Brain electrical signal independent component extraction method based on convolution blind source separation

Abstract

The invention discloses a brain electrical signal independent component extraction method based on convolution blind source separation. The brain electrical signal independent component extraction method based on the convolution blind source separation includes concrete steps: building a brain electrical signal independent component extraction system based on the convolution blind source separation, which comprises an AD (analog to digital) sampling module, a short time Fourier transformation module, a frequency domain instantaneous blind source separation module, a sequence adjustment module and a short time inverse Fourier transformation module; using the AD sampling module to sample brain electrical signals; using the short time Fourier transformation module to transform the brain electrical signals from a time domain to a frequency domain; using the frequency domain instant blind source separation module to separate instantaneous mixing signals in the frequency domain; using the sequence adjustment module to perform sequence adjustment on independent components in a vector on each frequency domain segment; using the short time inverse Fourier transformation module to transform a frequency domain separation result into an independent component on the time domain. The brain electrical signal independent component extraction method based on the convolution blind source separation extracts the independent components of brain electrical signals based on a true convolution mixing model, uses a convolution blind source separation frequency domain algorithm, and is simple to achieve, good in separation effect, and low in calculation complexity.

Description

technical field [0001] The invention relates to a method for extracting independent components of electroencephalogram signals, in particular to a method for extracting independent components of electroencephalogram signals based on convolutional blind source separation. Background technique [0002] EEG signal feature extraction is one of the key steps in brain-computer interface. Separating independent components from multi-channel EEG signals, and then extracting feature signals from independent components is a widely used EEG feature extraction process. The EEG signal independent component extraction method models the multi-channel EEG mixing process, and performs blind source separation on the EEG mixed signal based on the model, and obtains multiple components that are statistically independent. At present, the extraction method of independent components of EEG signals is generally based on the instantaneous mixing model, that is, it is assumed that multi-channel EEG ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a method for extracting independent components of EEG signals based on convolutional blind source separation, so as to solve the problem that the independent component extraction method based on the instantaneous mixing model cannot truly describe the mixing process of EEG signals, thereby affecting the feature extraction and classification of EEG signals The problem of recognition accuracy