Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method

Abstract

The invention relates to a dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method. The method comprises the steps that electroencephalogram signals with suitable channels are selected at first, up-sampling or down-sampling is conducted on the original frequency according to the characteristics of dual-tree complex wavelet frequency partitions, frequency bands corresponding to the frequency ranges of delta, theta, alpha and beta rhythm waves are obtained by means of dual-tree complex wavelet multi-scale decomposition, signal reconstruction is conducted in the scales, a plurality of reconstruction signals in the corresponding frequency bands are obtained, the same decomposition and reconstruction are conducted on the suitable channels, reconstruction signals of all the frequency bands of all the channels are combined to be input into a spatial filter, a six-dimensional feature vector is obtained, and motor imagery task classification is accomplished by means of a support vector machine at last. The dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method not only conducts frequency information analysis on motor imagery electroencephalogram signals, but also can effectively overcome electrode selection defects.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal processing, and relates to an electroencephalogram feature extraction method combining dual-tree complex wavelet and co-space mode. Background technique [0002] For patients with severely impaired neuromuscular function, a new way of communicating with the outside world is urgently needed. The brain-computer interface is just such a method. It does not rely on the peripheral nervous system and muscle tissue outside the brain. It is a way to establish a direct information exchange and control channel between the human brain and computers or other electronic devices. At present, there are many types of EEG signals suitable for brain-computer interfaces, among which motor imagery EEG signals are one of the most widely used types, because motor imagery EEG signals have the advantages of no external stimulation, asynchronous communication, etc., and conform to BCI technological development ...

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Problems solved by technology

Subsequently, Sannelli, Schroder, Barachant, etc. studied the EEG data of multiple subjects imagining the movement of left and right hands, feet, etc., especially when some channels were randomly selected for research, the classification accuracy obtained by different channel combinations was quite different

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