Automatic identification and removal of scalp reference signal for intracranial EEGs based on independent component analysis
Automatic identification and removal of scalp reference signal for intracranial EEGs based on independent component analysis
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DOI：
10.1109/tbme.2007.892929
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发表时间：
2007-09-01
影响因子：
4.6
通讯作者：
Worrell, Gregory A.
中科院分区：
文献类型：
--
作者：
Hu, Sanqing;Stead, Matt;Worrell, Gregory A.
对非活动记录基准的追求是脑电(EEG)最古老的技术问题之一。由于常用的头位参考会污染脑电，并可能导致误解，因此提取参考贡献具有重要意义。在本文中，我们将独立分量分析(ICA)应用于颅内记录，并提出了两种自动识别和去除参考的方法，该方法基于头皮参考独立于局部和分布的颅内来源的假设。这一假设得到了我们的结果的支持，通常是有效的，因为参考头皮电极由于头骨的高电阻率而与颅内电极相对电隔离。我们指出，当参考被视为源时，线性模型是欠定的，并讨论了一种特殊的欠定情况，在这种情况下，唯一的一类输出可以被分离。对于这种情况，大多数ICA算法都可以应用，我们认为颅内或头皮脑电符合这种特殊情况。我们将所提出的两种方法应用于三名接受癫痫手术评估的患者的颅内脑电，并将结果与双极和平均参考记录进行比较。该方法在定量脑电信号研究中具有广阔的应用前景。
The pursuit of an inactive recording reference is one of the oldest technical problems in electroencephalography (EEG). Since commonly used cephalic references contaminate EEG and can lead to misinterpretation, extraction of the reference contribution is of fundamental interest. Here, we apply independent component analysis (ICA) to intracranial recordings and propose two methods to automatically identify and remove the reference based on the assumption that the scalp reference is independent from the local and distributed intracranial sources. This assumption, supported by our results, is generally valid because the reference scalp electrode is relatively electrically isolated from the intracranial electrodes by the skull's high resistivity. We point out that the linear model is underdetermined when the reference is considered as a source, and discuss one special underdetermined case for which a unique class of outputs can be separated. For this case most ICA algorithms can be applied, and we argue that intracranial or scalp EEGs follow this special case. We apply the two proposed methods to intracranial EEGs from three patients undergoing evaluation for epilepsy surgery, and compare the results to bipolar and average reference recordings. The proposed methods should have wide application in quantitative EEG studies.