A method, device and storage medium for identifying drug addicts

Abstract

The present invention discloses a method, device and storage medium for identifying drug addicts, belonging to the field of identifying drug addicts. The method includes: acquiring electroencephalogram (EEG) data; preprocessing the EEG data to obtain EEG signals; using differential entropy to extract dynamic features of the EEG signals to obtain EEG signal features; inputting the obtained EEG signal features into incremental width learning for training, and verifying the results through ten-fold cross-validation. By introducing differential entropy (DE) as the feature representation of EEG signals and combining incremental width learning as a classifier, the present invention can achieve fast adaptation and efficient training, perform excellently in dealing with high-dimensional data and non-linear classification problems, avoid the high computational resource consumption caused by a large number of parameter trainings in deep learning, can realize edge computing deployment, and can effectively classify and identify EEG signals.

Description

Technical Field

[0001] The present invention relates to the field of drug addict identification, and in particular to a drug addict identification method, device and storage medium. Background Art

[0002] In the field of drug control and drug rehabilitation law enforcement, the methods for evaluating drug use or addiction mainly rely on behavioral observation, questionnaires, and biological sample testing. Although behavioral observation and questionnaires can obtain self-reports and behavioral performance of subjects to a certain extent, the subjective attitudes and memory biases of the parties involved often affect the objectivity and accuracy of the evaluation results. Biological sample testing can detect biomarkers of drug use behavior, but it takes a lot of time and economic costs, and it is easy for drug users to use deceptive means to evade detection. Therefore, it is necessary to use advanced scientific and technological means and data-driven methods, such as EEG signal analysis, to achieve accurate, objective and dynamic monitoring of drug use behavior and addiction treatment effects.

[0003] Compared with other current EEG-based identification methods, they are too expensive, target a single type of drug, and have complex data preprocessing, making them difficult to use in practice. In addition, the practice of directly inputting EEG signals into the model causes the model to learn other information and noise in the EEG signals, ignoring the characteristics of dynamic changes in EEG signals, resulting in low classification accuracy. In addition, the wet electrode EEG caps used in most methods are not portable, are complicated to wear, and have high equipment costs, making them difficult to deploy in actual testing, which in turn increases the workload.

[0004] At present, with the emergence of new types of drugs, most model samples trained based on EEG data have fewer types of drugs and are unable to learn the characteristics of new drugs. Therefore, in actual large-scale screening, as the number of drug types and sample size increases, the model accuracy will decrease. To solve this problem, it is necessary to re-collect samples and train the model. The training time is long and it is difficult to deploy in actual large-scale testing. Summary of the invention

[0005] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a method, device and storage medium for identifying drug addicts.

[0006] The first technical solution adopted by the present invention is:

[0007] A method for identifying a drug addict comprises the following steps:

[0008] Acquiring EEG data; the EEG data includes EEG data of healthy persons and EEG data of drug addicts;

[0009] Preprocessing the EEG data to obtain EEG signals;

[0010] The differential entropy is used to extract the dynamic features of the EEG signal to obtain the EEG signal features;

[0011] The obtained EEG signal features are input into incremental width learning for training, and the results are verified by ten-fold cross-validation.

[0012] Further, the preprocessing includes at least one of data filtering processing, data segmentation processing, baseline correction processing, or artifact and motion artifact removal processing.

[0013] Further, the preprocessing of the EEG data to obtain the EEG signal includes:

[0014] The 0.5 - 45 hz filtering technique is used to remove the noise and unwanted frequency components in the EEG data, and the filtered EEG data is segmented into time periods of a preset time length.

[0015] Further, the use of differential entropy to extract the dynamic features of the EEG signal to obtain the EEG signal features includes:

[0016] For the EEG signal of each time period, calculate its first-order difference sequence;

[0017] Estimate the probability density function of the first-order difference sequence;

[0018] Using the estimated probability density function, calculate the differential entropy;

[0019] After calculating the differential entropy value of each time period, use the differential entropy value as the EEG signal feature.

[0020] Further, the first-order difference sequence is calculated by the difference between the numerical values of the EEG signals at adjacent time points;

[0021] The probability density function is estimated by using the histogram method or the kernel density estimation method.

[0022] Further, the calculation formula of the differential entropy is as follows:

[0023]

[0024] Among them, p(x) represents the probability density function of the signal, and x represents the value of the difference signal.

[0025] Further, the input of the obtained EEG signal features into incremental width learning for training includes:

[0026] The learning process of width learning:

[0027] Input the feature data X, which first forms the mapped feature nodes Z through random mapping n ≡ [Z1, Z2, …, Z n ; where the i-th group of mapped features Z i is calculated as shown in formula (1):

[0028]

[0029] In the formula, is the linear mapping function, W ei is the random weight, β ei is the random bias, and n is the number of groups of mapped features;

[0030] The obtained mapped feature nodes Z n pass through the non-linear mapping function to form the enhanced feature nodes H m ≡ [H1, H2, …, H m ; The calculation of the j-th enhanced feature H j is shown in formula (2):

[0031] H j ≡ ζ(Z n W hj + β hj ), j = 1, 2, …, m (2)

[0032] In the formula, W hj , β hj are the connection weights, m is the number of enhanced nodes, and ζ(Z, W hj , β hj ) is a non-linear activation function;

[0033] Merge the two obtained features to form the new addiction EEG recognition feature A = [Z|H], and connect it to the model output layer Y;

[0034] Since it is known whether each sample has taken drugs, only the connection weight W between the feature layer and the output layer needs to be calculated. The calculation formula is as follows:

[0035] W = A -1 Y (3)

[0036] During the calculation, ridge regression is used to solve the weight matrix, that is, solve W through the optimization problem in formula (4):

[0037]

[0038] Among them, σ1 = σ2 = v = u = 2, and the solution is:

[0039] W = (λI + AA T ) -1 A TY (5)

[0040] Where Y is the true output, I is the identity matrix, λ is the optimization coefficient, and T represents the transpose;

[0041] The prediction output of the width learning is:

[0042]

[0043] The learning process of the data incremental width learning:

[0044] For the newly added input data {X a , Y a}, calculate its corresponding incremental mapping feature and enhanced node according to formulas (7) and (8) respectively:

[0045]

[0046]

[0047] Where represents the mapping feature node, represents the enhanced feature node; is the mapping feature node after increment;

[0048] Then the new feature layer is:

[0049]

[0050] Where is the non-incremental feature layer;

[0051] The output layer weight is updated by the following formula:

[0052]

[0053] Where is the label corresponding to the data Ba; T represents the transpose; represents the non-incremental feature layer; represents the non-incremental output layer weight.

[0054] Furthermore, the calculation formula for the confidence corresponding to the width learning prediction output Y is as follows:

[0055]

[0056] Where represents the output of the sparse width liveness recognition network, represents the output corresponding to the sparse width liveness recognition network judging that the image is of the jth category.

[0057] The second technical solution adopted by the present invention is as follows:

[0058] A device for identifying drug addicts, comprising:

[0059] At least one processor;

[0060] At least one memory for storing at least one program;

[0061] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0062] The third technical solution adopted by the present invention is as follows:

[0063] A computer-readable storage medium storing a program executable by a processor, the program executable by the processor being used to execute the method as described above when executed by the processor.

[0064] The beneficial effects of the present invention are as follows: By introducing differential entropy (DE) as the feature representation of EEG signals and combining incremental width learning as a classifier, the present invention can achieve fast adaptation and efficient training, perform excellently in dealing with high-dimensional data and non-linear classification problems, avoid the high computational resource consumption caused by a large number of parameter trainings in deep learning, can implement edge computing deployment, and can effectively classify and identify EEG signals. Description of the Drawings

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the drawings introduced below only conveniently and clearly illustrate some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0066] Figure 1 It is a flowchart of the steps of a method for identifying drug addicts in an embodiment of the present invention;

[0067] Figure 2 It is a schematic diagram of identification by training a detection model in a traditional evaluation scheme. Detailed Embodiments

[0068] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0069] In the description of the present invention, it should be understood that with respect to the orientation description, such as the orientations or positional relationships indicated by up, down, front, rear, left, right, etc., are based on the orientations or positional relationships shown in the accompanying drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as limiting the present invention.

[0070] In the description of the present invention, the meaning of "a number of" is one or more, the meaning of "a plurality of" is two or more. Understandings such as "greater than", "less than", "exceeding", etc. do not include the recited number, and understandings such as "above", "below", "within", etc. include the recited number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features. In addition, "and / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0071] In the description of the present invention, unless otherwise clearly defined, words such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0072] See Figure 2, the subjectivity and inefficiency of traditional evaluation methods still cannot meet the actual needs, resulting in inaccurate evaluation and low efficiency. Moreover, current EEG-based methods often target individual drug types, lack universality, and require a large number of preprocessing means to ensure the effectiveness of features. To address this technical problem, the present invention proposes a method for identifying multiple drug addicts based on EEG, differential entropy, and incremental width learning. From the data aspect, it covers multiple drug types. To enhance the model's feature extraction of EEG signals and multiple drugs, differential entropy (DE) is introduced as a feature representation of EEG signals. Differential entropy is an effective feature representation method that can capture the dynamic features and complexity in EEG signals, and has good discrimination ability and robustness. Combined with incremental width learning as a classifier, and incremental width learning is an efficient machine learning method that learns and updates by gradually adding new data to the existing model. Different from traditional deep learning methods, incremental width learning does not require retraining the entire model, but updates parameters incrementally to achieve fast adaptation and efficient training. It performs well in dealing with high-dimensional data and nonlinear classification problems. And incremental width learning avoids the high computational resource consumption caused by the training of a large number of parameters in deep learning by adding data and parameters layer by layer, and can achieve edge computing deployment, and can effectively classify and identify EEG signals. Therefore, it attempts to improve the current method for identifying drug addicts from three aspects: efficiency, accuracy, and practicality.

[0073] The present invention can be applied in front-line anti-drug law enforcement units to test potential drug users and play a crucial role in scenarios such as anti-drug law enforcement and compulsory isolation for drug rehabilitation. The addiction assessment technology based on EEG data penetrates into various related applications due to its advantages such as objectivity, speed, and efficiency. For example, it can quickly identify the addiction status of drug-related personnel in anti-drug law enforcement to improve law enforcement efficiency; analyze the impact of drugs on the brain in psychological research to provide research data support; and assist doctors in accurate diagnosis in medical diagnosis.

[0074] As Figure 1 shown, this embodiment provides a method for identifying drug addicts, including the following steps:

[0075] A method for identifying drug addicts, including the following steps:

[0076] S1. Obtain EEG data. The EEG data includes the EEG data of healthy people and the EEG data of drug addicts.

[0077] S2. Preprocess the EEG data to obtain EEG signals.

[0078] EEG data preprocessing is a crucial step in EEG signal analysis, aiming to extract clean and accurate signals for subsequent analysis. Specifically, this process typically includes steps such as importing raw data, data filtering, data segmentation, baseline correction, and artifact and motion artifact removal.

[0079] In some embodiments, first, the raw EEG data is loaded into memory by selecting an appropriate reading method. Then, a 0.5 - 45 hz filtering technique is used to remove noise and unwanted frequency components from the signal, and then the data is segmented into 2 - second time periods. Subsequently, baseline correction is performed to eliminate possible DC offsets, and corresponding algorithms or methods are used to remove artifacts and motion artifacts. Through these steps, high - quality and accurate EEG signals can be obtained, providing a reliable data basis for subsequent signal analysis and feature extraction.

[0080] S3. Use differential entropy to extract dynamic features of the EEG signal to obtain EEG signal features.

[0081] Differential entropy is a statistic used to describe the complexity and information content of a signal, and is commonly used in the analysis and feature extraction of EEG signals. Differential entropy reflects the uncertainty and the degree of change of the signal over time, and is an effective EEG signal feature. In some embodiments, the calculation of differential entropy includes the following steps:

[0082] S31. Data segmentation: First, the EEG signal is divided into several equal - length time periods for the calculation of differential entropy. Given that the data has been segmented in step S2, this step can be omitted in step S3.

[0083] S32. Signal differencing: For the EEG signal of each time period, calculate its first - order difference sequence. As an alternative implementation, the difference sequence can be calculated by the difference between the EEG signal values at adjacent time points.

[0084] S33. Estimate the probability density function: Based on the difference sequence of the signal, estimate its probability density function. In some alternative implementations, methods such as the histogram method or kernel density estimation are usually used to estimate the probability density function.

[0085] S34. Calculate differential entropy: Using the estimated probability density function, apply the definition formula of differential entropy for calculation. The definition of differential entropy is as follows:

[0086]

[0087] where \(p(x)\) represents the probability density function of the signal, and \(x\) represents the value of the difference signal. The differential entropy \(DE\) represents the information content and complexity of the signal difference sequence. The larger the value, the more complex the signal and the richer the information content.

[0088] S35. Feature extraction: After calculating the differential entropy value for each time period, it is used as one of the features of the EEG signal. As an alternative implementation, it is usually analyzed and modeled in combination with other features to identify different EEG patterns or judge the state of the EEG signal.

[0089] The application of differential entropy in EEG signal analysis is based on the time-domain characteristics and dynamic characteristics of the EEG signal. The EEG signal is an electrophysiological signal of brain neuron activities, which contains rich information, such as the spatio-temporal distribution of brain activities, frequency characteristics, and temporal changes. As a statistic reflecting the complexity and information content of the signal, differential entropy can reveal the dynamic change characteristics of the EEG signal from the temporal sequence perspective, so it is closely related to the analysis of the EEG signal.

[0090] In EEG signal analysis, differential entropy is often used to study the complexity and uncertainty of the EEG signal. The EEG signal has different dynamic characteristics in different brain regions and different physiological states. For example, in different cognitive tasks, different emotional states, or different neurological diseases, the complexity and information content of the EEG signal will change, thus providing important clues for understanding the spatio-temporal dynamics of brain activities.

[0091] In addition, differential entropy can also be used to compare the complexity and information content between different EEG signals. For example, by comparing the differential entropy values of EEG signals between the control group and the experimental group, differences in brain activities under different cognitive tasks or different neurological disease states can be found. Therefore, as a flexible and effective feature extraction method, differential entropy has broad application prospects in EEG signal analysis and neuroscience research.

[0092] S4. Input the obtained EEG signal features into incremental width learning for training, and verify the results through ten-fold cross-validation.

[0093] Input the data into incremental width learning for training. Finally, verify the results through ten-fold cross-validation. The verification metrics mainly include precision, specificity, sensitivity, and ROC / AUC. The incremental width learning system specifically includes the following:

[0094] 1) The learning process of width learning:

[0095] Input the feature data X, which first forms the mapped feature nodes Z n ≡[Z1, Z2, …, Z n ; where the calculation of the i-th group of mapped features Z i is shown in formula (1), and each group of features contains k mapped features:

[0096]

[0097] From the obtained mapped feature nodes Z n Through a non - linear mapping function, enhanced feature nodes H are formed m ≡[H1, H2, …, H m ; The calculation of the j - th enhanced feature H j is shown in formula (2):

[0098] H j ≡ζ(Z n W hj +β hj ), j = 1, 2, …, m (2)

[0099] Merge the two obtained features to form a new addiction EEG recognition feature A = [Z|H], and connect it to the model output layer Y;

[0100] Since it is known whether each sample has taken drugs, only the connection weight W between the feature layer and the output layer needs to be calculated, and the calculation formula is as follows:

[0101] W = A -1 Y (3)

[0102] During the calculation, ridge regression is used to solve the weight matrix, that is, solve W through the optimization problem in formula (4):

[0103]

[0104] Among them, σ1 = σ2 = v = u = 2, and the solution is:

[0105] W = (λI + AA T ) -1 A T Y (5)

[0106] The width learning prediction output is:

[0107]

[0108] The confidence corresponding to this output is obtained from formula (7), specifically:

[0109]

[0110] The learning process of data - incremental width learning:

[0111] For the newly added input data {X a , Y a}, calculate its corresponding incremental mapped features and enhanced nodes according to formulas (8) and (9) respectively.

[0112]

[0113]

[0114] Then the new feature layer is as follows:

[0115]

[0116] Output layer weights It is updated by the following formula:

[0117]

[0118] where is the label corresponding to data Ba;

[0119] The above method will be described in detail below in combination with experimental tests.

[0120] Aiming at the problems of high cost, complex collection and inability to conduct large-scale screening existing in the current traditional drug detection methods, and aiming at the problems of poor universality and inability to be put into practical application caused by the single drug and complex preprocessing process in the current electroencephalogram (EEG)-based detection methods, and aiming at the problem that the EEG data sample size used during training is small and cannot cover new drugs and other types of people, this embodiment is based on EEG data combined with differential entropy feature extraction and incremental width learning, which significantly improves the accuracy and real-time performance of detecting multiple drug addicts. Moreover, when new drug users or new samples appear, new features can be learned through incremental learning, which is applicable to law enforcement units for large-scale screening. The information of addicted and healthy people in the samples is shown in Tables 1 and 2 as follows:

[0121] Table 1

[0122] Number of people Age (years) Male Female Addicted persons 80 37.6(±5.1) 48 32 Healthy controls 70 30.6(±7.3) 36 34

[0123] Table 2

[0124]

[0125] Differential entropy features can effectively capture the dynamic changes of EEG signals and provide high-resolution time-frequency domain analysis. In an actual drug rehabilitation center, in the tests covering various drug types, the accuracy rate of the evaluation model in identifying addicted individuals reached 96.429%, as shown in Table 3. This method solves the problems of complex collection and low efficiency existing in traditional evaluation methods, and also solves the problems of complex preprocessing and poor universality caused by single drug in the current EEG-based identification methods.

[0126] Table 3

[0127] Accuracy Incremental width learning 96.429％

[0128] This embodiment also provides a device for identifying drug addicts, including:

[0129] At least one processor;

[0130] At least one memory for storing at least one program;

[0131] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0132] A drug addiction personnel recognition device according to this embodiment can execute a drug addiction personnel recognition method provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has corresponding functions and beneficial effects of the method.

[0133] An embodiment of the present application also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0134] This embodiment also provides a storage medium, which stores instructions or programs that can execute a drug addiction personnel recognition method provided by an embodiment of the method of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method are provided.

[0135] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown can actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0136] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features described may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of such modules would be understood within the ordinary skill of an engineer. Thus, those of ordinary skill in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the particular concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0137] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0139] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0140] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0141] In the foregoing description of this specification, the descriptions referring to the terms "one embodiment / Example", "another embodiment / Example", or "certain embodiments / Examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0142] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0143] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for identifying drug addicts, characterized in that, Including the following steps: Obtain electroencephalogram (EEG) data; Preprocess the EEG data to obtain EEG signals; Extract dynamic features of the EEG signals using differential entropy to obtain EEG signal features; Input the obtained EEG signal features into incremental width learning for training, and verify the results through ten-fold cross-validation; The extracting dynamic features of the EEG signals using differential entropy to obtain EEG signal features includes: For the EEG signals in each time period, calculate its first-order difference sequence; the first-order difference sequence is calculated by the difference between the values of the EEG signals at adjacent time points; Estimate the probability density function of the first-order difference sequence; the probability density function is estimated by using the histogram method or the kernel density estimation method; Calculate the differential entropy using the estimated probability density function; After calculating the differential entropy values for each time period, use the differential entropy values as EEG signal features; The calculation formula of the differential entropy is as follows: where p(x) represents the probability density function of the signal, and x represents the value of the difference signal; The inputting the obtained EEG signal features into incremental width learning for training includes: The learning process of width learning: Input the feature data X, which first undergoes random mapping to form the mapped feature nodes Z n ≡[Z1, Z2, …, Z n ; where the calculation of the i-th group of mapped features Z i is shown in Equation (1): In the formula, is a linear mapping function, W ei is a random weight, β ei is a random bias, and n is the number of groups of mapped features; From the obtained mapped feature nodes Z n Through a non-linear mapping function, enhanced feature nodes H are formed m ≡ [H1, H2, …, H m ; The j-th enhanced feature H j is calculated as shown in Equation (2): H j ≡ζ(Z n W hj +β hj ), j = 1, 2, …, m (Equation (2)) where W hj , β hj are connection weights, m is the number of enhancement nodes, and ζ(Z, W hj , β hj ) is a non-linear activation function; Combine the two obtained features to form a new addictive EEG recognition feature A = [Z|H], and connect it to the model output layer Y; since it is known whether each sample has taken drugs, only the connection weight W between the feature layer and the output layer needs to be calculated, and the calculation formula is as follows: W = A -1 When calculating Y(3), ridge regression is used to solve the weight matrix, that is, W is solved through the optimization problem in formula (4): where σ1 = σ2 = v = u = 2, and the solution is: W = (λI + AA T ) -1 A T Y(5) In the formula, Y is the true output, I is the identity matrix, λ is the optimization coefficient, and T represents transpose; The width learning prediction output is: The learning process of data incremental width learning: For the newly added input data {X a ,Y a}, calculate their corresponding incremental mapping features and enhanced nodes respectively according to formulas (7) and (8): Then the new feature layer is: Output layer weights Updated by the following formula: Among them, is the label corresponding to data Ba; The calculation formula of the confidence corresponding to the width learning prediction output Y is as follows: Wherein, represents the output of the sparse-width liveness recognition network, represents the output corresponding to the sparse-width liveness recognition network determining that the image is of the j-th category.

2. The method for identifying drug addicts according to claim 1, wherein The preprocessing includes at least one of data filtering processing, data segmentation processing, baseline correction processing, or artifact and motion artifact removal processing.

3. The method for identifying drug addicts according to claim 1, wherein The preprocessing the EEG data to obtain EEG signals includes: Use a 0.5 - 45 hz filtering technique to remove noise and unwanted frequency components from the EEG data, and segment the filtered EEG data into time periods of a preset time length.

4. A drug addiction personnel recognition device, characterized in that, Including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 - 3.

5. A computer-readable storage medium storing a program executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to execute the method according to any one of claims 1 - 3.

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