[The reconstruction study of EEG signal based on sparse approximation & compressive sensing].

OBJECTIVE

Due to random sampling of non-adaptive, high-quality reconstruction of the original signal, one-dimensional non-stationary multi-channel EEG signal can be achieved automatic detection and analysis.

METHODS

A new multicomponent redundant dictionaries with the atoms of the Gaussian function and its first and second derivatives was built in the paper, and reconstructed signal base on compressed sensing measurement model.

RESULTS

The selected dictionary atoms can more effectively match the EEG signals in a variety of transient characteristics of the waveform, allowing the formation of EEG signal is more sparse matching pursuit decomposition. With the theory based on compressed sensing signal sampling, only half of the original signal with different sample size can be used to reconstruct the original signal quality, the important instantaneous features of the waveform can well be maintained.

CONCLUSION

Signal sampling based on the theory of compressed sensing contains enough information of the original signal, using the prior conditions of EEG signals (or compressibility) sparsity, high-dimensional signal and original image can be reconstructed through a certain decoding of linear or nonlinear model.