Abstract
Introduction: Electroencephalogram (EEG) signals have gained significant popularity in various applications due to their rich information content. However, these signals are prone to contamination from various sources of artifacts, notably the electrooculogram (EOG) artifacts caused by eye movements. The most effective approach to mitigate EOG artifacts involves recording EOG signals simultaneously with EEG and employing blind source separation techniques, such as independent component analysis (ICA). Nevertheless, the availability of EOG recordings is not always feasible, particularly in pre-recorded datasets. Objective: In this paper, we present a novel methodology that combines a long short-term memory (LSTM)-based neural network with ICA to address the challenge of EOG artifact removal from contaminated EEG signals. Approach: Our approach aims to accomplish two primary objectives: 1) estimate the horizontal and vertical EOG signals from the contaminated EEG data, and 2) employ ICA to eliminate the estimated EOG signals from the EEG, thereby producing an artifact-free EEG signal. Main results: To evaluate the performance of our proposed method, we conducted experiments on a publicly available dataset comprising recordings from 27 participants. We employed well-established metrics such as mean squared error, mean absolute error, and mean error to assess the quality of our artifact removal technique. Significance: Furthermore, we compared the performance of our approach with two state-of-the-art deep learning-based methods reported in the literature, demonstrating the superior performance of our proposed methodology.
Abstract (translated)
介绍:EEG信号因其丰富的信息内容而在各种应用中获得了广泛应用。然而,这些信号容易受到各种干扰项的影响,特别是由于眼睛运动引起的眼动电信号干扰项。为了减轻眼动电信号干扰项的影响,我们提出了一种新方法,它同时记录EEG信号和眼动电信号,并使用独立成分分析(ICA)等盲源分离技术。方法:我们的目标是实现两个主要目标:1)从污染的EEG数据中估计水平和垂直的眼动电信号;2)使用ICA从EEG中删除估计的眼动电信号,从而生成无干扰的EEG信号。结果:为了评估我们提出的新方法的性能,我们在一个包含27个参与者记录的公开数据集上进行了实验。我们使用了 established metrics,如平方误差、绝对误差和平均误差,来评估我们的干扰去除技术的质量。意义:此外,我们比较了我们的新方法的性能与文献中报道的两种先进的深度学习方法,证明了我们提出的新方法的性能优势。
URL
https://arxiv.org/abs/2308.13371
