Method for eliminating myoelectricity noise in electroencephalogram signal based on single channel

An EEG signal and noise elimination technology, applied in the field of biological information, can solve the problem of excessive EEG noise elimination and other problems

Active Publication Date: 2015-06-24
HEFEI UNIV OF TECH
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Problems solved by technology

[0007] In order to avoid the disadvantages of the above-mentioned prior art, the present invention provides a method for eliminating EMG noise in EEG signals based on a single channel, aiming at improving the flexibility and accuracy of EEG noise elimination in EEG signals : On the one hand, solv

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  • Method for eliminating myoelectricity noise in electroencephalogram signal based on single channel
  • Method for eliminating myoelectricity noise in electroencephalogram signal based on single channel
  • Method for eliminating myoelectricity noise in electroencephalogram signal based on single channel

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Embodiment 2

[0057] In order to further quantitatively evaluate the effect of the present invention, it is illustrated that the present invention is not only applicable to single-channel EEG equipment, but also applicable to multi-channel EEG equipment, and is more effective than the previous method with multi-channel as the processing object. The present embodiment simulates N= 19 channels of EEG signals with myoelectric noise, the signal sampling frequency is 250Hz, the signal length is 10 seconds, each channel has T=2500 points, such as image 3 (a) shows the simulated 19-channel clean real EEG signal x EEG (t)=[x EEG1 (t),x EEG2 (t),...,x EEG19 (t)] T ,Such as image 3 (b) shows the mixed EEG signal x(t)=[x 1 (t),x 2 (t),...,x 19 (t)] T , where x(t)=x EEG (t)+λ·x EMG (t), where λ is used to control the strength of myoelectric noise interference, image 3 In (b), λ=1.5. By performing the steps in Example 1 for each channel, the method of the present invention obtains the fol...

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Abstract

The invention discloses a method for eliminating myoelectricity noise in an electroencephalogram signal based on a single channel. The method is characterized by comprising the steps that firstly, a single-channel electroencephalogram signal is decomposed into a plurality of intrinsic mode components through general average empirical mode decomposition; secondly, blind signal separation s conducted on the intrinsic mode components through multi-set canonical correlation analysis, and a plurality of canonical variables are obtained; finally, the canonical variables with the autocorrelation coefficient lower than a certain threshold value are judged to be myoelectricity noise, the myoelectricity noise variables are removed, and reconstruction is conducted to obtain the electroencephalogram signal with the myoelectricity noise removed. According to the method, the purpose of eliminating the myoelectricity noise in the electroencephalogram signal is effectively achieved from the brand new angle of the single channel, and compared with the traditional blind signal separation technology based on the multiple channels, the myoelectricity noise can be better eliminated. The method is suitable for portable and wearable single-channel and few-channel electroencephalogram devices, is also suitable for multi-channel electroencephalogram devices for clinical diagnosis and neuroscience researches, and significant importance is achieved in further researches of the true physiological activities of the human brain.

Description

technical field [0001] The invention belongs to the technical field of biological information, and in particular relates to a single-channel-based method for eliminating myoelectric noise in EEG signals, which is mainly applied to mobile health monitoring of human brain functions and research on human brain-related diseases. Background technique [0002] EEG signals are weak spontaneous and rhythmic electrophysiological activities of human brain nerve cell groups recorded by precision medical instruments. They have the advantages of non-invasive acquisition and high time resolution, and have been widely used in medical clinical diagnosis and human-machine interface. and many other fields. However, since the EEG signal is a relatively weak electrophysiological signal at the microvolt level, it will inevitably be interfered by other electrophysiological activities such as myoelectricity, oculoelectricity, and electrocardiography. Among them, EMG noise is the most difficult so...

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Application Information

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IPC IPC(8): A61B5/0476
CPCA61B5/7203A61B5/316A61B5/369
Inventor 陈勋刘爱萍彭虎
Owner HEFEI UNIV OF TECH
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