Mayeli Ahmad, Zotev Vadim, Refai Hazem, Bodurka Jerzy
Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA.
Laureate Institute for Brain Research, Tulsa, OK, USA.
J Neurosci Methods. 2016 Dec 1;274:27-37. doi: 10.1016/j.jneumeth.2016.09.012. Epub 2016 Sep 30.
Simultaneous acquisition of EEG and fMRI data results in EEG signal contamination by imaging (MR) and ballistocardiogram (BCG) artifacts. Artifact correction of EEG data for real-time applications, such as neurofeedback studies, is the subject of ongoing research. To date, average artifact subtraction (AAS) is the most widespread real-time method used to partially remove BCG and imaging artifacts without requiring extra hardware equipment; no alternative software-only real time methods for removing EEG artifacts are available.
We introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA). The rtICA is based on real time independent component analysis (ICA) and it is employed following the AAS method. The rtICA was implemented and validated during EEG and fMRI experiments on healthy subjects.
Our results demonstrate that the rtICA employed after the rtAAS can obtain 98.4% success in detection of eye blinks, 4.4 times larger INPS reductions compared to RecView-corrected data, and effectively reduce motion artifacts, as well as imaging and muscle artifacts, in real time on six healthy subjects.
We compared our real-time artifact reduction results with the rtAAS and various offline methods using multiple evaluation metrics, including power analysis. Importantly, the rtICA does not affect brain neuronal signals as reflected in EEG bands of interest, including the alpha band.
A novel real-time ICA method was proposed for improving the EEG quality signal recorded during fMRI acquisition. The results show substantial reduction of different types of artifacts using real-time ICA method.
同时采集脑电图（EEG）和功能磁共振成像（fMRI）数据会导致EEG信号被成像（MR）伪迹和心冲击图（BCG）伪迹污染。针对神经反馈研究等实时应用的EEG数据伪迹校正，是正在进行的研究课题。迄今为止，平均伪迹减法（AAS）是最广泛使用的实时方法，用于部分去除BCG和成像伪迹，无需额外的硬件设备；目前尚无仅用软件的替代实时方法来去除EEG伪迹。
我们引入了一种用于在fMRI期间进行实时EEG伪迹校正的新颖改进方法（rtICA）。rtICA基于实时独立成分分析（ICA），并在AAS方法之后使用。rtICA在对健康受试者进行的EEG和fMRI实验中得到了实施和验证。
我们的结果表明，在rtAAS之后使用rtICA能够在检测眨眼方面获得98.4%的成功率，与RecView校正后的数据相比，INPS降低幅度大4.4倍，并且能在六名健康受试者身上实时有效减少运动伪迹以及成像和肌肉伪迹。
我们使用包括功率分析在内的多种评估指标，将我们的实时伪迹减少结果与rtAAS和各种离线方法进行了比较。重要的是，rtICA不会影响感兴趣的EEG频段（包括α频段）所反映的脑神经元信号。
提出了一种新颖的实时ICA方法，用于提高在fMRI采集期间记录的EEG质量信号。结果表明，使用实时ICA方法可大幅减少不同类型的伪迹。
