Electroencephalogram signal feature extraction method combining with public space mode algorithm and EMD

Abstract

The invention discloses an electroencephalogram signal feature extraction method combining with a public space mode algorithm and an EMD. Firstly, electroencephalogram signals of a subject are selected as a training set and a testing set, and the signals of the single subject in two channels C3 and C4 are pre-processed; then, experience mode decomposition is conducted on the prepressed EEG signals to obtain a series of intrinsic mode functions IMFi, and energy spectrum diagrams of all the intrinsic mode functions are drawn; then, previous three order IMF components of the channels C3 and C4 undergoing a single pass test are merged to form a N * T matrix Xi, wherein N represents IMF number, T represents the number of sampling points undergoing one test, the whole experiment process includes G groups of tests, and G groups of vector matrixes are obtained and are divided into G1 groups of test vector matrixes and G2 groups of training vector matrixes which are respectively subjected to public space mode decomposition. CSP filtering is conducted on the intrinsic mode functions decomposed by utilizing the three channels and the EMD, frequency domain information of the EMD is added on the basis of the CSP, and the problem that the CSP is lack of the frequency domain information is solved.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves upon previous methods such as time series analysis (TSA) or spectral clustering techniques. It achieves this through performing specific functions called multirculation decompositions into multiple signals based on their underlying modes. These signals are then used together to make up an improved understanding about complex systems like biological tissue.

Problems solved by technology

This patents describes how we now work with Braille systems - specifically those found within brains – to communicate data wirelessly over long distances without being limited physically. Current techniques like Time Division Multiplex Interfaces (TDMls) require multiple inputs and cannot extract sufficient detail about specific areas called targets accurately. To address this problem, there emerged various technical solutions involving advanced mathematical models and machine learning technologies.

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