Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Authors:
Muhammad Tahir Akhtar;Wataru Mitsuhashi;Christopher J. James
Affiliations:
The Center for Frontier Science and Engineering (CFSE), The University for Electro-Communications, 1-5-1 Chofugaoka, Tokyo 182-8585, Japan;Department of Information and Communication Engineering, The University for Electro-Communications, 1-5-1 Chofugaoka, Tokyo 182-8585, Japan;Institute of Digital Healthcare, International Digital Laboratory, WMG, University of Warwick, Coventry, UK
Venue:
Signal Processing
Year:
2012

Citing 7
Cited 5

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Nonlinear independent component analysis: existence and uniqueness results

Neural Networks
Independent component analysis: algorithms and applications

Neural Networks
A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case

Computers in Biology and Medicine
A novel semiblind signal extraction approach for the removal of eye-blink artifact from EEGs

EURASIP Journal on Advances in Signal Processing
Translation-invariant denoising using multiwavelets

IEEE Transactions on Signal Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach

IEEE Transactions on Audio, Speech, and Language Processing

Complexity analysis of EEG data during rest state and visual stimulus

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking

Expert Systems with Applications: An International Journal
Automatic removal of eye-blink artifacts for neurofeedback training systems

Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive Technology
Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis

Signal Processing
Comparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. The independent component analysis (ICA) can be an effective and applicable method for EEG denoising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ concept of the spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from the extracted-artifacts ICs, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out, which demonstrate effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.