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Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

机译：独立分量分析在同时EEG-fMRI数据挖掘中的应用：睡眠发作的初步经验

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摘要

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.
机译：脑电图（EEG）和功能性MRI（fMRI）信号的同时采集具有潜在的优势，因为分别在时域和空间域均能实现较高的分辨率。然而，心动描记术伪影以及眼部伪影是检测感兴趣的EEG签名的主要障碍。由于与这些工件相对应的来源与产生EEG签名的来源是独立的，因此我们应用了基于Infomax的独立成分分析（ICA）技术来从工件中分离EEG签名。分离的EEG签名进一步用于建模典型的血液动力学反应功能（HRF）。随后，从fMRI数据分析中，识别出产生这些EEG签名的大脑区域为激活模式的区域。在识别并评估了从癫痫病患者身上产生发作性癫痫发作（IED）峰值的大脑区域后，所提出的方法已成功应用于检测与睡眠发作相关的脑电图特征的θ和α节律以及随后的神经回路来自睡眠不足的志愿者。这些结果表明，ICA技术可用于同时进行EEG-fMRI采集的预处理，特别是在没有参考范式的情况下。

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