This study proposes a novel combination of independent component analysis (ICA) in conjunction with support vector machine (SVM) and denoising autoencoder (DA), for the first time, for removal of eyeblink artefacts from the corrupted electroencephalography (EEG). At first the eyeblink corrupted EEG signals are decomposed into independent components (ICs) using ICA, the corrupted-ICs are then identified using SVM as a classifier. From the corrupted-ICs, the artefacted segment is identified with a second SVM classifier and corrected by the pre-trained DA. Finally, inverse-ICA operation is applied on the remaining ICs and the corrected ICs to obtain the artefact-free EEG signal. The proposed methodology modifies only the portion corrupted with artefacts, and does not alter the uncorrupted part, thereby preserving the neural information in the original EEG. The proposed methodology was implemented to remove eyeblinks from the EEG data collected from the publicly available EEGLab data set. The results reveal that the proposed methodology is superior to the other recently reported methods in terms of the mutual information and average correlation coefficient. Further, the proposed method is automatic and does not require any intervention of the operator, whereas the other methods require intervention of the user.

中文翻译：

---

使用独立分量分析结合支持向量机和去噪自动编码器的自动EEG眨眼伪像识别和去除技术

这项研究首次提出了一种独立成分分析（ICA）结合支持向量机（SVM）和去噪自动编码器（DA）的新颖组合，用于从损坏的脑电图（EEG）中去除眨眼伪像。首先，眨眼损坏的EEG信号使用ICA分解为独立的组件（IC），然后使用SVM作为分类器识别损坏的IC。从损坏的IC中，使用第二个SVM分类器识别伪造的段，并通过预训练的DA对其进行校正。最后，将反向ICA操作应用于其余IC和校正后的IC，以获得无伪像的EEG信号。拟议的方法仅修改了伪造品损坏的部分，而不更改未损坏的部分，从而将神经信息保留在原始EEG中。实施建议的方法是为了消除从可公开获得的EEGLab数据集收集的EEG数据中的眨眼。结果表明，所提出的方法在相互信息和平均相关系数方面优于其他最近报道的方法。此外，所提出的方法是自动的，不需要操作者的任何干预，而其他方法则需要用户的干预。