Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

Abstract

Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the "wavelet enhanced" ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved. Index Terms-Electroencephalographic (EEG) artifacts, independent component analysis, entropy, kurtosis, wavelet.1530-437X/$26.00

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

“…AWICA and EAWICA are based on the joint use of wavelet transform, ICA and higher order statistics used as markers for automatic detection [22]. The new method, EAWICA, here proposed is based on the same choice of parameters, but involves an additional detection step, which refines the detail of artifact detection further.…”

“…The first step (Block 1) consists of partitioning each EEG window into the four main brain waves (delta, theta, alpha and beta): therefore, each EEG signal is split into four time series, the four so-called wavelet components (WCs). For this purpose, discrete wavelet transform (DWT) is used [22]. Figure 1.…”

“…Proper metrics were selected in [22] to quantify these properties: namely Renyi's entropy and kurtosis.…”

“…In order to remove artifacts from the EEG automatically, a technique, automatic wavelet-independent component analysis (AWICA), was recently proposed by the authors ( [22][23][24][25][26][27]), which performs automatic artifact rejection, while not discarding any epoch of the EEG recording.…”

Removal of EMG Artifacts from Multichannel EEG Signal Using Automatic Dynamic Segmentation and Adaptive Thresholding with Multilevel Decomposed Wavelets

“…A [2]. A new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique.…”