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Multi-modal BCI feature extraction method based on PF coefficient

A feature extraction and multi-modal technology, applied in the field of pattern recognition, can solve problems such as only considering data correlation, achieve reasonable channel distribution, improve classification performance, and moderate quantity

Pending Publication Date: 2021-03-26
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the channel selection method based on Pearson correlation coefficient only considers the correlation between data, while the channel selection method based on Fisher value only considers the separability of different task features

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  • Multi-modal BCI feature extraction method based on PF coefficient
  • Multi-modal BCI feature extraction method based on PF coefficient
  • Multi-modal BCI feature extraction method based on PF coefficient

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Embodiment Construction

[0017] The PF coefficient-based multimodal BCI feature extraction method of the present invention will be described in detail below in conjunction with the accompanying drawings. like figure 1 , the implementation of the present invention mainly includes 4 steps: (1) EEG-near-infrared signal acquisition and preprocessing; (2) channel selection; (3) feature extraction; (4) feature normalization and LDA classification.

[0018] Each step will be described in detail below one by one.

[0019] Step (1): The present invention is described by using the public dataset established by Shin et al., Technical University of Berlin. The dataset contains EEG and fNIRS signals collected from 29 healthy subjects (14 males and 15 females, mean age 28.5±3.7). The sampling rate of the EEG system was 1000 Hz. EEG acquisition electrode position is determined by AFp1, AFp2, AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7, T8, CCz, CCP3h, CCP4h, CCP6h, Pz, P3, P4 , P7,...

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Abstract

The invention discloses a multi-modal BCI feature extraction method based on a PF coefficient. The method comprises the following steps: firstly, selecting reasonable time window data to perform PF coefficient channel selection; and combining the Person coefficient representing the correlation between the signals with the Fisher value representing the separability between the features to constructa PF coefficient representing the task discrimination, and setting a reasonable threshold to select the channel; then extracting a common spatial pattern feature in the EEG and a statistical featurein the fNIRS. and finally, carrying out classification through a shrinkage linear discriminant analysis SLDA classifier. The blocks selected by the method can effectively avoid difference between different individuals and different channels, the channels are reasonable in distribution and moderate in number, the classification performance of the multi-mode BCI system is improved to a certain extent, and a new thought is provided for feature extraction of electroencephalogram-near-infrared signals.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and proposes a channel selection method based on PF (Person-Fisher, PF) coefficients. Task classification for mental counting-based BCI systems. By combining the Person coefficient representing the correlation between signals with the Fisher value representing the separability between features, the PF coefficient representing the task differentiation is constructed, and a reasonable threshold is set to select the channel. Redundant information between channels is reduced, and then common space pattern (Common space pattern, CSP) features in EEG and statistical features in fNIRS are extracted. Finally, it is classified by shrinking linear discriminant analysis (Shrinking linear discriminant analysis, SLDA) classifier. Background technique [0002] Brain-computer interface (BCI) provides a direct communication channel between the human central nervous system and the computer. This means that w...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06F17/16
CPCG06F17/16G06F2218/04G06F2218/08G06F2218/12
Inventor 戴橹洋孟明马玉良佘青山
Owner HANGZHOU DIANZI UNIV
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