Biomedical Engineering Graduate Program, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
Sensors (Basel). 2022 Apr 15;22(8):3051. doi: 10.3390/s22083051.
Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color-Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.
警觉水平评估对于避免危及生命的人为错误至关重要。航空管制、驾驶或军事监视等关键工作环境要求操作员保持警觉。脑电图（EEG）是一种非常常见的评估警觉水平的模态。不幸的是，由于眼球运动、肌肉收缩和电噪声，EEG 信号容易受到伪影的影响。减轻这些伪影对于准确的警觉水平评估很重要。独立成分分析（ICA）是一种有效的方法，已广泛应用于 EEG 伪影的抑制。然而，在警觉评估应用中，发现它容易受到大脑活动泄露到伪影中的影响。在这项工作中，我们表明，小波独立成分分析（wICA）方法为减少伪影提供了一种替代方法，从而改善了警觉水平评估结果。我们在九名人类受试者中进行了一项实验，在大约 45 分钟的时间内，通过执行 Stroop 颜色-单词测试来诱发两种警觉状态，警觉和警觉下降。然后，我们使用五种分类器比较了 ICA 和 wICA 预处理方法的性能。我们的分类结果表明，在特征提取方面，wICA 方法优于现有的 ICA 方法。在 delta、theta 和 alpha 波段，我们使用 ICA 方法获得了 84.66%的平均分类准确率，而使用 wICA 方法的平均准确率为 96.9%。然而，在 beta 波段没有观察到显著的改善。此外，我们比较了地形图，以显示两种警觉状态下大脑区域的功率谱密度变化。该方法表明，额叶和中央区域对警觉下降最敏感。然而，在这种应用中，与 Fast-ICA 相比，所提出的 wICA 仅显示出略有改善。
