivity is very different from the noise-free case, and the limit value has a strong sensitivity; audio 3 is an emergency alarm sound and audio 4 is an annoying mosquito call, and in this audio environment, the mood of the person being tested for anxiety is less affected by the added noise.

Fig. 14 shows absolute differential integral ratios of noise signals of different levels to the original audio signal, and it can be seen from fig. 4 to 14 that white noise of the same intensity has a smaller effect on the user in the mood of the existing user, compared to the relaxed audio environment. This is consistent with the lower subject limits for audio 1 and 2 (

noise levels

1 and 2, respectively) than for audio 3 and 4 (noise level 4, both). Of all the audio, audio 3 is the most noisy, with white gaussian noise at a more indistinguishable level, so that the connectivity of the tested brain is not highly uniform before the noise level reaches 4, compared to the case without noise, and the limit is the least sensitive. On the whole level, the endurance limits of the

audios

1 and 2 are respectively the

noise levels

1 and 2, and are very low noise levels; the endurance limits of

audio

3 and 4 are both noise level 4, which is a very high noise level, and the limits within the two sets of audio are highly consistent with each other. Therefore, the user's tolerance limit to noise is considered to be highly related to the emotion brought by the audio itself and inversely proportional to the difference degree between the audio signal itself and the noise signal.

Example two

The embodiment of the present invention further provides a device for measuring noise perception intensity, where the device for measuring noise perception intensity is mainly used for executing the method for measuring noise perception intensity provided in the first embodiment of the present invention, and the following describes the device for measuring noise perception intensity provided in the embodiment of the present invention in detail.

Fig. 15 is a functional block diagram of an apparatus for measuring perceived noise strength according to an embodiment of the present invention, and as shown in fig. 15, the apparatus mainly includes: an obtaining module 10 and a determining module 20, wherein:

the acquiring module 10 is configured to acquire an original brain wave signal, where the original brain wave signal is a brain wave signal monitored by a subject when listening to a preset audio, the preset audio is multiple segments of audio obtained by adding a noise with a target noise intensity to the original audio, and the target noise intensity is any one of preset noise intensities.

And a determining module 20, configured to determine a noise perception strength of the original audio of the subject based on the original brain wave signal.

Compared with the prior art, the noise perception intensity measuring device provided by the invention determines the noise perception intensity of a testee on an original audio by analyzing the nerve-electric reaction (original brain wave signal) of the primary cognition of the testee on the preset audio, provides the physiological standard of the noise perception intensity, can calibrate and guide the quality evaluation of the audio to be measured, avoids all the defects of the traditional subjective evaluation, and effectively solves the technical problem of low accuracy of the noise perception intensity measuring method in the prior art.

Optionally, the determining module 20 includes:

the preprocessing unit is used for preprocessing the original brain wave signals to obtain preprocessed brain wave signals, wherein the preprocessed brain wave signals do not contain fluctuating signals caused by non-audio stimulation.

And the first determining unit is used for determining the directional information flow intensity between the brain wave collecting electrodes under the target noise intensity based on the preprocessed brain wave signals.

And the calculating unit is used for calculating the comprehensive difference coefficient vector of the target noise intensity full frequency band based on the directed information flow intensity.

And the second determining unit is used for determining the noise perception intensity of the testee on the original audio based on the comprehensive difference coefficient vector of all the noise intensities.

Optionally, the first determining unit includes:

the first determining subunit is configured to determine a target brain wave signal based on the preprocessed brain wave signal, where the target brain wave signal is a brain wave signal monitored by the subject when the subject listens to an audio to which a target noise intensity noise is added on an original audio.

A conversion subunit, configured to convert a time domain correspondence relationship between the target brain wave signal and the noise signal into a functional relationship between the target brain wave signal and the noise signal in a frequency domain, where the time domain correspondence relationship is expressed as

A_dA d-th order AR model coefficient matrix representing a x a, p representing the order of the AR model, a representing the number of brain wave collecting electrodes, x_i＝(x_1,i,...,x_a,i) Representing brain wave signal vectors, e_i＝(e_1,i,...,e_a,i) Representing multivariate uncorrelated noise vectors, i representing the sampling time point, frequency of the brain wave signalThe functional relationship in the domain is represented by x (f) ═ h (f) e (f), x (f) represents the electroencephalogram signal with an electroencephalogram frequency f in the frequency domain, h (f) represents the system transfer function, and e (f) represents the noise signal with an electroencephalogram frequency f in the frequency domain.

And the second determining subunit is used for determining the strength of the directional information flow between the brain wave acquisition electrodes under the target noise strength based on the system transfer function of the functional relation.

Optionally, the preset noise intensity includes a zero-order noise intensity; the first determining subunit is specifically configured to: intercepting the target brain wave signal in the preprocessed brain wave signal by using a contrast audio, wherein the contrast audio is the audio with zero-order noise intensity noise added on the original audio.

Optionally, the directional information flow intensity between the brain wave collecting electrodes under the target noise intensity is expressed as F a × a matrixes DTF^q(f) Where a denotes the number of brain wave collecting electrodes, q denotes the target noise intensity, F denotes the brain wave frequency, and F denotes the total number of brain wave frequencies.

Optionally, the computing unit is specifically configured to:

respectively constructing weighted directed graphs among brain wave acquisition electrodes under each brain wave frequency based on directed information flow strength

representing weighted directed graphs

Computing weighted directed graph

Weighting degree of middle brain wave collecting electrode

And weighted directed graph

Weighting degree of middle brain wave collecting electrode

and representing the weighted degree of the brain wave collecting electrode j under the condition that the target noise intensity is q and the brain wave frequency is f.

Weighting degree of each brain wave collecting electrode

Corresponding degree of tape weight

Equation of utilization

Optionally, the second determining unit is specifically configured to:

and clustering the comprehensive difference coefficient vectors of all the noise intensities by using a preset clustering method to obtain the noise perception intensity of the testee to the original audio.

EXAMPLE III

Referring to fig. 16, an embodiment of the present invention provides an electronic device, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.

The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.

The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 16, but that does not indicate only one bus or one type of bus.

The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.

The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.

The method and the apparatus for measuring noise perception intensity and the computer program product of the electronic device provided by the embodiments of the present invention include a computer-readable storage medium storing a non-volatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

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, it need not be further defined and explained in subsequent figures.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.

Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.

In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring the perceived intensity of noise, comprising:

acquiring an original brain wave signal, wherein the original brain wave signal is a brain wave signal monitored by a testee when listening to a preset audio, the preset audio is a plurality of sections of audio with target noise intensity noise added on the original audio, and the target noise intensity is any one of preset noise intensities;

determining a noise perception intensity of the subject for the original audio based on the original brain wave signal;

wherein determining the noise perception intensity of the subject on the original audio based on the original brain wave signal comprises:

preprocessing the original brain wave signals to obtain preprocessed brain wave signals, wherein the preprocessed brain wave signals do not contain fluctuating signals caused by non-audio stimulation;

determining the directional information flow intensity between the brain wave acquisition electrodes under the target noise intensity based on the preprocessed brain wave signals;

calculating a comprehensive difference coefficient vector of the target noise intensity full frequency band based on the directed information flow intensity;

determining the noise perception intensity of the testee on the original audio based on the comprehensive difference coefficient vector of all the noise intensities;

wherein the directional information flow between the brain wave collecting electrodes is strong under the target noise intensityDegree is expressed as F a x a matrices DTF^q(f) Wherein a represents the number of brain wave collecting electrodes, q represents the target noise intensity, F represents the brain wave frequency, and F represents the total number of brain wave frequencies;

wherein, calculating the comprehensive difference coefficient vector of the target noise intensity full frequency band based on the directed information flow intensity comprises:

respectively constructing weighted directed graphs among the brain wave acquisition electrodes under each brain wave frequency based on the directed information flow strength

A→R⁺representing the weighted directed graph

Computing weighted directed graph

Weighting degree of middle brain wave collecting electrode

And weighted directed graph

Weighting degree of middle brain wave collecting electrode

representing the weighted degree of the brain wave collecting electrode j under the condition that the target noise intensity is q and the brain wave frequency is f;

weighting degree of each brain wave collecting electrode

Corresponding degree of tape weight

Equation of utilization

2. The method according to claim 1, wherein determining the directional information flow strength between the brain wave collecting electrodes at the target noise strength based on the preprocessed brain wave signals comprises:

determining a target brain wave signal based on the preprocessed brain wave signal, wherein the target brain wave signal is a brain wave signal monitored when the testee listens to an audio frequency added with target noise intensity noise on an original audio frequency;

converting the time domain corresponding relation of the target brain wave signal and the noise signal into the function relation of the target brain wave signal and the noise signal in the frequency domain, wherein the time domain corresponding relationIs shown as

A_dA d-th order AR model coefficient matrix representing a x a, p representing the order of the AR model, a representing the number of brain wave collecting electrodes, x_i＝(x_1,i,...,x_a,i) Representing brain wave signal vectors, e_i＝(e_1,i,...,e_a,i) Representing multivariate uncorrelated noise vectors, i represents sampling time points of the brain wave signals, and the functional relationship in the frequency domain is represented by x (f) ═ h (f) e (f), x (f) represents the brain wave signals with the brain wave frequency f in the frequency domain, h (f) represents the system transfer function, and e (f) represents the noise signals with the brain wave frequency f in the frequency domain;

and determining the directional information flow intensity between the brain wave acquisition electrodes under the target noise intensity based on the system transfer function of the functional relation.

3. The method according to claim 2, wherein the preset noise intensity comprises a zero-order noise intensity;

determining a target brain wave signal based on the preprocessed brain wave signals, including:

intercepting a target brain wave signal in the preprocessed brain wave signal by using a contrast audio, wherein the contrast audio is an audio obtained by adding zero-order noise intensity noise to an original audio.

4. The method of claim 1, wherein determining the noise perception strength of the subject on the original audio based on the integrated difference coefficient vector of all noise strengths comprises:

and clustering the comprehensive difference coefficient vectors of all the noise intensities by using a preset clustering method to obtain the noise perception intensity of the original audio frequency of the testee.

5. A device for measuring perceived intensity of noise, comprising:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an original brain wave signal, the original brain wave signal is a brain wave signal monitored by a testee when listening to preset audio, the preset audio is a multi-section audio with target noise intensity noise added on the original audio, and the target noise intensity is any one of preset noise intensities;

a determination module for determining the noise perception intensity of the subject on the original audio based on the original brain wave signal;

wherein the determining module comprises:

the preprocessing unit is used for preprocessing the original brain wave signals to obtain preprocessed brain wave signals, wherein the preprocessed brain wave signals do not contain fluctuating signals caused by non-audio stimulation;

the first determining unit is used for determining the directional information flow intensity between the brain wave collecting electrodes under the target noise intensity based on the preprocessed brain wave signals;

the calculation unit is used for calculating a comprehensive difference coefficient vector of the target noise intensity full frequency band based on the directed information flow intensity;

a second determining unit, configured to determine a noise perception strength of the original audio of the subject based on a comprehensive difference coefficient vector of all noise strengths;

wherein the intensity of the directional information flow between the brain wave collecting electrodes under the target noise intensity is expressed as F a x a matrixes DTF^q(f) Wherein a represents the number of brain wave collecting electrodes, q represents the target noise intensity, F represents the brain wave frequency, and F represents the total number of brain wave frequencies;

wherein the computing unit is specifically configured to:

Where, V { (1, 2.·, a } represents an electroencephalogram collecting electrode, and a { (i, j) | i, j }represents an electroencephalogram collecting electrodeE.1, a, and i ≠ j } represents a set of directed edges between any two brain wave acquisition electrodes,

A→R⁺representing the weighted directed graph

Computing weighted directed graph

Weighting degree of middle brain wave collecting electrode

And weighted directed graph

Weighting degree of middle brain wave collecting electrode

weighting degree of each brain wave collecting electrode

Corresponding degree of tape weight

Equation of utilization

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1 to 4 when executing the computer program.

7. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1 to 4.