IEEE Trans Biomed Eng. 2017 Aug;64(8):1906-1913. doi: 10.1109/TBME.2016.2628958. Epub 2016 Nov 16.
Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.
眼电图（EOG）伪迹干扰是普通脑电图（EEG）研究以及脑机接口（BCI）研究中常见的关键问题。当没有专用的EOG通道可用，或者仅有很少的EEG通道可用于基于独立成分分析的眼动伪迹去除时，这一问题尤其具有挑战性。在伪迹校正过程中避免感兴趣信号的丢失更具挑战性，因为感兴趣信号可能比伪迹弱多个数量级。为了解决这些问题，我们提出了一种用于EEG分析中特征学习的新型判别性眼动伪迹校正方法。无需额外的眼动测量，即可从原始EEG数据中提取伪迹，这完全是自动的，无需人工检查伪迹。然后，通过最大化同一类试验之间的振荡相关性并最小化不同类试验之间的振荡相关性，将伪迹校正与特征提取联合进行优化。我们在一个包含68名执行认知任务受试者的真实EEG数据集上评估了这种方法。结果表明，该方法不仅能够抑制伪迹成分，还能在统计上显著提高分类器的判别能力。我们还证明了所提出的方法解决了认知EEG研究中由眼动引起的混杂问题。
