CN112614539A

CN112614539A - Motor imagery detection method based on TEO-MIC algorithm

Info

Publication number: CN112614539A
Application number: CN202011642735.6A
Authority: CN
Inventors: 李亚兵; 陈墨; 王红玉; 李红叶
Original assignee: Xian University of Posts and Telecommunications
Current assignee: Xian University of Posts and Telecommunications
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-04-06
Anticipated expiration: 2040-12-31
Also published as: CN112614539B

Abstract

The invention provides a motion imagination state detection method based on a TEAger energy operator and micro-state combined TEO-MIC algorithm, which comprises the following steps: (1) preprocessing an original EEG by adopting a data segmentation method, and establishing Teager modeling for preparation; (2) establishing a discrete time sequence based on a Teager model; (3) determining corresponding whole brain field intensity according to the discrete time sequence; (4) clustering the field intensity of the whole brain, and calculating corresponding micro-states; (5) and calculating the micro-state parameters in each state according to the existing micro-state, thereby determining the motor imagery state. According to the invention, through a method of combining the Teager energy operator and the micro-state, the brain function state can be effectively analyzed, and the motor imagery state can be detected.

Description

Motor imagery detection method based on TEO-MIC algorithm

Technical Field

The invention relates to the crossing field of signal and information processing and neurobiology, in particular to a method for combining electroencephalogram (EEG) and a micro-state algorithm based on a Teager energy operator. It proposes and designs an algorithm that combines Teager with the micro-state algorithm (TEO-MIC). Because EEG signal collection is easily interfered by Gaussian noise, an algorithm with better robustness is needed, and a Teager energy operator is a theory with better suppression characteristic on the Gaussian noise, the invention is a method for combining the EEG signal collection and the Gaussian noise.

Background

EEG signals, which are non-stationary and complex signals, are generally thought to be produced by a combination of concussive activities in different brain regions. Compared with the traditional characteristic analysis method, the complex network analysis method has more visual and effective effect on analysis of non-stationary signals such as EEG. The microstate, as a parameter describing the global brain functional state change information, can reflect the complexity of different brain regional activities in different cognitive states. The change of the activity state of the brain is indicated by the mutual conversion between different micro states.

Therefore, the EEG signals are analyzed by using a complex network analysis method, the micro-states of the EEG signals in different cognitive states are calculated, the change of different motor imagery states can be detected, and the motor imagery states are further evaluated. However, the EEG signal adopted by the method is susceptible to gaussian noise interference, and robustness for motor imagery state detection is not high.

The Teager Energy Operator (Teager Energy Operator) is a theory with good inhibition characteristic on Gaussian noise, so that the method is suitable for carrying out Teager Operator modeling on an EEG signal to further calculate the micro state of the EEG signal.

Based on Teager energy operator and micro-state, the invention provides a method for detecting a motor imagery state based on a TEO-MIC algorithm

Disclosure of Invention

The invention provides a method for detecting a motor imagery state by combining a Teager energy operator and a micro-state TEO-MIC algorithm, which adopts the Teager energy operator to perform Teager modeling on an original EEG signal to construct a new discrete time sequence, then performs calculation of micro-state parameters on the discrete time characteristic sequence, and realizes the detection of the motor imagery state by using the micro-state parameters, wherein the basic scheme is as follows:

1. preprocessing an original EEG, segmenting an EEG signal according to a specific experimental paradigm, and preparing for Teager modeling;

2. processing the segmented data, and establishing a discrete time sequence based on a Teager model;

3. extracting characteristics by using a discrete time sequence based on a Teager model, and solving corresponding micro-state parameters:

the following method for extracting the micro-state features by utilizing the discrete time sequence is established:

(1) calculating the whole brain Field intensity (GFP) of the electroencephalogram data by using the discrete time sequence based on the Teager model;

(2) clustering brain maps at the peak of the field intensity of the whole brain according to a TAAHC (probabilistic analysis and aggregation architecture) algorithm to obtain four basic microstate;

4. calculating the micro-state parameters in each state by using the variation trend of four micro-states based on the time sequence: number of occurrences per second (occupancy), Duration (Duration), microstate fraction (Coverage), spatial correlation coefficient (Mspatcorr);

5. and detecting the motor imagery state by using the obtained micro-state parameters.

The method has the advantages that in the process of finally detecting the state of the EEG signal, the Teager modeling method is adopted to reduce the interference of Gaussian noise, so that the robustness of the calculation of the micro state is further greatly improved; by comparing various algorithms with a method based on the combination of TEO energy operators and micro-states, the method obtains high state detection accuracy.

Drawings

FIG. 1 computing flow based on TEO-MIC algorithm

FIG. 2 is a graph comparing the calculated micro-state parameter ROC curves of the present solution and the classical algorithm

FIG. 3 is a comparison graph of AUC area of the micro-state parameter of the present solution and the micro-state parameter of the classical algorithm

FIG. 4 is a schematic diagram showing comparison between micro-state parameters calculated by the present scheme and recognition performance of a classical algorithm

Detailed Description

The following detailed description of embodiments of the invention refers to the accompanying drawings.

The flow of the TEO-MIC algorithm proposed by the present invention is shown in FIG. 1, and the following describes the detailed implementation of the present invention in detail with reference to the accompanying drawings.

1. Preprocessing raw EEG, including: segmenting the EEG data to facilitate Teager energy operator modeling of the data; according to the design of an experimental paradigm, task segmentation processing is carried out on an EEG signal, and 3s data of a motor imagery segment are selected for analysis;

2. teager energy operator modeling is carried out on the segmented EEG data, namely:

where x (n) represents a discrete EEG signal,

representing the modeling output of Teager energy operators, wherein a, b, c and d are respectively selected to be 0, 1, 2 and 3;

3. calculating the micro-state of the electroencephalogram signals:

1) calculating the whole brain field intensity GFP of the brain electrical signal, which can reflect the change of the brain to the motor imagery state:

in the formula u_iIs the voltage value of the i-th lead,

the voltage average of all leads, N represents the number of leads.

2) Calculating the time point corresponding to the maximum value of the whole brain field intensity obtained in the step 1), and constructing a time sequence of the local maximum value by taking the electric field intensity corresponding to the local maximum value time point as a local field potential to obtain a brain topographic map;

3) according to TAAHC algorithm, four micro states are obtained for the time sequence of local maximum of the whole brain field intensity under each tested task state;

4) and sequentially calculating the micro-state parameters of each tested moment according to the four micro-states obtained in the step 3).

4. And detecting the motor imagery state by utilizing the relation between the brain nerve activity degree, the spatial information and the micro state.

The algorithm of the present invention is compared to the classical micro-state algorithm. Experimental results, as shown in fig. 2-4, the algorithm of the present invention has better recognition performance than the classical micro-state algorithm.

In the process of motor imagery, the robustness of the calculation of the micro state is insufficient, and the system identification performance can be greatly improved by combining the Teager operator with the micro state. In the examples, the experimental data was derived from the BCI Competition IV 2008 database. The scheme proves the effectiveness of the scheme by analyzing the significance of the micro states of different motor imaginations and showing the experimental results in fig. 3 and 4. Meanwhile, the TEO-MIC algorithm and the calculation of the micro state are compared, and an experimental result is shown in figure 4, so that the result shows that the algorithm can obviously improve the identification performance.

Claims

1. A motion imagination state detection method based on a TEO-MIC algorithm comprises the following steps:

(1) preprocessing an original EEG to prepare for modeling of a Teager energy operator, and then performing coordinate transformation to establish a Teager model of a discrete time sequence;

(2) discrete time sequence constructed by Teager model of discrete time sequence

Calculating corresponding micro-state parameters;

(3) and determining the motor imagery state according to the obtained micro-state parameters.

2. Motor imagery state detection method according to claim 1The method is characterized in that Teager energy operator modeling is carried out on the EEG signal and the extracted discrete sequence is subjected to

The micro-state parameter calculations are performed instead of directly performing the micro-state calculations on the raw data.

3. The TEO-MIC algorithm-based motor imagery state detection method research of claim 1, wherein: the method utilizes the Teager energy operator model to extract the discrete time sequence of the EEG signal, and overcomes the problem that the EEG signal is easily interfered by noise when being directly utilized.