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Brain-electrical signal processing method based on isolated component automatic clustering process

An automatic clustering and EEG signal technology, applied in the direction of user/computer interaction input/output, instrument, character and pattern recognition, etc., can solve the problems of immature and effective, ICA technology limitations, etc., to achieve separability improvement, It is beneficial to the extraction and analysis of signal features or the recognition of task patterns and the effect of efficient classification

Inactive Publication Date: 2009-07-22
TIANJIN UNIV
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Problems solved by technology

However, the independent components obtained through traditional ICA decomposition are randomly sorted. There are no mature and effective methods for how to eliminate spontaneous components, extract evoked components related to target stimuli, and eliminate interference components corresponding to noise sources, and often depend on the user. Subjective experience, which makes the application of ICA technology to evoked EEG processing is relatively limited

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  • Brain-electrical signal processing method based on isolated component automatic clustering process
  • Brain-electrical signal processing method based on isolated component automatic clustering process
  • Brain-electrical signal processing method based on isolated component automatic clustering process

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Embodiment Construction

[0030] The present invention develops an Independent Component Automatic Clustering (ICAC) method, by defining the mutual information distance measurement between the independent components in the EEG, and using the sequence similarity of the evoked components in the repeated stimulation EEG signal To achieve the automatic classification of independent components, so as to realize the effective identification of evoked components related to target stimuli.

[0031] The present invention will be further described below in conjunction with the accompanying drawings and examples.

[0032] 1. Technical solution:

[0033] 1 ICA model

[0034] The classic ICA model can be given by figure 1 Indicates that the multi-channel observation signal X(t)=[x 1 (t),x 2 (t),...,x n (t)] T (here, x i In (t), t represents the sampling moment, and i represents the acquisition channel) is considered to be composed of multiple information sources S(t)=[s 1 (t), s 2 (t),...,s m (t)] T Form...

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Abstract

The invention relates to electroencephalographic signal extraction, in particular to an electroencephalographic signal processing method based on isolated component automatic-clustering process. In order to improve signal-to-noise ratio for inducing an electroencephalographic signal, to remarkably improve the separability of the electroencephalographic signal within an action period under the stimulation of different tasks, and to facilitate the extraction and analysis of signal characteristics and the recognition of the task mode, the invention adopts a technical proposal which is in particular as below: the electroencephalographic signal processing method based on isolated component automatic-clustering process comprises the following steps: firstly, adopting an online maximum information algorithm (Informax algorithm) based on an information maximization criterion to sequentially carry out isolated component analysis (ICA) on stimulated and induced multi-channel signals, constructing a large component sample set Y with all obtained components, calculating the mutual information of the components, and finally adopting a total intra-class distance minimization criterion to carry out clustering process on a mutual information distance matrix so as to obtain class tags of all the components.

Description

technical field [0001] The invention relates to separating and extracting induced electroencephalogram signals in a spontaneous electroencephalogram background, in particular to an electroencephalogram signal processing method based on independent component automatic clustering processing. technical background [0002] EEG signals are non-stationary random signals, they are very weak, strong random, and have strong background noise. Therefore, it is quite difficult to extract, analyze and identify them. Brain-computer interface technology is a direct communication and control channel established between the human brain and computers or other electronic devices. Through this channel, people can directly use their thoughts to control external devices without any physical actions. It realizes the functions expected by people by collecting, analyzing, processing and identifying the characteristic signals of EEG. People's research on it began in the 1990s, and the research main...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F3/01
Inventor 綦宏志朱誉环明东周仲兴万柏坤
Owner TIANJIN UNIV
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