Improving EEG-Based Emotion Classification Using Conditional Transfer Learning.
Improving EEG-Based Emotion Classification Using Conditional Transfer Learning.
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DOI：
10.3389/fnhum.2017.00334
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发表时间：
2017
影响因子：
2.9
通讯作者：
Jung TP
中科院分区：
文献类型：
--
作者：
Lin YP;Jung TP
为了克服个体差异，精确的基于脑电图（EEG）的情感分类系统需要对每个个体进行大量的生态校准数据，这是劳动密集型和耗时的。近年来，迁移学习在脑电信号挖掘领域受到越来越多的关注。TL利用从其他人那里收集的现有数据，为几乎没有校准数据的新个体构建模型。然而，暴力转移到个人（即，因此，本研究提出了一个条件性目标语（cTL）框架，以促进每个个体的正迁移（在不增加标记数据的情况下提高被试的具体表现）。cTL首先评估个体的可迁移性以获得正迁移，然后选择性地利用来自具有可比特征空间的其他人的数据。实证结果表明，在26个个体中，所提出的cTL框架确定了16和14个可转移的个人谁可以受益于其他数据的情绪效价和唤醒分类，分别。然后，这些可转移的个体可以利用来自18和12个具有相似EEG特征的个体的数据，以获得效价和觉醒分类准确性的最大TL改善。cTL提高了26个人的整体分类性能的~15%的效价分类和~12%的唤醒对应物，相比，他们的默认性能仅基于特定主题的数据。这项研究显然证明了所提出的cTL框架的可行性，提高个人的默认情感分类性能的数据库。cTL框架可能会揭示一个强大的情感分类模型的发展，使用更少的标记特定于主题的数据对现实生活中的情感脑机接口（ABCI）。
To overcome the individual differences, an accurate electroencephalogram (EEG)-based emotion-classification system requires a considerable amount of ecological calibration data for each individual, which is labor-intensive and time-consuming. Transfer learning (TL) has drawn increasing attention in the field of EEG signal mining in recent years. The TL leverages existing data collected from other people to build a model for a new individual with little calibration data. However, brute-force transfer to an individual (i.e., blindly leveraged the labeled data from others) may lead to a negative transfer that degrades performance rather than improving it. This study thus proposed a conditional TL (cTL) framework to facilitate a positive transfer (improving subject-specific performance without increasing the labeled data) for each individual. The cTL first assesses an individual’s transferability for positive transfer and then selectively leverages the data from others with comparable feature spaces. The empirical results showed that among 26 individuals, the proposed cTL framework identified 16 and 14 transferable individuals who could benefit from the data from others for emotion valence and arousal classification, respectively. These transferable individuals could then leverage the data from 18 and 12 individuals who had similar EEG signatures to attain maximal TL improvements in valence- and arousal-classification accuracy. The cTL improved the overall classification performance of 26 individuals by ~15% for valence categorization and ~12% for arousal counterpart, as compared to their default performance based solely on the subject-specific data. This study evidently demonstrated the feasibility of the proposed cTL framework for improving an individual’s default emotion-classification performance given a data repository. The cTL framework may shed light on the development of a robust emotion-classification model using fewer labeled subject-specific data toward a real-life affective brain-computer interface (ABCI).