Kim Kyung Hwan, Yoon Hyo Woon, Park Hyun Wook
Department of Biomedical Engineering, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Kangwon-do 220-710, South Korea.
J Neurosci Methods. 2004 May 30;135(1-2):193-203. doi: 10.1016/j.jneumeth.2003.12.016.
The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance image (fMRI) is a promising tool that is capable of providing high spatiotemporal brain mapping, with each modality supplying complementary information. One of the major barriers to obtain high-quality simultaneous EEG/fMRI data is that pulsatile activity due to the heartbeat induces significant artifacts in the EEG. The purpose of this study was to develop a novel algorithm for removing heartbeat artifact, thus overcoming problems associated with previous methods. Our method consists of a mean artifact wave form subtraction, the selective removal of wavelet coefficients, and a recursive least-square adaptive filtering. The recursive least-square adaptive filtering operates without dedicated sensor for the reference signal, and only when the mean subtraction and wavelet-based noise removal is not satisfactory. The performance of our system has been assessed using simulated data based on experimental data of various spectral characteristics, and actual experimental data of alpha-wave-dominant normal EEG and epileptic EEG.
同步记录脑电图（EEG）和功能磁共振成像（fMRI）是一种很有前景的工具，能够提供高时空分辨率的脑图谱，每种模态都能提供互补信息。获取高质量同步EEG/fMRI数据的主要障碍之一是心跳引起的脉动活动会在EEG中产生显著伪影。本研究的目的是开发一种去除心跳伪影的新算法，从而克服与先前方法相关的问题。我们的方法包括平均伪影波形减法、小波系数的选择性去除以及递归最小二乘自适应滤波。递归最小二乘自适应滤波在没有专用参考信号传感器的情况下运行，并且仅在平均减法和基于小波的噪声去除效果不理想时使用。我们系统的性能已通过基于各种频谱特征的实验数据的模拟数据以及α波占主导的正常EEG和癫痫EEG的实际实验数据进行了评估。
