CN112617824B - Ice drug addict detection system based on multichannel fNIRS signals - Google Patents
Ice drug addict detection system based on multichannel fNIRS signals Download PDFInfo
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- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Abstract
The invention relates to an ice drug addict detection system based on a multichannel fNIRS signal, which is characterized in that the fNIRS signal of a tester is extracted in real time through a multichannel fNIRS channel to obtain and store the multichannel fNIRS signal of the tester; after preprocessing operation is carried out on each channel fNIRS signal, the value of a directed transfer function between each channel fNIRS signal is calculated, a directed transfer function matrix of a tester is generated, and the directed transfer function matrix of the tester is used as input and is sent into a classifier, so that a detection result is obtained. The scheme is based on cranial nerve signals, overcomes the defects of the traditional ice toxin detection, provides a novel method for the ice toxin detection, greatly prolongs the effective time of the detection, and finally improves the detection accuracy rate through testing.
Description
Technical Field
The invention relates to the technical field of feature detection, in particular to an ice drug addict detection system based on a multichannel fNIRS signal.
Background
The ice toxin enters China in the 90 s, and the detection difficulty of drug addicts is very high due to less cognition on the ice toxin and lack of effective detection means.
The main component of methamphetamine, also known as methamphetamine, is also called methamphetamine as the high-purity methamphetamine is a transparent crystal which is very similar to crystal sugar. Excessive drinking of methamphetamine can not only cause strong dependence, but also cause symptoms such as methamphetamine psychosis and schizophrenia. Most of the existing detection methods for methamphetamine are urine and blood detection. The traditional urine and blood detection method mainly judges whether a person takes poison or not by detecting components in human urine or blood, but cannot detect whether the person takes poison or not by recovering the components in the urine and the blood to be normal after a period of time through normal metabolism of a human body. Therefore, the traditional method can only detect people who have recently taken the ice toxin, but is difficult to accurately detect people who have not been exposed to the ice toxin for a long time.
Disclosure of Invention
The invention provides a method and a system for detecting an ice drug addict based on multichannel fNIRS signals, which aims at solving the technical problems in the prior art, and provides a method and a system for detecting the ice drug addict based on the multichannel fNIRS signals.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the invention provides a method for detecting an addiction to an ice drug based on a multichannel fNIRS signal, comprising the steps of:
extracting the fNIRS signals of the testee in real time through a multi-channel fNIRS channel to obtain and store the multi-channel fNIRS signals of the testee;
after preprocessing operation is carried out on each channel fNIRS signal, the value of a directed transfer function between each channel fNIRS signal is calculated to generate a directed transfer function matrix of a tester,
and sending the directional transfer function matrix of the tester into a classifier as input to obtain a detection result.
Further, the preprocessing operations include filtering, segmentation, baseline correction, and superposition averaging.
Further, the preprocessing operation specifically includes:
filtering the fNIRS signals, wherein the filtering parameters are respectively set to be 0.03-0.1Hz band-pass filtering; after filtering, segmenting fNIRS data from 1S before stimulation to 15S after stimulation as an epoch, and calling the epoch as an S2 stimulation response; baseline correction was performed with the pre-stimulation 1s data as baseline, followed by a mean-add for every 5 epochs in the data.
Further, the calculation method of the values of the directional transfer functions between the fNIRS signals of the channels is as follows:
for a given time lag r, consider a p-order multivariate autoregressive process of dimension M, i.e. measuring M signals x simultaneously1(t)，x2(t)......xM(t), then the time domain representation of the fNIRS signal:
wherein A isrIs a matrix of coefficients of order M x M, and ε (t) is independent white Gaussian noise with a covariance matrix, M is the number of fNIRS channels;
the frequency domain representation of the fNIRS signal can be obtained by computing a power spectral density matrix:
S(f)＝H(f)∑HH(f) (2)
wherein (.)HDenotes the Hermitian transpose, H (f) is the transfer function matrix, f denotes the frequency;
obtained according to equations (1) and (2):
h (f) a column vector representing the transfer function matrix H (f);
the value of the directed transfer function DTF for the j-channel fnIRS signal to the i-channel fnIRS signal is:
in the formula (3), Hij(f) Is an element of row i and column j of H (f), hj(f) The j-th column vector representing H (f).
Further, before testing the tester, the method further includes training the classifier, and the training process of the classifier includes:
extracting fNIRS signals of addicts and healthy subjects in real time through a multi-channel fNIRS channel respectively to obtain multi-channel fNIRS signals of the two subjects respectively and storing the multi-channel fNIRS signals;
sequentially preprocessing each channel fNIRS signal of the two types of subjects to obtain fNIRS signals corresponding to each channel addiction stimulation of the two types of subjects;
respectively calculating values of the directed transfer functions between the fNIRS signals of the channels of the two types of subjects to generate two groups of directed transfer function matrixes corresponding to the two types of subjects;
calculating average directed transfer function matrixes corresponding to the two types of subjects, carrying out t-test statistical test on the average value of the two groups of directed transfer functions of each connected pair, and using Bonferroni multiple correction to obtain adjacent edges corresponding to the average values of the two groups of directed transfer functions with significant differences;
and constructing a characteristic vector as sample data by using directed transfer function values of adjacent edges with significance difference of the two types of subjects, and performing K-fold cross validation on the initial machine learning model to obtain the classifier with the optimal parameter combination.
Further, the K-fold cross validation comprises: in each compromise of cross validation, K-1 sample data of the addictive group and K-1 sample data of the healthy group were used in the training set, and the remaining 1 sample data of the addictive group and 1 sample data of the healthy group were used in the test set.
Further, the classifier adopts a Fisher classifier.
In a second aspect, the invention provides a system for detecting an addiction to an ice drug based on a multichannel fNIRS signal, comprising:
the signal extraction module is used for extracting the fNIRS signal of the tester in real time through the multi-channel fNIRS channel to obtain and store the multi-channel fNIRS signal of the tester;
the preprocessing and matrix generating module is used for calculating the value of the directed transfer function between the fNIRS signals of each channel after preprocessing the fNIRS signals of each channel to generate a directed transfer function matrix of a tester,
and the classification detection module is used for sending the directed transfer function matrix of the tester into a classifier as input to obtain a detection result.
In a third aspect, the present invention provides an electronic device comprising:
a memory for storing a computer software program;
a processor for reading and executing the computer software program stored in the memory, thereby implementing the method for detecting an icy drug addict based on a multichannel fNIRS signal according to the first aspect of the present invention.
In a fourth aspect, a non-transitory computer readable storage medium having stored thereon a method for performing a method for detecting an addictive drug addict based on a multichannel fNIRS signal according to the first aspect of the invention.
The invention has the beneficial effects that:
first, the fNIRS (functional near infrared spectroscopy) utilizes the good scattering property of the main components of blood to the near infrared light of 600-900nm, so as to obtain the change conditions of oxyhemoglobin and deoxyhemoglobin during brain activities. No case of using the fNIRS technology to detect the drug addiction of the ice-poison exists in the prior art.
Secondly, the method is a very robust method in performing a quantitative description of the propagation direction of the electrical activity and its frequency content. Even under the condition that the real signal is several times lower than the noise amplitude (under the condition that the signal-to-noise ratio is very low), a better information flow analysis result can be obtained. Compared with other algorithms, the method can obtain better analysis results when the multi-channel signals of more than two channels face.
Thirdly, the method provided by the invention has short testing time and convenient and fast data processing; the method can roughly understand the addiction degree of the ice-poison addict during detection, and is convenient for evaluating the effect of the ice-poison abstinence.
Drawings
FIG. 1 is a flow chart of a method for detecting an addictive drug addict based on multichannel fNIRS signals according to an embodiment of the invention;
fig. 2 is a flowchart of a classifier construction method according to an embodiment of the present invention.
FIG. 3 is a flow chart of fNIRS signal processing for 12-fold cross-validation according to an embodiment of the present invention;
FIG. 4 is a flow chart of a specific experiment provided by an embodiment of the present invention;
FIG. 5 is a diagram of a fNIRS channel provided by an embodiment of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Since methamphetamine relies on affecting the central nervous system of a human to produce a pleasure in humans and thus to produce dependency on methamphetamine, there is a large difference between the signal of the fNIRS diagram of an addictive human from a normal human when performing the same task.
Example 1
The embodiment of the invention discloses an ice toxicity detection method based on multi-channel fNIRS signals and a directed transfer function, which comprises the steps of calculating the directed transfer function value between the fNIRS signals of each channel of two types of subjects to generate directed transfer function matrixes of the two types of subjects; and after each channel fNIRS signal is subjected to each preprocessing operation, a directed transfer function value between each channel fNIRS signal of the tester is calculated to generate a directed transfer function matrix of the tester, and the matrix is used as input and is sent into a trained classifier to obtain a detection result. The scheme is based on cranial nerve signals, overcomes the defects of the traditional ice toxin detection, provides a novel method for the ice toxin detection, greatly prolongs the effective time of the detection, and finally improves the detection accuracy rate through testing.
Specifically, the method comprises two parts of classifier construction and detection, wherein:
as shown in fig. 1, constructing a classifier includes the steps of:
in this embodiment, 6 channels of 7, 12, 29, 30, 39 and 40 are selected, as shown in fig. 5, and the six channels are located in the frontal lobe and central region, respectively. The frontal lobe is responsible for high-level cognitive activities such as judgment, planning, decision making, thinking, memory and the like, and is closely related to intelligence and mental activities; the central region has a somatosensory cortex, which senses the somatic information.
In this example, 36 of the ice drug addicted women and healthy women with an average age of 24.83 + -4.9 years were selected as the subjects. The grouping standard is as follows: 1) the product meets the standard of ice toxicity dependence, but has no dependence on other substances or abuse (such as ***e, heroin, marijuana, alcohol, nicotine, etc.); 2) no psychiatric disease or brain trauma; 3) no drugs with an effect on mental activity were used for the first two weeks of the experiment. The 22 healthy control subjects were matched to the subjects with addiction to ice-toxicity by age and education, and the control subjects had no history of drug use.
An improved double-selection oddball experimental mode is adopted, 3 blocks are provided in total, each block is provided with 100 test times, 70 standard test times, 15 addiction deviation test times and 15 contrast deviation test times, standard stimulation pictures are basketball pictures, and 45 deviation picture ice toxicity related pictures and neutral pictures are provided respectively. The specific experimental process is shown in FIG. 4, wherein a gaze point "+" 300ms is first displayed on a computer screen, a jitter stimulus interval (ISI: 1000- > 1500ms) is then displayed, and then a target stimulus 15s is displayed, and the user is required to press an F key for the basketball picture and a J key for the non-basketball picture.
And step 3, feature extraction: the above-mentioned directed transfer function values of the data sets of the S2 stimulus responses of the two types of pre-processed population are calculated respectively, and 288 directed transfer function matrices of 6 × 6 × 30 (number of channels × frequency) are generated, including 144 directed transfer function matrices of 6 × 6 × 30 (number of channels × frequency) of the addict population (18 people) and 144 directed transfer function matrices of 6 × 6 × 30 (number of channels × frequency) of the healthy population (18 people). In this example only the 6 channel fNIRS signal was selected for analysis. In this embodiment, a directed transfer function matrix (square matrix) is generated by using the hemmes toolkit, and the horizontal and vertical axes represent the selected 6 channels. And (3) performing t-test statistical test on the two groups of directed transfer function values of each connection pair, and using Bonferroni multiple correction to take the directed transfer function values of adjacent edges with significant difference as classification features.
The calculation method of the values of the directed transfer functions among the fNIRS signals of the channels is as follows:
for a given time lag r, consider a p-order multivariate autoregressive process of dimension M, i.e. measuring M signals x simultaneously1(t)，x2(t)......xM(t), then the time domain representation of the fNIRS signal:
a in the formula (1)rIs a matrix of coefficients of order M x M, and ε (t) is independent white Gaussian noise with a covariance matrix, M is the number of fNIRS channels;
the frequency domain representation of the fNIRS signal can be obtained by computing a power spectral density matrix:
S(f)＝H(f)∑HH(f) (2)
formula (2) medium (.)HDenotes the Hermitian transpose, H (f) is the transfer function matrix, f denotes the frequency;
obtained according to equations (1) and (2):
h (f) a column vector representing the transfer function matrix H (f);
then the value of the directed transfer function DTF from the j-channel fNIRS signal to the i-channel fnrs signal is:
in the formula (3), Hij(f) Is an element of row i and column j of H (f), hj(f) The j-th column vector representing H (f).
And 4, pattern recognition and classification: performing 12-fold cross validation on the feature set, for example, if each group is 144 pieces of data, in each trade-off, 132 sample data of each group are used for a training set, and the remaining 12 sample data are used for a test set.
And 5, selecting a Fisher classifier according to the machine learning model. Then, the test set is sent to the classifier, and whether the test data belongs to addicts or healthy persons is judged according to the previous training result so as to complete the test. The FNIRS signal processing flow of this study is shown in figure 3,
after the classifier is constructed, the detection stage is entered, and the detection process is as shown in fig. 2: and (2) extracting the fNIRS signals of the testee in real time through the multi-channel fNIRS channels in the step (1) to obtain and store the multi-channel fNIRS signals of the testee, performing the preprocessing operation on the fNIRS signals of the channels in the step (2), generating a directed transfer function matrix of the testee by using the step (3), and sending the matrix into the classifier obtained in the step (5) as input to obtain a detection result.
The classification accuracy results of this scheme are shown in table 1.
TABLE 1 Classification accuracy results
The invention discloses a new method, firstly proposes that a multichannel fNIRS acquisition mode is adopted in the ice toxicity detection technology, the latest achievement technology of multichannel signal analysis and processing is fully utilized, a directed transfer function algorithm is adopted to analyze the coherence among a plurality of channel signals, then the directed transfer function values of channel pairs with obvious differences are used as classification characteristics and are sent into a machine learning algorithm to realize classification, and further the detection accuracy is improved.
The scheme adopts the above novel directed transfer function algorithm based on the multichannel fNIRS signals and the novel machine learning method, and the patent invents a detection system based on few times of stimulation, which can reduce stimulation times and greatly reduce test time, thereby greatly reducing the fatigue degree of a tested person and greatly increasing the effective time of ice toxicity detection.
Example 2
The embodiment of the invention provides electronic equipment, which comprises a memory, a processor and a plurality of peripherals, such as a plurality of brain electrodes and the like, wherein the plurality of brain electrodes are connected with the electronic equipment to form a system for detecting an ice drug addict based on a multichannel fNIRS signal, and the system comprises:
the signal extraction module is composed of a brain electrode and a connecting channel between the brain electrode and the electronic equipment, and extracts the fNIRS signal of the tester in real time through the multi-channel fNIRS channel to obtain the multi-channel fNIRS signal of the tester and store the multi-channel fNIRS signal into the memory;
the memory is also stored with a computer software program which is read and executed by the processor and is used for realizing the method for detecting the syphilis addiction patient based on the multichannel fNIRS signal disclosed by the embodiment 1 of the invention. Whereby the memory may include:
the preprocessing and matrix generating module is used for calculating the value of a directed transfer function between the fNIRS signals of each channel after preprocessing the fNIRS signals of each channel to generate a directed transfer function matrix of a tester;
and the classification detection module is used for sending the directed transfer function matrix of the tester into a classifier as input to obtain a detection result.
It should also be noted that the logic instructions in the computer software program can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. An ice drug addict detection system based on a multichannel fNIRS signal, the system comprising:
the signal extraction module is used for extracting the fNIRS signal of the tester in real time through the multi-channel fNIRS channel to obtain and store the multi-channel fNIRS signal of the tester;
the preprocessing and matrix generating module is used for calculating the value of a directed transfer function between the fNIRS signals of each channel after preprocessing the fNIRS signals of each channel to generate a directed transfer function matrix of a tester;
the classification detection module is used for sending the directed transfer function matrix of the tester into a classifier as input to obtain a detection result;
the method for calculating the value of the directed transfer function between the fNIRS signals of each channel by the preprocessing and matrix generating module is as follows:
for a given time lag r, consider a p-order multivariate autoregressive process of dimension M, i.e. measuring M signals x simultaneously1(t)，x2(t)……xM(t), the time domain of the fNIRS signalExpressing:
a in the formula (1)rIs a matrix of coefficients of order M x M, and ε (t) is independent white Gaussian noise with a covariance matrix, M is the number of fNIRS channels;
the frequency domain representation of the fNIRS signal can be obtained by computing a power spectral density matrix:
S(f)＝H(f)∑HH(f) (2)
formula (2) medium (.)HDenotes the Hermitian transpose, H (f) is the transfer function matrix, f denotes the frequency;
obtained according to equations (1) and (2):
h (f) represents the column vectors that make up the transfer function matrix H (f);
then the value of the directed transfer function DTF from the j-channel fNIRS signal to the i-channel fnrs signal is:
h in the formula (3)ij(f) Is an element of row i and column j of H (f), hj(f) The j-th column vector representing H (f).
2. The system of claim 1, wherein the preprocessing operations include filtering, segmentation, baseline correction, and superposition averaging.
3. The system according to claim 2, wherein the preprocessing operation specifically comprises:
filtering the fNIRS signals, wherein the filtering parameters are respectively set to be 0.03-0.1Hz band-pass filtering; after filtering, segmenting fNIRS data from 1S before stimulation to 15S after stimulation as an epoch, and calling the epoch as an S2 stimulation response; baseline correction was performed with the pre-stimulation 1s data as baseline, followed by a mean-add for every 5 epochs in the data.
4. The system of claim 1, further comprising, prior to testing the tester, training the classifier, the training of the classifier comprising:
extracting fNIRS signals of addicts and healthy subjects in real time through a multi-channel fNIRS channel respectively to obtain multi-channel fNIRS signals of the two subjects respectively and storing the multi-channel fNIRS signals;
sequentially preprocessing each channel fNIRS signal of the two types of subjects to obtain fNIRS signals corresponding to each channel addiction stimulation of the two types of subjects;
respectively calculating values of the directed transfer functions between the fNIRS signals of the channels of the two types of subjects to generate two groups of directed transfer function matrixes corresponding to the two types of subjects;
calculating average directed transfer function matrixes corresponding to the two types of subjects, carrying out t-test statistical test on the average value of the two groups of directed transfer functions of each connected pair, and using Bonferroni multiple correction to obtain adjacent edges corresponding to the average values of the two groups of directed transfer functions with significant differences;
and constructing a characteristic vector as sample data by using directed transfer function values of adjacent edges with significance difference of the two types of subjects, and performing K-fold cross validation on the initial machine learning model to obtain the classifier with the optimal parameter combination.
5. The system of claim 4, wherein the K-fold cross validation comprises: in each compromise of cross validation, K-1 sample data of the addictive group and K-1 sample data of the healthy group were used in the training set, and the remaining 1 sample data of the addictive group and 1 sample data of the healthy group were used in the test set.
6. The system according to any one of claims 1 to 5, wherein the classifier employs a Fisher classifier.
7. An electronic device, comprising:
a memory for storing a computer software program;
a processor for reading and executing a computer software program stored in the memory to implement a multi-channel fnis signal-based syphilis addiction detection system as claimed in any one of claims 1 to 6.
8. A non-transitory computer readable storage medium having stored therein a computer software program for implementing a multichannel fNIRS signal based icy drug addict detection system of any of claims 1 to 6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106109427A (en) * | 2016-07-19 | 2016-11-16 | 中南民族大学 | The application in preparation treatment asthmatic medicament of a kind of compound recipe puppet fiber crops U.S. sweet smell sheet |
| CN107106862A (en) * | 2014-07-29 | 2017-08-29 | 电路治疗公司 | System and method for light genetic therapy |
| CN107438775A (en) * | 2015-01-30 | 2017-12-05 | 特里纳米克斯股份有限公司 | Detector for the optical detection of at least one object |
| CN109444066A (en) * | 2018-10-29 | 2019-03-08 | 山东大学 | Model transfer method based on spectroscopic data |
| CN110501338A (en) * | 2019-09-29 | 2019-11-26 | 吉林省天慧大数据科技有限公司 | A kind of detection method of methamphetamine based on EO-1 hyperion, heroin and hemp |
| CN110547793A (en) * | 2019-07-02 | 2019-12-10 | 上海大学 | Electroencephalogram and near-infrared combined drug addiction evaluation method |
| CN110680282A (en) * | 2019-10-09 | 2020-01-14 | 黑龙江洛唯智能科技有限公司 | Method, device and system for detecting temporary abnormal state of brain |
| CN111012360A (en) * | 2019-12-30 | 2020-04-17 | 中国科学院合肥物质科学研究院 | Device and method for collecting nervous system data of drug-dropping person |
| CN111035840A (en) * | 2019-12-20 | 2020-04-21 | 上海大学 | VR closed-loop drug addiction self-control force training system |
| CN111714089A (en) * | 2020-06-11 | 2020-09-29 | 阿呆科技（北京）有限公司 | Drug addiction evaluation system based on multi-stimulus short video event related potential |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090024050A1 (en) * | 2007-03-30 | 2009-01-22 | Searete Llc, A Limited Liability Corporation Of The State Of Delaware | Computational user-health testing |
| US20170020442A1 (en) * | 2015-07-24 | 2017-01-26 | Johnson & Johnson Vision Care, Inc. | Biomedical devices for biometric based information communication and feedback |
| CN106264570B (en) * | 2016-08-23 | 2023-09-26 | 北京大智商医疗器械有限公司 | Method, device and system for monitoring addiction status of drug addict in real time |
| CN206183286U (en) * | 2016-08-23 | 2017-05-24 | 北京大智商医疗器械有限公司 | Portable terminal and system of wearing of real -time supervision rehabilitation person heart addiction state |
| US20190223770A1 (en) * | 2018-01-24 | 2019-07-25 | Bela Malik | Biological sample extraction and detection system |
-
2020
- 2020-12-09 CN CN202011450176.9A patent/CN112617824B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107106862A (en) * | 2014-07-29 | 2017-08-29 | 电路治疗公司 | System and method for light genetic therapy |
| CN107438775A (en) * | 2015-01-30 | 2017-12-05 | 特里纳米克斯股份有限公司 | Detector for the optical detection of at least one object |
| CN106109427A (en) * | 2016-07-19 | 2016-11-16 | 中南民族大学 | The application in preparation treatment asthmatic medicament of a kind of compound recipe puppet fiber crops U.S. sweet smell sheet |
| CN109444066A (en) * | 2018-10-29 | 2019-03-08 | 山东大学 | Model transfer method based on spectroscopic data |
| CN110547793A (en) * | 2019-07-02 | 2019-12-10 | 上海大学 | Electroencephalogram and near-infrared combined drug addiction evaluation method |
| CN110501338A (en) * | 2019-09-29 | 2019-11-26 | 吉林省天慧大数据科技有限公司 | A kind of detection method of methamphetamine based on EO-1 hyperion, heroin and hemp |
| CN110680282A (en) * | 2019-10-09 | 2020-01-14 | 黑龙江洛唯智能科技有限公司 | Method, device and system for detecting temporary abnormal state of brain |
| CN111035840A (en) * | 2019-12-20 | 2020-04-21 | 上海大学 | VR closed-loop drug addiction self-control force training system |
| CN111012360A (en) * | 2019-12-30 | 2020-04-17 | 中国科学院合肥物质科学研究院 | Device and method for collecting nervous system data of drug-dropping person |
| CN111714089A (en) * | 2020-06-11 | 2020-09-29 | 阿呆科技（北京）有限公司 | Drug addiction evaluation system based on multi-stimulus short video event related potential |
Non-Patent Citations (4)
| Title |
|---|
| Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol;Yeom Seul-Ki,Won Dong-Ok,Chi Seong In,等;《PLOS ONE》;20171109;第12卷(第11期);全文 * |
| ***成瘾者脑电信号的分类研究;高军峰,张家琦,韦思宏,等;《电子科技大学学报》;20201130;全文 * |
| 用于痫样放电和脑血流响应分析的光电测控***设计;张程;《中国优秀硕士学位论文全文数据库医药卫生科技辑》;20190415;全文 * |
| 近红外光谱术用于人脑光神经信号检测的可行性研究;孙白雷;《中国博士学位论文全文数据库工程科技Ⅰ辑》;20150715;全文 * |
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