Brain participation degree determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a brain participation degree determination method and device, electronic equipment and a storage medium. The method comprises the steps that electroencephalogram signals of a target object are collected; performing sliding window processing on the electroencephalogram signal of the target object to obtain an electroencephalogram signal after sliding window processing, performing wavelet packet decomposition on the electroencephalogram signal after sliding window processing to obtain wavelet coefficients corresponding to a plurality of electroencephalogram rhythms, and determining power of the plurality of electroencephalogram rhythms based on the wavelet coefficients corresponding to the plurality of electroencephalogram rhythms; determining a power characteristic based on the power of the plurality of electroencephalogram rhythms, and determining an initial brain participation degree based on the power characteristic; and correcting the initial brain participation degree to obtain a corrected brain participation degree. According to the technical scheme, the initial brain participation degree is determined and corrected, and the accuracy of the brain participation degree is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-computer interfaces, and in particular, to a method, device, electronic device, and storage medium for determining brain engagement. Background Art

[0002] Electroencephalogram (EEG) signals are spontaneous potential activities generated by brain nerve activities and always present in the central nervous system, containing rich brain activity information. They are an important means for brain science research, physiological research, and clinical diagnosis of brain diseases.

[0003] Brain engagement refers to the degree of mental concentration of a person in a real-time task participation state. Detecting brain engagement through EEG signals can evaluate the brain cognitive ability and mental concentration of the detected person, and has important application values in the fields of medical treatment, education, military, aviation, and driving.

[0004] In the process of implementing the present invention, it is found that there are at least the following technical problems in the prior art: in the existing brain engagement determination scheme, there is a problem of inaccurate brain engagement. Summary of the Invention

[0005] The present invention provides a method, device, electronic device, and storage medium for determining brain engagement to improve the accuracy of brain engagement.

[0006] According to one aspect of the present invention, there is provided a method for determining brain engagement, including:

[0007] Collecting EEG signals of a target object;

[0008] Performing a sliding window process on the EEG signals of the target object to obtain the EEG signals after the sliding window process, performing wavelet packet decomposition on the EEG signals after the sliding window process to obtain wavelet coefficients corresponding to multiple brain rhythms, and determining the powers of the multiple brain rhythms based on the wavelet coefficients corresponding to the multiple brain rhythms;

[0009] Determining a power feature based on the powers of the multiple brain rhythms, and determining an initial brain engagement based on the power feature;

[0010] Correcting the initial brain engagement to obtain the corrected brain engagement.

[0011] According to another aspect of the present invention, there is provided a device for determining brain engagement, including:

[0012] An EEG signal acquisition module for collecting EEG signals of a target object;

[0013] An electroencephalogram rhythm power determination module, configured to perform a sliding window process on the electroencephalogram signal of the target object to obtain the electroencephalogram signal after the sliding window process, perform wavelet packet decomposition on the electroencephalogram signal after the sliding window process to obtain wavelet coefficients corresponding to multiple electroencephalogram rhythms, and determine the power of the multiple electroencephalogram rhythms based on the wavelet coefficients corresponding to the multiple electroencephalogram rhythms;

[0014] An initial brain participation degree determination module, configured to determine a power feature based on the power of the multiple electroencephalogram rhythms, and determine an initial brain participation degree based on the power feature;

[0015] A brain participation degree correction module, configured to correct the initial brain participation degree to obtain a corrected brain participation degree.

[0016] According to another aspect of the present invention, there is provided an electronic device, where the electronic device includes:

[0017] At least one processor;

[0018] And a memory communicatively connected to the at least one processor;

[0019] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the brain participation degree determination method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, there is provided a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the brain participation degree determination method according to any embodiment of the present invention when executed by a processor.

[0021] The technical solution of the embodiment of the present invention collects the electroencephalogram signal of the target object, performs a sliding window process on the electroencephalogram signal of the target object to obtain the electroencephalogram signal after the sliding window process, performs wavelet packet decomposition on the electroencephalogram signal after the sliding window process to obtain wavelet coefficients corresponding to multiple electroencephalogram rhythms, determines the power of the multiple electroencephalogram rhythms based on the wavelet coefficients corresponding to the multiple electroencephalogram rhythms, determines a power feature based on the power of the multiple electroencephalogram rhythms, determines an initial brain participation degree based on the power feature, and then corrects the initial brain participation degree to obtain a corrected brain participation degree. The above technical solution realizes the determination and correction of the initial brain participation degree, and effectively improves the accuracy of the brain participation degree.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0024] Figure 1 is a flowchart of a method for determining brain engagement according to Embodiment 1 of the present invention;

[0025] Figure 2 is a flowchart of a method for determining brain engagement according to Embodiment 2 of the present invention;

[0026] Figure 3 is a flowchart of a method for determining brain engagement according to Embodiment 3 of the present invention;

[0027] Figure 4 is a flowchart of a method for determining brain engagement according to Embodiment 4 of the present invention;

[0028] Figure 5 is a flowchart of a method for determining brain engagement according to Embodiment 5 of the present invention;

[0029] Figure 6 is a schematic structural diagram of a device for determining brain engagement according to Embodiment 6 of the present invention;

[0030] Figure 7 is a schematic structural diagram of an electronic device for implementing the method for determining brain engagement in the embodiments of the present invention. Detailed Embodiments

[0031] To enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of the data in the technical solution of this application all comply with the relevant regulations of national laws and regulations.

[0033] Embodiment 1

[0034] Figure 1 FIG. is a flowchart of a method for determining brain engagement provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of adaptively calculating the brain engagement for a target object. This method can be executed by a brain engagement determination device, which can be implemented in the form of hardware and / or software. The brain engagement determination device can be configured in a terminal or other electronic devices capable of implementing a brain-computer interface. As Figure 1 shown, the method includes:

[0035] S110. Collect the electroencephalogram (EEG) signal of the target object.

[0036] In the embodiment of the present invention, the target object is a human or an animal, and no specific limitation is made here.

[0037] Exemplarily, the EEG signal of the scalp at least including the FP1 channel (located in the left front area of the scalp) can be collected through an EEG acquisition device.

[0038] S120. Perform a sliding window process on the EEG signal of the target object to obtain the EEG signal after the sliding window process, perform wavelet packet decomposition on the EEG signal after the sliding window process to obtain wavelet coefficients corresponding to multiple brain rhythms, and determine the power of multiple brain rhythms based on the wavelet coefficients corresponding to the multiple brain rhythms.

[0039] S130. Determine a power feature based on the power of the multiple brain rhythms, and determine an initial brain engagement based on the power feature.

[0040] Among them, the electroencephalogram rhythms may include, but are not limited to, δ (0.1 - 4 Hz), θ (4 - 8 Hz), α (8 - 15 Hz), β (15 - 30 Hz), etc. The power feature refers to a feature associated with the power of the electroencephalogram rhythm, or a parameter calculated based on the power of the electroencephalogram rhythm. For example, the power feature may be relative power or power ratio, etc.

[0041] In the embodiment of the present invention, through processes such as sliding window, wavelet packet decomposition, and power feature determination, the accurate calculation of the initial brain participation degree is achieved.

[0042] S140. Modify the initial brain participation degree to obtain the modified brain participation degree.

[0043] Specifically, a correction coefficient can be obtained based on the historical electroencephalogram signal of the target object or other body measurement data of the target object, and then the initial brain participation degree is corrected based on the correction coefficient to obtain the modified brain participation degree.

[0044] The technical solution of the embodiment of the present invention collects the electroencephalogram signal of the target object, performs sliding window processing on the electroencephalogram signal of the target object to obtain the electroencephalogram signal after sliding window, performs wavelet packet decomposition on the electroencephalogram signal after sliding window to obtain wavelet coefficients corresponding to multiple electroencephalogram rhythms, determines the power of multiple electroencephalogram rhythms based on the wavelet coefficients corresponding to multiple electroencephalogram rhythms, determines the power feature based on the power of multiple electroencephalogram rhythms, determines the initial brain participation degree based on the power feature, and then modifies the initial brain participation degree to obtain the modified brain participation degree. The above technical solution realizes the determination and modification of the initial brain participation degree, and effectively improves the accuracy of the brain participation degree.

[0045] Embodiment Two

[0046] Figure 2 It is a flowchart of a method for determining brain participation degree provided by the second embodiment of the present invention. The method in this embodiment can be combined with each optional solution in the method for determining brain participation degree provided in the above embodiment. The method for determining brain participation degree provided in this embodiment is further optimized. Optionally, any electroencephalogram rhythm corresponds to multiple wavelet coefficients; correspondingly, the determining the power of multiple electroencephalogram rhythms based on the wavelet coefficients corresponding to multiple electroencephalogram rhythms includes: for any electroencephalogram rhythm, determining the square of each wavelet coefficient, and superimposing the squares of each wavelet coefficient to obtain the power of the electroencephalogram rhythm.

[0047] As Figure 2 shown, the method includes:

[0048] S210. Collect the electroencephalogram signal of the target object.

[0049] S220. Perform a sliding window process on the EEG signal of the target object to obtain the EEG signal after the sliding window process. Perform wavelet packet decomposition on the EEG signal after the sliding window process to obtain wavelet coefficients corresponding to multiple EEG rhythms. Any one EEG rhythm corresponds to multiple wavelet coefficients.

[0050] S230. For any one EEG rhythm, determine the square of each wavelet coefficient, and superimpose the squares of each wavelet coefficient to obtain the power of the EEG rhythm.

[0051] Exemplarily, the power calculation formula of the EEG rhythm is as follows:

[0052]

[0053] Among them, E represents the power of δ, and d(k) represents the k-th wavelet coefficient in the frequency range of 0.1 - 4 Hz;

[0054]

[0055] Among them, E θ represents the power of θ, and d(k) represents the k-th wavelet coefficient in the frequency range of 4 - 8 Hz;

[0056]

[0057] Among them, E α represents the power of α, and d(k) represents the k-th wavelet coefficient in the frequency range of 8 - 15 Hz;

[0058]

[0059] Among them, E β represents the power of β, and d(k) represents the k-th wavelet coefficient in the frequency range of 15 - 30 Hz.

[0060] S240. Determine the power feature based on the powers of the multiple EEG rhythms, and determine the initial brain participation degree based on the power feature.

[0061] S250. Modify the initial brain participation degree to obtain the modified brain participation degree.

[0062] The technical solution of the embodiment of the present invention realizes the accurate calculation of the power of the EEG rhythm by, for any one EEG rhythm, determining the square of each wavelet coefficient and superimposing the squares of each wavelet coefficient.

[0063] Embodiment III

[0064] Figure 3 The flowchart of a method for determining brain engagement provided in Embodiment 3 of the present invention. The method of this embodiment can be combined with each optional solution in the method for determining brain engagement provided in the above embodiments. The method for determining brain engagement provided in this embodiment is further optimized. Optionally, the brain rhythms include δ, θ, α, and β; correspondingly, the determination of the power feature based on the powers of the multiple brain rhythms includes: determining the power feature based on the power of δ, the power of θ, the power of α, and the power of β.

[0065] As Figure 3 shown, the method includes:

[0066] S310. Collect the electroencephalogram (EEG) signals of the target object.

[0067] S320. Perform a sliding window process on the EEG signals of the target object to obtain the EEG signals after the sliding window process, and perform wavelet packet decomposition on the EEG signals after the sliding window process to obtain the wavelet coefficients corresponding to δ, the wavelet coefficients corresponding to θ, the wavelet coefficients corresponding to α, and the wavelet coefficients corresponding to β.

[0068] S330. Determine the power of δ based on the wavelet coefficients corresponding to δ, determine the power of theta based on the wavelet coefficients corresponding to θ, determine the power of α based on the wavelet coefficients corresponding to α, and determine the power of β based on the wavelet coefficients corresponding to β.

[0069] S340. Determine the power feature based on the power of δ, the power of θ, the power of α, and the power of β, and determine the initial brain engagement based on the power feature.

[0070] Exemplarily, the power feature can be calculated based on E δ , E θ , E α , and E β .

[0071] Optionally, the power characteristics include delta relative power, theta relative power, alpha relative power, beta relative power, a first power ratio, a second power ratio, a third power ratio, and a fourth power ratio; correspondingly, determining the power characteristics based on the power of delta, the power of theta, the power of alpha, and the power of beta includes: adding the power of delta, the power of theta, the power of alpha, and the power of beta to obtain the sum of electroencephalogram rhythm powers; determining the delta relative power based on the power of delta and the sum of electroencephalogram rhythm powers; determining the theta relative power based on the power of theta and the sum of electroencephalogram rhythm powers; determining the alpha relative power based on the power of alpha and the sum of electroencephalogram rhythm powers; determining the beta relative power based on the power of beta and the sum of electroencephalogram rhythm powers; determining the sum of delta-theta power based on the power of delta and the power of theta, determining the sum of alpha-beta power based on the power of alpha and the power of beta, and determining the first power ratio based on the sum of delta-theta power and the sum of alpha-beta power; determining the second power ratio based on the power of delta and the power of alpha; determining the third power ratio based on the power of theta and the power of beta; determining the fourth power ratio based on the power of theta and the sum of alpha-beta power; correspondingly, determining the initial brain engagement based on the power characteristics includes: determining the initial brain engagement based on the delta relative power, the theta relative power, the alpha relative power, the beta relative power, the first power ratio, the second power ratio, the third power ratio, and the fourth power ratio.

[0072] Exemplarily, the calculation formula of the power characteristics is as follows:

[0073]

[0074] where R 1 represents the delta relative power;

[0075]

[0076] where R 2 represents the theta relative power;

[0077]

[0078] where R 3 represents the alpha relative power;

[0079]

[0080] where R 4 represents the beta relative power;

[0081]

[0082] where R 5 represents the first power ratio;

[0083]

[0084] Among them, R 6 represents the second power ratio;

[0085]

[0086] Among them, R 7 represents the third power ratio;

[0087]

[0088] Among them, R 8 represents the fourth power ratio.

[0089] Furthermore, the formula for calculating the initial brain engagement is as follows:

[0090] B = ∑k i R i ;

[0091] Among them, R i represents the i-th power feature, and k i represents the weighting coefficient corresponding to the i-th power feature.

[0092] S350. Modify the initial brain engagement to obtain the modified brain engagement.

[0093] The technical solution of the embodiment of the present invention realizes the accurate calculation of the power feature by determining the power feature based on the power of δ, the power of θ, the power of α, and the power of β.

[0094] Embodiment 4

[0095] Figure 4 The flowchart of a method for determining brain engagement provided in Embodiment 4 of the present invention. The method in this embodiment can be combined with each optional solution in the method for determining brain engagement provided in the above embodiment. The method for determining brain engagement provided in this embodiment is further optimized. Optionally, the modifying the initial brain engagement to obtain the modified brain engagement includes: determining a first correction coefficient and a second correction coefficient; modifying the initial brain engagement based on the first correction coefficient and the second correction coefficient to obtain the modified brain engagement.

[0096] As Figure 4 shown, the method includes:

[0097] S410. Collect the electroencephalogram signal of the target object.

[0098] S420. Perform a sliding window process on the EEG signal of the target object to obtain the EEG signal after the sliding window process. Perform wavelet packet decomposition on the EEG signal after the sliding window process to obtain wavelet coefficients corresponding to multiple EEG rhythms. Determine the power of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms.

[0099] S430. Determine a power feature based on the power of the multiple EEG rhythms, and determine an initial brain participation degree based on the power feature.

[0100] S440. Determine a first correction coefficient and a second correction coefficient.

[0101] Among them, the first correction coefficient and the second correction coefficient are used to correct the initial brain participation degree. The first correction coefficient and the second correction coefficient can be obtained by user definition, or can be obtained based on the historical EEG signal of the target object or other body measurement data of the target object.

[0102] Optionally, determining the first correction coefficient and the second correction coefficient includes: determining the average value of the brain participation degree of the target object; determining a first brain participation degree threshold based on the average value of the brain participation degree of the target object and a first adjustment coefficient; determining a second brain participation degree threshold based on the average value of the brain participation degree of the target object and a second adjustment coefficient, where the first adjustment coefficient is greater than the second adjustment coefficient; determining the first correction coefficient and the second correction coefficient based on the first brain participation degree threshold and the second brain participation degree threshold.

[0103] Exemplarily, calculate the brain participation degree values of the target object in multiple time periods, perform an averaging process on the brain participation degree values of the target object in multiple time periods to obtain the average value of the brain participation degree of the target object. Further, the threshold calculation formula is as follows:

[0104]

[0105] Among them, Ba represents the first brain participation degree threshold, c1 represents the first adjustment coefficient, Bi represents the second brain participation degree threshold, c2 represents the second adjustment coefficient, represents the average value of the brain participation degree of the target object, and c1 > c2.

[0106] It should be noted that after the preset time for threshold calculation, the first brain participation degree threshold and the second brain participation degree threshold can be fixed so that the subsequent changes in the EEG signal of the target object will not continuously affect the setting of the threshold.

[0107] Further, the first brain participation threshold and the second brain participation threshold can be used to determine the first correction coefficient and the second correction coefficient. Exemplarily, the first brain participation threshold can be used as the first correction coefficient, and the second brain participation threshold can be used as the second correction coefficient; alternatively, mathematical operations can be performed on the first brain participation threshold and the second brain participation threshold to obtain the first correction coefficient and the second correction coefficient.

[0108] In some alternative embodiments, after a preset time for threshold calculation, if the brain participation values of the target object in multiple time periods are continuously greater than the first brain participation threshold, the first brain participation threshold and the second brain participation threshold are updated to make the thresholds match the current activity state of the target object's brain.

[0109] In some alternative embodiments, after a preset time for threshold calculation, if the brain participation values of the target object in multiple time periods are continuously less than 80% of the first brain participation threshold, it indicates that the current threshold does not match the target object, and the first brain participation threshold and the second brain participation threshold can be initialized.

[0110] S450. Based on the first correction coefficient and the second correction coefficient, correct the initial brain participation to obtain the corrected brain participation.

[0111] Specifically, multiply the first correction coefficient by the initial brain participation to obtain the brain participation corrected by the first correction coefficient; add the brain participation corrected by the first correction coefficient and the second correction coefficient to obtain the corrected brain participation.

[0112] Exemplarily, the calculation formula for the corrected brain participation is:

[0113] Be = a1 * B + a2;

[0114] Where, a1 represents the first correction coefficient, a2 represents the second correction coefficient, B represents the initial brain participation, and Be represents the corrected brain participation.

[0115] The technical solution of the embodiments of the present invention realizes the correction of the initial brain participation by determining the first correction coefficient and the second correction coefficient; and then correcting the initial brain participation based on the first correction coefficient and the second correction coefficient to obtain the corrected brain participation, effectively improving the accuracy of the brain participation.

[0116] Embodiment Five

[0117] Figure 5The flowchart of a method for determining brain engagement provided in the fifth embodiment of the present invention. The method in this embodiment can be combined with each alternative in the method for determining brain engagement provided in the above embodiments. The method for determining brain engagement provided in this embodiment is further optimized. Optionally, after collecting the electroencephalogram (EEG) signals of the target object, it further includes: performing a preprocessing operation on the collected EEG signals of the target object to obtain preprocessed EEG signals, where the preprocessing operation includes: removing power frequency interference from the EEG signals of the target object; and / or, denoising the EEG signals of the target object; correspondingly, performing a sliding window processing on the EEG signals of the target object to obtain sliding windowed EEG signals includes: performing a sliding window processing on the preprocessed EEG signals to obtain sliding windowed EEG signals.

[0118] As Figure 5 shown, the method includes:

[0119] S510. Collect the EEG signals of the target object.

[0120] S520. Perform a preprocessing operation on the collected EEG signals of the target object to obtain preprocessed EEG signals.

[0121] Among them, the preprocessing operation includes: removing power frequency interference from the EEG signals of the target object; and / or, denoising the EEG signals of the target object.

[0122] Exemplarily, the power frequency interference in the EEG signals can be removed by a notch filter; the EEG signals can be denoised based on ensemble empirical mode decomposition (EEMD) of independent component analysis (ICA) algorithm, so as to effectively separate the effective EEG signal components and reconstruct the EEG signal components, thereby obtaining clean preprocessed EEG signals.

[0123] S530. Perform a sliding window processing on the preprocessed EEG signals to obtain sliding windowed EEG signals, perform wavelet packet decomposition on the sliding windowed EEG signals to obtain wavelet coefficients corresponding to multiple brain rhythms, and determine the powers of multiple brain rhythms based on the wavelet coefficients corresponding to the multiple brain rhythms.

[0124] S540. Determine a power feature based on the powers of the multiple brain rhythms, and determine an initial brain engagement based on the power feature.

[0125] S550. Correct the initial brain engagement to obtain a corrected brain engagement.

[0126] The technical solution of the embodiment of the present invention effectively improves the quality of the electroencephalogram (EEG) signal by removing power frequency interference and denoising the collected EEG signal of the target object.

[0127] Embodiment Six

[0128] Figure 6 FIG. is a schematic structural diagram of an apparatus for determining brain participation provided in Embodiment Six of the present invention. As Figure 6 shown, the apparatus includes:

[0129] an EEG signal acquisition module 610, configured to acquire the EEG signal of the target object;

[0130] an EEG rhythm power determination module 620, configured to perform a sliding window process on the EEG signal of the target object to obtain the EEG signal after the sliding window process, perform wavelet packet decomposition on the EEG signal after the sliding window process to obtain wavelet coefficients corresponding to multiple EEG rhythms, and determine the powers of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms;

[0131] an initial brain participation determination module 630, configured to determine a power feature based on the powers of the multiple EEG rhythms, and determine an initial brain participation based on the power feature;

[0132] a brain participation correction module 640, configured to correct the initial brain participation to obtain a corrected brain participation.

[0133] The technical solution of the embodiment of the present invention acquires the EEG signal of the target object, performs a sliding window process on the EEG signal of the target object to obtain the EEG signal after the sliding window process, performs wavelet packet decomposition on the EEG signal after the sliding window process to obtain wavelet coefficients corresponding to multiple EEG rhythms, determines the powers of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms, determines a power feature based on the powers of the multiple EEG rhythms, determines an initial brain participation based on the power feature, and further corrects the initial brain participation to obtain a corrected brain participation. The above technical solution realizes the determination and correction of the initial brain participation, and effectively improves the accuracy of the brain participation.

[0134] In some optional embodiments, any EEG rhythm corresponds to multiple wavelet coefficients;

[0135] Correspondingly, the EEG rhythm power determination module 620 may specifically be configured to:

[0136] For any EEG rhythm, determine the square of each wavelet coefficient, and superimpose the squares of each wavelet coefficient to obtain the power of the EEG rhythm.

[0137] In some optional embodiments, the EEG rhythms include δ, θ, α, and β;

[0138] Correspondingly, the initial brain engagement determination module 630 includes:

[0139] A power feature determination unit, configured to determine a power feature based on the power of δ, the power of θ, the power of α, and the power of β.

[0140] In some alternative embodiments, the power feature includes a δ relative power, a θ relative power, an α relative power, a β relative power, a first power ratio, a second power ratio, a third power ratio, and a fourth power ratio;

[0141] Correspondingly, the power feature determination unit may specifically be configured to:

[0142] Add the power of δ, the power of θ, the power of α, and the power of β to obtain the sum of the electroencephalogram rhythm powers;

[0143] Determine the δ relative power based on the power of δ and the sum of the electroencephalogram rhythm powers;

[0144] Determine the θ relative power based on the power of θ and the sum of the electroencephalogram rhythm powers;

[0145] Determine the α relative power based on the power of α and the sum of the electroencephalogram rhythm powers;

[0146] Determine the β relative power based on the power of β and the sum of the electroencephalogram rhythm powers;

[0147] Determine the sum of the δ-θ powers based on the power of δ and the power of θ, determine the sum of the α-β powers based on the power of α and the power of β, and determine the first power ratio based on the sum of the δ-θ powers and the sum of the α-β powers;

[0148] Determine the second power ratio based on the power of δ and the power of α;

[0149] Determine the third power ratio based on the power of θ and the power of β;

[0150] Determine the fourth power ratio based on the power of θ and the sum of the α-β powers;

[0151] Correspondingly, the power feature determination unit and the initial brain engagement determination module 630 are further configured to:

[0152] Determine the initial brain engagement based on the δ relative power, the θ relative power, the α relative power, the β relative power, the first power ratio, the second power ratio, the third power ratio, and the fourth power ratio.

[0153] In some alternative embodiments, the brain engagement correction module 640 includes:

[0154] A correction factor determination unit for determining a first correction factor and a second correction factor;

[0155] An initial brain engagement correction unit for correcting the initial brain engagement based on the first correction factor and the second correction factor to obtain a corrected brain engagement.

[0156] In some alternative embodiments, the correction factor determination unit may specifically be configured to:

[0157] Determine the average value of the brain engagement of the target object;

[0158] Determine a first brain engagement threshold based on the average value of the brain engagement of the target object and a first adjustment factor;

[0159] Determine a second brain engagement threshold based on the average value of the brain engagement of the target object and a second adjustment factor, where the first adjustment factor is greater than the second adjustment factor;

[0160] Determine the first correction factor and the second correction factor based on the first brain engagement threshold and the second brain engagement threshold.

[0161] In some alternative embodiments, the initial brain engagement correction unit may specifically be configured to:

[0162] Multiply the first correction factor by the initial brain engagement to obtain a brain engagement corrected by the first correction factor;

[0163] Add the brain engagement corrected by the first correction factor and the second correction factor to obtain a corrected brain engagement.

[0164] In some alternative embodiments, the brain engagement determination device further includes:

[0165] A signal preprocessing module for preprocessing the electroencephalogram (EEG) signal collected from the target object to obtain a preprocessed EEG signal, where the preprocessing operation includes: removing power frequency interference from the EEG signal of the target object; and / or, denoising the EEG signal of the target object;

[0166] Correspondingly, the electroencephalogram rhythm power determination module 620 may further specifically be configured to:

[0167] Perform a sliding window process on the preprocessed EEG signal to obtain a EEG signal after the sliding window process.

[0168] The brain engagement determination device provided by the embodiments of the present invention may execute the brain engagement determination method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0169] Example VII

[0170] Figure 7 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0171] As Figure 7 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The I / O interface 15 is also connected to the bus 14.

[0172] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0173] The processor 11 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the brain engagement determination method, which includes:

[0174] Collect the electroencephalogram (EEG) signals of the target object;

[0175] Perform a sliding window process on the EEG signals of the target object to obtain the EEG signals after the sliding window process. Perform wavelet packet decomposition on the EEG signals after the sliding window process to obtain wavelet coefficients corresponding to multiple EEG rhythms. Determine the powers of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms;

[0176] Determine a power feature based on the powers of the multiple EEG rhythms, and determine an initial brain engagement based on the power feature;

[0177] Correct the initial brain engagement to obtain the corrected brain engagement.

[0178] In some embodiments, the brain engagement determination method can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the brain engagement determination method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the brain engagement determination method by any other suitable means (e.g., by means of firmware).

[0179] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0181] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0182] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0183] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected with each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0184] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0185] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0186] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining brain engagement, characterized in that: include: Collecting EEG signals of the target object; Performing sliding window processing on the EEG signal of the target object to obtain an EEG signal after sliding window, performing wavelet packet decomposition on the EEG signal after sliding window to obtain wavelet coefficients corresponding to multiple EEG rhythms, and determining the power of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms; determining a power feature based on the powers of the plurality of EEG rhythms, and determining an initial brain engagement degree based on the power feature; The initial brain engagement degree is corrected to obtain a corrected brain engagement degree.

2. The method according to claim 1, characterized in that Any EEG rhythm corresponds to multiple wavelet coefficients; Accordingly, determining the power of multiple EEG rhythms based on the wavelet coefficients corresponding to the multiple EEG rhythms includes: For any EEG rhythm, the square of each wavelet coefficient is determined, and the square of each wavelet coefficient is superimposed to obtain the power of the EEG rhythm.

3. The method according to claim 1, characterized in that The EEG rhythms include delta, theta, alpha and beta; Accordingly, the power characteristics are determined based on the powers of the multiple EEG rhythms, including: The power feature is determined based on the power of δ, the power of θ, the power of α, and the power of β.

4. The method according to claim 3, characterized in that The power characteristics include δ relative power, θ relative power, α relative power, β relative power, a first power ratio, a second power ratio, a third power ratio and a fourth power ratio; Accordingly, the power characteristics are determined based on the power of δ, the power of θ, the power of α, and the power of β, including: Add the power of δ, the power of θ, the power of α, and the power of β to get the sum of the EEG rhythm power; Determining the relative power of delta based on the sum of the power of delta and the power of the EEG rhythm; Determining theta relative power based on the sum of the theta power and the EEG rhythm power; Determining alpha relative power based on the sum of the alpha power and the EEG rhythm power; Determining the β relative power based on the sum of the β power and the EEG rhythm power; Determine a δ-θ power sum based on the δ power and the θ power, determine an α-β power sum based on the α power and the β power, and determine a first power ratio based on the δ-θ power sum and the α-β power sum; determining a second power ratio based on the power of the delta and the power of the alpha; determining a third power ratio based on the power of θ and the power of β; determining a fourth power ratio based on the power of θ and the sum of the α-β powers; Accordingly, determining the initial brain engagement degree based on the power feature includes: An initial brain engagement degree is determined based on the delta relative power, the theta relative power, the alpha relative power, the beta relative power, the first power ratio, the second power ratio, the third power ratio, and the fourth power ratio.

5. The method according to claim 1, characterized in that The step of correcting the initial brain engagement degree to obtain a corrected brain engagement degree includes: determining a first correction factor and a second correction factor; The initial brain engagement degree is corrected based on the first correction coefficient and the second correction coefficient to obtain a corrected brain engagement degree.

6. The method according to claim 5, characterized in that The determining of the first correction coefficient and the second correction coefficient comprises: Determine the mean brain engagement score of the target subject; Determining a first brain engagement threshold based on the target object's brain engagement mean and a first adjustment coefficient; Determining a second brain engagement threshold based on the target object's mean brain engagement value and a second adjustment coefficient, wherein the first adjustment coefficient is greater than the second adjustment coefficient; A first correction factor and a second correction factor are determined based on the first brain engagement threshold and the second brain engagement threshold.

7. The method according to claim 5, characterized in that The step of correcting the initial brain engagement degree based on the first correction coefficient and the second correction coefficient to obtain a corrected brain engagement degree includes: multiplying the first correction coefficient by the initial brain engagement degree to obtain the brain engagement degree corrected by the first correction coefficient; The brain engagement degree corrected by the first correction coefficient and the second correction coefficient are added to obtain a corrected brain engagement degree.

8. A device for determining brain engagement, characterized in that: include: An EEG signal acquisition module, used to acquire EEG signals of a target object; an EEG rhythm power determination module, configured to perform sliding window processing on the EEG signal of the target object to obtain an EEG signal after sliding window processing, perform wavelet packet decomposition on the EEG signal after sliding window processing to obtain wavelet coefficients corresponding to a plurality of EEG rhythms, and determine the power of a plurality of EEG rhythms based on the wavelet coefficients corresponding to the plurality of EEG rhythms; an initial brain engagement determination module, configured to determine a power feature based on the powers of the plurality of EEG rhythms, and determine an initial brain engagement based on the power feature; The brain engagement correction module is used to correct the initial brain engagement to obtain a corrected brain engagement.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the brain engagement determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the brain engagement determination method according to any one of claims 1 to 7 when executed.

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