Modifying the Spatially-Constrained ICA for Efficient Removal of Artifacts from EEG Data
This paper concerns artifact removal from multichannel EEG data. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. 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 the concept of spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from extracted artifacts, 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 all ICs are not identified. The computer experiments are carried out, which demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.
Muhammad Tahir Akhtar Christopher J.James Wataru Mitsuhashi
The Center for Frontier Science and Engineering (CFSE),The University of Electro-Communications, 1-5 Department of Information and Communication Engineering, The University of Electro-Communications, 1 Signal Processing and Control Group, Institute of Sound and Vibration Research (ISVR), University of
国际会议
成都
英文
1-4
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）