Xiaopei Wu | PubMed Health

PubMed2003

[Application of complexity sequence in sleep staging based on sleep EEG data].

In this paper an approach of time-window complexity sequence is applied to sleep EEG analysis. This approach can reduce the loss of state information due to the nonstationarity of EEG signal and the unevenness of state space, and can overcome certain limitations of the complexity itself to some extent. It will help to extract the state features of EEG in different sleep stages. In addition, we preprocess EEG by adopting ICA and wavelet transform (WT). The results show that some physiological artifact in EEG can be eliminated effectively by these methods, and the sleep staging based on sleep EEG data will be more exact.

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PubMed2003

[Eliminating artifacts of EEG data based on independent component analysis].

As a new array processing technique, independent component analysis(ICA) is an effective means to resolve the blind source separation(BSS) problem. Based on the brief introductions of ICA theory and algorithm, we apply ICA to the removal of ocular artifacts from EEG recordings. The EEG data collected from the human scalp is actually the mixtures of some independent components. It is coincident with the basic assumptions of ICA. Compared with the traditional methods of artifacts elimination, ICA, a kind of spatial filter, is not restricted by the case of spectrum overlapping, and it has a good reservation of useful detail signals. In addition, the inverse weight matrix of ICA can be used to reflect the topographic structure of different independent sources of EEG.

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