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PCA and Granger causality based brain network feature extraction method

A feature extraction and brain network technology, applied in the field of brain-computer interface, can solve problems such as the functional connectivity relationship of brain regions that are not considered, and achieve the effect of meeting the requirements of EEG feature extraction and broad application prospects

Inactive Publication Date: 2015-12-16
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that most of the existing EEG feature extraction algorithms are based on the signal research of isolated brain regions and do not take into account the functional connectivity relationship between brain regions, and propose a brain function based on PCA and Granger causality. Network Feature Extraction Algorithm

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  • PCA and Granger causality based brain network feature extraction method
  • PCA and Granger causality based brain network feature extraction method
  • PCA and Granger causality based brain network feature extraction method

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Embodiment Construction

[0015] Describe in detail the brain function network feature extraction algorithm based on PCA and Granger causality of the present invention below in conjunction with accompanying drawing, figure 1 for the implementation flow chart.

[0016] Such as figure 1 , the implementation of the inventive method mainly comprises 3 steps: (1) carry out the rough division of brain functional area to multi-channel signal; (2) utilize PCA to extract the maximum principal component time information of each functional area; (3) calculate the maximum principal component time information; A causal measure between components and used as a feature parameter.

[0017] Each step will be described in detail below one by one.

[0018] Step 1: Roughly divide the functional regions of the brain for multi-channel signals

[0019] According to Brodmann's partition system (Brodmannarea) and related theories of brain functional areas, the multi-channel signals are roughly divided into brain regions. L...

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Abstract

The invention discloses a PCA and Granger causality based brain network feature extraction method. The method comprises: firstly, performing brain functional region division on a plurality of channel signals; secondly, extracting maximum main component time information of each functional region by utilizing PCA; and finally, calculating causality measure between maximum main components, and taking the causality measure as a feature parameter. According to the method, connection effects among the brain regions are revealed based on a Granger causality theory from a brain functional network effect, so that rich information contained in electroencephalogram signals is represented more comprehensively and further mode classification is facilitated.

Description

technical field [0001] The invention belongs to the field of brain-computer interface, and relates to a brain function network feature extraction algorithm based on PCA and Granger causality for the control of intelligent rehabilitation aids. Background technique [0002] Brain-Computer Interface (BCI) technology has developed rapidly in recent years and is currently a research hotspot in the field of human-computer interaction. Based on EEG signals, it realizes human The "mind control" of the company has expanded from the initial rehabilitation medical treatment to astronaut training, smart home, game entertainment and other fields. [0003] With the continuous exploration of the structure and function of the human brain, more and more researchers have realized the importance of the brain functional network for the study of motor imagery (MI). MI refers to a kind of mental work that only repeatedly performs exercise simulation training in the brain, which is helpful for th...

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Application Information

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IPC IPC(8): G06F3/01A61B5/0476
Inventor 佘青山陈希豪田卓韩笑
Owner HANGZHOU DIANZI UNIV
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