Neural-ICA and wavelet transform for artifacts removal in surface EMG
- 21 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4 (10987576) , 3223-3228
- https://doi.org/10.1109/ijcnn.2004.1381194
Abstract
Recent works have shown that artifacts removal in biomedical signals, like electromyographic (EMG) or electroencephalographic (EEG) recordings, can be performed by using discrete wavelet transform (DWT) or independent component analysis (ICA). Often, the removal of some artifacts is very hard because they are superimposed on the recordings and they corrupt biomedical signals also in frequency domain. In these cases DWT and ICA methods cannot perform artifacts cancellation. We present a method based on the joint use of wavelet transform and independent component analysis. We show the obtained results and the comparisons among the proposed method, DWT and ICA techniques. In this preliminary study, a user interface is needed to identify the artifact.Keywords
This publication has 7 references indexed in Scilit:
- Common drive detection for axial muscles cerebral control and coherence analysis of surface electromyography by neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transformIEEE Transactions on Biomedical Engineering, 2002
- Independent Component AnalysisPublished by Wiley ,2001
- Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjectsPublished by Elsevier ,2000
- Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian SourcesNeural Computation, 1999
- Time Frequency and Wavelets in Biomedical Signal ProcessingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995