Classifying method based on sparse measurement

Abstract

The invention discloses a classifying method based on sparse measurement. The classifying method specifically comprises the following steps of: culturing a distribution information matrix among classes and a distribution information matrix among classes; decomposing the obtained matrixes; converting a Fisher criterion to an L1 module structure; and estimating a projection vector for enabling an objective function to obtain the maximum value. As the existing LDA (Linear Discriminant Analysis) based on L2 mode measure function generates unfavorable amplification to the noises such as Outliers and the like, the classifying method disclosed by the invention can overcome the problem that the existing linear discriminant analysis based on L2 mode is affected by the Outliers and noise isolated points by adopting a measure function based on L1 mode during a discriminant analysis constructing process, so that stable classifying and identifying effect is obtained, and the stability of a BCI (Brain Computer Interface) system is improved to certain extent.

Description

technical field [0001] The invention belongs to the technical field of biomedical information, in particular to a pattern classification method in the field of brain-computer interface. Background technique [0002] Brain Computer Interface (BCI) is a channel for direct communication and control between the human brain and the outside world using computers or other external electronic devices (Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002 ) Brain-computer interfaces for communication and control. Clin Neurophysiol 113, 767-791). BCI research involves many disciplines, such as: neuroscience, signal detection, signal processing, pattern recognition, control theory, etc. The cross-development of these disciplines promotes the advancement of BCI research. The basic theory and clinical application research of BCI has been included in the scope of brain science and neuroengineering, and is also considered by many international authoritative organizations...

AI Technical Summary

Problems solved by technology

Although the solution based on the L2 mode method has good smoothness, it does not have sparsity, and is easily affected by outliers (Outliers) and isolated noise points caused by loose electrodes, eye blinking and improper operation during the experiment. Even sometimes the signal matrix becomes singular, which may cause a wrong estimate of the projection w, which will eventually affect the classification effect; and these interferences will increase the difficulty of reading the EEG signal and hinder the subsequent analysis and research of the EEG signal

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