RESUMO
In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.
Assuntos
Humanos , Algoritmos , Artefatos , Eletroencefalografia , Métodos , Potenciais Evocados , Fisiologia , Análise de Componente Principal , Processamento de Sinais Assistido por Computador , Análise de OndaletasRESUMO
The robust extracting of evoked potential (EP) has become a vexed question in the process of electroencephalogram owing to the faint signal-to-noise ratio of EP. This paper presents the single trial of EP in time, transform and space field. Several methods such as adaptive filter, wavelet transform, principal component analysis (PCA) and independent component analysis (ICA) have been applied to the process of EP.