Lin Yuan-Pin, Jao Ping-Keng, Yang Yi-Hsuan
Institute of Medical Science and Technology, National Sun Yat-sen UniversityKaohsiung, Taiwan.
Institute for Neural Computation, University of California, San DiegoLa Jolla, CA, United States.
Front Comput Neurosci. 2017 Jul 19;11:64. doi: 10.3389/fncom.2017.00064. eCollection 2017.
Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.
如今，利用非侵入式记录的脑电图（EEG）信号构建一个强大的情感感知分析框架已引起广泛关注。然而，在将面向实验室的概念验证研究应用于实际应用时，研究人员面临着一个生态学挑战，即现实生活中记录的EEG模式在不同日子里会发生显著变化（即日间变异性），这可能使预先定义的预测模型容易受到不同日子给定EEG信号的影响。目前的工作旨在解决如何减轻情感反应的日间EEG变异性，以促进跨日情感分类，而这在文献中较少受到关注。本研究提出了一种基于稳健主成分分析（RPCA）的信号滤波策略，并在一项二元情感分类任务（快乐与悲伤）中，使用12名受试者参与音乐聆听任务的五天EEG数据集，验证了其神经生理有效性和机器学习实用性。实证结果表明，RPCA分解的稀疏信号（RPCA-S）能够滤除对日间变异性贡献更大的背景EEG活动，并主要捕捉情感反应的EEG振荡，这些振荡在不同日子里表现相对一致。通过应用一种现实的添加日分类验证方案，RPCA-S逐渐利用了更多信息特征（从12.67±5.99到20.83±7.18），并提高了跨日二元情感分类准确率（从58.31±12.33%提高到64.03±8.40%），即从一个到四个记录日训练EEG信号，并在随后一个未见过的日子进行测试。原始的EEG特征（在RPCA处理之前）既没有实现跨日分类（准确率约为随机水平），也没有由于日间EEG变异性而复制令人鼓舞的改进。这一结果证明了所提出方法的有效性，并可能为开发一个减轻日间变异性的现实情感分类分析框架提供一些启示。
