CN115153531A - Method for detecting brain state stability characteristics - Google Patents
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Abstract
The invention relates to a method for detecting brain state stability characteristics, which is technically characterized by comprising the following steps of: establishing a test paradigm for detecting brain state stability characteristics; a tester wears a functional near-infrared probe and tests according to a test paradigm, collects brain signals of the tester when the tester carries out a gait task, and preprocesses the collected brain signals; dividing the preprocessed signals by utilizing sliding windows, and constructing functional connectivity of the signals in each sliding window to obtain a connectivity matrix: clustering all connectivity matrixes; and calculating to obtain brain state stability characteristics, wherein the brain state stability characteristics comprise the variance and the variation coefficient of the state in the walking task. The brain state stability detection system is reasonable in design, can accurately detect and obtain brain state stability characteristics, effectively reveals the operation mechanism of the brain, has the characteristics of rapidness, accuracy, convenience in use and the like, and can be widely applied to the fields of driver fatigue detection, brain-computer interfaces and the like.
Description
Technical Field
The invention belongs to the technical field of signal detection, relates to brain signal detection, and particularly relates to a method for detecting brain state stability characteristics.
Background
With the rapid development of brain imaging technology, brain signal analysis and detection are receiving more and more attention and attention from many people. The existing brain signal acquisition technology mainly comprises electroencephalogram measurement, functional nuclear magnetism, positron emission tomography and the like. However, functional nuclear magnetic and positron emission tomography are difficult to collect brain changes of a person during a motor task, and the preparation process for electroencephalogram measurement is complicated and requires a long preparation time.
In recent years, more and more researchers have utilized functional near-infrared to analyze and study brain signals. The functional near-infrared preparation time is short, the functional near-infrared preparation method has strong anti-motion interference capability, and can effectively collect the functional change of the brain during the motion task, so the method is favored by researchers.
In the study of functional near infrared, the most commonly used features include time domain features, frequency domain features, map features and phase features, however these features do not take into account the stabilization of brain states. Research shows that the brain state can describe the exchange of brain information at a certain moment, the brain state can change along with time, the brain operation mechanism can be revealed through the change of the brain state in a certain period of time, and the method can be used in the fields of driver fatigue detection, brain-computer interfaces and the like.
In summary, how to accurately detect the stable characteristics of the brain state is a problem which needs to be solved urgently at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for detecting the brain state stability characteristics, which is reasonable in design, rapid, accurate and convenient to use.
The invention solves the technical problems in the prior art by adopting the following technical scheme:
a method for detecting brain state stabilization features comprises the following steps:
step 1, establishing a test paradigm for detecting brain state stability characteristics;
step 2, the tester wears the functional near-infrared probe and tests according to the test paradigm established in the step 1, collects the brain signals of the tester when the tester carries out the gait task, and preprocesses the collected brain signals;
step 3, dividing the preprocessed signals by utilizing the sliding windows, and constructing functional connectivity of the signals in each sliding window to obtain a connectivity matrix:
step 4, clustering all connectivity matrixes;
and 5, calculating to obtain a brain state stability characteristic, wherein the brain state stability characteristic comprises the variance and the variation coefficient of the state in the walking task.
Further, the test paradigm established in step 1 includes the following:
firstly, a test person stands for 35 seconds;
secondly, the tester walks for one minute at a normal walking speed;
and thirdly, allowing the test personnel to rest for 2 minutes.
Further, the method for preprocessing the collected brain signals in the step 2 comprises the following steps:
converting a collected light intensity signal into concentration change of oxygen-containing hemoglobin by using a modified Lambert beer law;
denoising by using a 0.01-0.2Hz band-pass filter;
removing the signal artifact by using a sliding window technology:
and extracting data at the gait task time and correcting the data by taking the signal 5 seconds before the walking task as a base line.
Further, the specific implementation method of step 3 is as follows: firstly, setting the size and the step length of a sliding window, and then calculating the Pearson correlation coefficient between every two channel signals:
wherein x is i And x j Signals representing the i and j channels, m representing the signal x i The length of (a) is greater than (b),
representing a signal x i The mean value of (a);
finally, a connectivity matrix is established:
where N is the total number of channels.
Further, the size of the sliding window is set to 20 seconds, and the step size is set to 1 second.
Further, the specific implementation method of step 4 is as follows: calculating the distance between classes of which the class number is from 1 to 10, and determining the optimal class number by using an elbow method; and obtaining the clustering center of each category according to the optimal number of the categories, wherein each clustering center represents a brain state, and determining the state of the brain function connection network at each moment by using the clustering centers.
Further, the specific implementation method of step 4 is as follows: said step 5 calculates the variance S according to the following equation SD Coefficient of variation S CV ：
Wherein T is generated during walkingThe number of windows, S (t), represents the brain state at time t,
means representing brain state.
The invention has the advantages and positive effects that:
the invention has reasonable design, utilizes functional near infrared to measure brain signals when a person carries out gait tasks, preprocesses the collected signals, divides a plurality of time windows with equal length, constructs a function communication matrix of each time window, clusters all the function communication matrices to obtain the brain state represented by the brain function network at each moment, calculates the variation coefficient of the variance of the brain within the task time, further obtains the brain state stability characteristic, effectively reveals the running mechanism of the brain, has the characteristics of rapidness, accuracy, convenient use and the like, and can be widely applied to the fields of driver fatigue detection, brain-computer interfaces and the like.
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FIG. 1 is a process flow diagram of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings.
A method for detecting brain state stabilization features, as shown in fig. 1, comprising the steps of:
step 1, establishing a testing paradigm for detecting brain state stability characteristics, wherein the specific flow of the testing paradigm is as follows:
test personnel stand for 35 seconds.
And the tester walks for one minute at normal walking speed.
And thirdly, allowing the test personnel to rest for 2 minutes.
And 2, wearing the functional near-infrared probe by the tester and ensuring that the functional near-infrared probe is close to a brain area to be measured, testing according to the testing paradigm established in the step 1, collecting brain signals of the tester during gait tasks, and preprocessing the collected brain signals.
In this step, the collected brain signals need to be preprocessed, the preprocessing method includes light intensity conversion, physiological noise removal, artifact removal and baseline correction, and the specific preprocessing method is as follows:
the method comprises the steps of converting collected light intensity signals into concentration changes of oxygen-containing hemoglobin by using a modified Lambert beer law, and completing light intensity conversion processing.
Denoising by using a 0.01-0.2Hz band-pass filter, and completing physiological noise removal processing.
Removing the signal artifact by using a sliding window technology.
And extracting data at the gait task time and correcting the data by taking the signal 5 seconds before the walking task as a base line.
And 3, dividing the preprocessed signals by using a sliding window, wherein the size of the sliding window is set to be 20 seconds, and the step length is set to be 1 second. Constructing functional connectivity of signals in each sliding window, wherein the specific method comprises the following steps: calculating the Pearson correlation coefficient of each two channel signals:
wherein x is i And x j Signals representing the i and j channels, m representing the signal x i The length of (a) of (b),
representing a signal x i Of the average value of (a).
And calculating coefficients between every two channels according to the formula, and constructing a connectivity matrix:
where N is the total number of channels.
And 4, clustering all the connectivity matrixes.
In this step, in order to determine the optimal number of clusters, the inter-class distance of the number of classes from 1 to 10 is calculated, and the optimal number of classes is determined using the elbow method. And confirming the optimal number of the classes to obtain a clustering center of each class, wherein each clustering center represents a brain state, and the clustering center is used for determining the state of the brain function connection network at each moment.
Step 5, calculating to obtain the stable characteristics of the brain state, including the variance S of the state in the walking task SD And coefficient of variation S CV ：
Where T is the number of windows generated during walking, S (T) represents the brain state at a certain time,
means representing brain state. Variance S SD And coefficient of variation S CV For measuring the stability of brain state, the smaller the value, the lower the stability of brain state.
The detection function of the brain state stability characteristic can be realized through the steps, the brain state stability characteristic can reveal the operation mechanism of the brain, and the method has wide application fields, such as:
a brain-computer interface: the brain state stabilization feature can describe the stability of the brain state, which when low, indicates the existence of some intention and activation, and by recognizing this intention, enhances the communication between the brain signal and the machine.
And (3) fatigue detection: when the human brain deals with fatigue, the stability of the brain state is reduced, thereby warning the driver and preventing traffic accidents caused by fatigue.
It should be emphasized that the embodiments described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, the embodiments described in the detailed description, as well as other embodiments that can be derived by one skilled in the art from the teachings herein.
Claims (7)
1. A method for detecting brain state stabilization features, comprising: the method comprises the following steps:
step 1, establishing a test paradigm for detecting brain state stability characteristics;
step 2, the tester wears the functional near-infrared probe and tests according to the test paradigm established in the step 1, collects the brain signals of the tester when the tester carries out the gait task, and preprocesses the collected brain signals;
step 3, dividing the preprocessed signals by utilizing sliding windows, and constructing functional connectivity of the signals in each sliding window to obtain a connectivity matrix:
step 4, clustering all connectivity matrixes;
and 5, calculating to obtain brain state stability characteristics, wherein the brain state stability characteristics comprise the variance and the variation coefficient of the state in the walking task.
2. The method for detecting brain state stabilization according to claim 1, wherein: the test paradigm established in step 1 includes the following:
firstly, a test person stands for 35 seconds;
secondly, testing the walking speed of the personnel for one minute;
and the testers rest for 2 minutes.
3. The method for detecting brain state stabilization according to claim 1, wherein: the method for preprocessing the collected brain signals in the step 2 comprises the following steps:
converting a collected light intensity signal into concentration change of oxygen-containing hemoglobin by using a modified Lambert beer law;
denoising by using a 0.01-0.2Hz band-pass filter;
removing the signal artifact by using a sliding window technology:
and extracting data at the gait task time and correcting the data by taking the signal 5 seconds before the walking task as a base line.
4. The method for detecting brain state stabilization according to claim 1, wherein: the specific implementation method of the step 3 comprises the following steps: firstly, setting the size and the step length of a sliding window, and then calculating the Pearson correlation coefficient between every two channel signals:
wherein x is i And x j Representing the signals of the i-th and j-th channels, m representing the signal x i The length of (a) of (b),
representing a signal x i The mean value of (a);
finally, a connectivity matrix is established:
where N is the total number of channels.
5. The method for detecting brain state stabilization according to claim 4, wherein: the size of the sliding window is set to 20 seconds and the step size is set to 1 second.
6. The method for detecting brain state stabilization according to claim 1, wherein: the specific implementation method of the step 4 comprises the following steps: calculating the distance between classes of which the class number is from 1 to 10, and determining the optimal class number by using an elbow method; and obtaining the clustering center of each category according to the optimal number of the categories, wherein each clustering center represents a brain state, and determining the state of the brain function connection network at each moment by using the clustering centers.
7. According to claim 1The method for detecting the brain state stability characteristics is characterized by comprising the following steps: the specific implementation method of the step 4 comprises the following steps: said step 5 calculating the variance S according to the following equation SD Coefficient of variation S CV ：
Where T is the number of windows generated during walking, S (T) represents the brain state at time T,
means representing brain state.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105078426A (en) * | 2014-05-08 | 2015-11-25 | 邱春元 | System, method and device for diagnosing sleep apnea |
| CN111616702A (en) * | 2020-06-18 | 2020-09-04 | 北方工业大学 | Lie detection analysis system based on cognitive load enhancement |
| US20210169417A1 (en) * | 2016-01-06 | 2021-06-10 | David Burton | Mobile wearable monitoring systems |
| CN113180668A (en) * | 2021-04-30 | 2021-07-30 | 浙江大学 | Real-time functional magnetic resonance lie detection system based on cognitive load change |
| CN113589935A (en) * | 2021-08-03 | 2021-11-02 | 李俊豪 | Brain wave interaction method based on artificial intelligence and brain-computer interface cloud server |
| CN113647938A (en) * | 2021-08-18 | 2021-11-16 | 苏州大学 | Method and system for advanced detection of motion state change based on physiological signals |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105078426A (en) * | 2014-05-08 | 2015-11-25 | 邱春元 | System, method and device for diagnosing sleep apnea |
| US20210169417A1 (en) * | 2016-01-06 | 2021-06-10 | David Burton | Mobile wearable monitoring systems |
| CN111616702A (en) * | 2020-06-18 | 2020-09-04 | 北方工业大学 | Lie detection analysis system based on cognitive load enhancement |
| CN113180668A (en) * | 2021-04-30 | 2021-07-30 | 浙江大学 | Real-time functional magnetic resonance lie detection system based on cognitive load change |
| CN113589935A (en) * | 2021-08-03 | 2021-11-02 | 李俊豪 | Brain wave interaction method based on artificial intelligence and brain-computer interface cloud server |
| CN113647938A (en) * | 2021-08-18 | 2021-11-16 | 苏州大学 | Method and system for advanced detection of motion state change based on physiological signals |
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