Neural-ICA and wavelet transform for artifacts removal in surface EMG

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.