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A motor imagery brain wave analysis method

An analysis method and motion imagery technology, applied in the field of biomedicine, can solve the problems of no self-adaptive function, poor recognition effect, low recognition rate, etc., and achieve the effect of improving classification efficiency, good real-time performance, and improving signal-to-noise ratio

Active Publication Date: 2018-02-09
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0006] The invention provides a motor imagery brain wave analysis method to solve the problems of poor recognition effect, low recognition rate and no self-adaptive function of existing recognition methods; the method can obtain relatively high signal-to-noise ratio, relatively Clean EEG signals greatly improve the classification accuracy and provide new ideas for feature extraction and classification of motor imagery EEG signals in the BCI system

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  • A motor imagery brain wave analysis method
  • A motor imagery brain wave analysis method
  • A motor imagery brain wave analysis method

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Experimental program
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Embodiment 1

[0027] Embodiment 1: as Figure 1-6 As shown, a motor imagery EEG analysis method, first uses the adaptive notch algorithm to remove the line electrical interference from the collected EEG signals of imagined left and right hand movements, and then uses the adaptive threshold value removal algorithm to discard the seriously polluted EEG fragments, and then use the fourth-order Butterworth high-pass filter to remove the baseline drift, and then use the automatic independent component analysis algorithm to automatically remove the artifact components of oculoelectricity, myoelectricity and non-motion parameter imagination-related neural signal artifacts. Obtain a clean brain signal, use the common space mode to extract the features of the clean brain signal, and obtain the EEG feature vector obtained after the feature extraction; classify the EEG feature vector through the support vector machine, and finally identify the EEG signal phase corresponding to different meanings.

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Embodiment 2

[0038] Embodiment 2: as Figure 1-6 As shown, a motor imagery EEG analysis method, first uses the adaptive notch algorithm to remove the line electrical interference from the collected EEG signals of imagined left and right hand movements, and then uses the adaptive threshold value removal algorithm to discard the seriously polluted EEG fragments, and then use the fourth-order Butterworth high-pass filter to remove the baseline drift, and then use the automatic independent component analysis algorithm to automatically remove the artifact components of oculoelectricity, myoelectricity and non-motion parameter imagination-related neural signal artifacts. Obtain a clean brain signal, use the common space mode to extract the features of the clean brain signal, and obtain the EEG feature vector obtained after the feature extraction; classify the EEG feature vector through the support vector machine, and finally identify the EEG signal phase corresponding to different meanings.

[...

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Abstract

The invention relates to a motor-imagery brain wave analysis method, and belongs to the field of biomedicine. The motor-imagery brain wave analysis method includes the steps that radio interference is firstly removed from collected brain waves with a self-adaptation trapped wave algorithm, then seriously-polluted brain wave segments of the obtained brain waves are abandoned, then baseline drifting is removed, electrooculogram and myoelectricity artifact ingredients and non-motor-parameter-imagery-related-neural-signal artifacts are removed, clean brain waves are obtained at the moment, feature extraction is carried out on the clean brain waves through a common spatial pattern, and brain wave feature vectors obtained after feature extraction are obtained; the brain wave feature vectors are classified through a support vector machine, and different meanings corresponding to the brain waves are finally recognized. By means of the motor-imagery brain wave analysis method, the defects that as for an existing brain wave noise elimination algorithm, noise in the brain waves can not be well eliminated, the recognition effect is not good, and the recognition rate is not high are effectively overcome, the computation burden is small, the algorithm convergence is rapid, and the signal separation accuracy is high; in addition, influences of parameters are small, and therefore the classification accuracy is greatly improved.

Description

technical field [0001] The invention relates to a motor imagery brain wave analysis method, which belongs to the technical field of biomedicine. Background technique [0002] Motor imagery (MI) EEG-based BCI is a very important type of BCI. This type of BCI can directly reconstruct motor control from brain signals and can be used strategically for military purposes. It can also be used for severe sports disabilities and Normal people provide assisted control, thereby improving their quality of life. Research on EEG signals has been widely used in neuroscience, cognitive science, cognitive psychology, and psychophysiology. In recent decades, EEG signals have been used in a new type of human-computer interface—brain-computer interaction. International major frontier research hotspots. [0003] Even so, at present, BCI based on motor imagery is facing huge challenges, one of which is the processing of EEG signals during engineering implementation, mainly due to the low signal...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476G06F19/00
CPCA61B5/7203A61B5/725A61B5/7264A61B5/316A61B5/369
Inventor 杨秋红伏云发孙会文刘传伟余正涛郭剑毅
Owner KUNMING UNIV OF SCI & TECH