Wearable electroencephalogram (EEG) solutions allow portability and real-time measurements in uncontrolled conditions. For reliable and reproducible interpretation of the EEG data, it is essential to accurately identify EEG segments contaminated by artefacts. Two data quality indicator approaches are proposed: pragmatic and regression based. The former extracts statistical features and applies data-driven thresholding, while the latter uses a regression model on the same set of statistical features to predict data quality. The performance of the approaches is validated against EEG data recorded during uncontrolled laboratory and free-living conditions, and compared to a validated approach. The proposed approaches achieve average accuracy of over $\text{83}\%$ in detecting artefactual data, which is higher than the FORCe signal quality estimation method ($\approx\!\text{79}\%$). The main strength of the proposed algorithms is in the significant increase of specificity over the state-of-the-art. The two models perform equally across different databases. Training of the two approaches on free-living conditions data showed better generalization when tested on different types of databases, i.e., uncontrolled laboratory and free-living. Although the accuracy in determining artefact-contaminated data is highest when using a window size of 8 s, the accuracy drop is minor when using shorter window size, demonstrating another advantage over existing methods. Given low complexity of both pragmatic and regression approach, it facilitates a real-time implementation, which is demonstrated using a wearable EEG headset system available at IMEC.

中文翻译：

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实用性和回归性方法对可穿戴式EEG信号质量评估的比较

穿戴式脑电图（EEG）解决方案可在不受控制的条件下实现便携性和实时测量。为了对EEG数据进行可靠且可重复的解释，准确识别被伪影污染的EEG片段至关重要。提出了两种数据质量指标方法：务实和基于回归。前者提取统计特征并应用数据驱动的阈值处理，而后者对同一组统计特征使用回归模型来预测数据质量。根据在不受控制的实验室和自由生活条件下记录的EEG数据验证了这些方法的性能，并与经过验证的方法进行了比较。拟议的方法在检测人工数据方面的平均准确度超过$ \ text {83} \％$，高于FORCe信号质量估计方法（$ \ approx \！\ text {79} \％$）。所提出的算法的主要优势在于与现有技术相比，特异性的显着提高。两种模型在不同的数据库中表现相同。在不同类型的数据库（即不受控制的实验室和自由生活）上进行测试时，对两种自由生活条件数据方法的培训显示出更好的概括性。尽管在使用8 s的窗口大小时确定伪影污染数据的准确性最高，但是在使用较短的窗口大小时精度下降很小，这证明了与现有方法相比的另一个优势。鉴于实用方法和回归方法的复杂性较低，它促进了实时实施，这是通过IMEC的可穿戴式EEG头戴式耳机系统进行演示的。