Motor imagery detection method based on TEO-MIC algorithm

Abstract

The invention provides a motor imagery state detection method based on a TEO-MIC algorithm combining a Teager energy operator and a micro-state, and the method comprises the steps: (1) carrying out the preprocessing of an original EEG through a data segmentation method, and building 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 whole-brain field intensity, and calculating a corresponding micro-state; and (5) according to the existing micro-state, calculating the micro-state parameter in each state so as to determine the motor imagery state. According to the method, the brain function state can be effectively analyzed through the method of combining the Teager energy operator and the micro-state, and the motor imagery state is detected.

Description

technical field [0001] The invention relates to the cross field of signal and information processing and neurobiology, in particular to a method for combining electroencephalogram (electroencephalogram, EEG) and microstate algorithm based on Teager energy operator. It proposes and designs an algorithm combining Teager and microstate algorithm (TEO-MIC). Since the EEG signal is easily interfered by Gaussian noise, a robust algorithm is needed, and the Teager energy operator is a theory that has better suppression characteristics for Gaussian noise. The present invention uses A way to combine the two. Background technique [0002] EEG signal, as a kind of non-stationary and complex signal, is generally believed to be produced by the combined action of different brain regions' concussion activities. Compared with the traditional feature analysis method, the complex network analysis method is more intuitive and effective for the analysis of non-stationary signals such as EEG. ...

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Problems solved by technology

However, the EEG signal used in this method is susceptible to Gaussian noise interference, and the robustness to motor imagery state detection is not high.

[0004] The Teager Energy Operator (Teager Energy Operator) is a theory that has good suppression characteristics for Gaussian noise, so it is suitable for the Teager operator modeling of EEG signals, and then calculates its microstate. This method is to make full use of EEG Due to the non-stationary characteristics of the signal, this patent combines the Teager energy operator with the microstate to propose a TEO-MIC algorithm, which overcomes the disadvantage of being easily interfered by Gaussian noise and effectively improves the system robustness

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