Lin Yuan-Pin, Jung Tzyy-Ping
Institute of Medical Science and Technology, National Sun Yat-sen UniversityKaohsiung, Taiwan.
Institute for Neural Computation, University of CaliforniaSan Diego, San Diego, CA, United States.
Front Hum Neurosci. 2017 Jun 27;11:334. doi: 10.3389/fnhum.2017.00334. eCollection 2017.
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).
为了克服个体差异，基于脑电图（EEG）的准确情绪分类系统需要为每个个体提供大量的生态校准数据，这既耗费人力又耗时。近年来，迁移学习（TL）在脑电信号挖掘领域受到了越来越多的关注。迁移学习利用从其他人那里收集的现有数据，为校准数据很少的新个体构建模型。然而，直接将数据迁移到个体（即盲目利用他人的标记数据）可能会导致负迁移，从而降低性能而非提高性能。因此，本研究提出了一种条件迁移学习（cTL）框架，以促进每个个体的正向迁移（在不增加标记数据的情况下提高个体特定性能）。cTL首先评估个体进行正向迁移的可迁移性，然后有选择地利用具有可比特征空间的其他人的数据。实证结果表明，在26名个体中，所提出的cTL框架分别识别出16名和14名可迁移个体，他们在情绪效价和唤醒分类方面可以从他人的数据中受益。然后，这些可迁移个体可以利用来自18名和12名具有相似脑电特征的个体的数据，以在效价和唤醒分类准确性方面实现最大的迁移学习改进。与仅基于个体特定数据的默认性能相比，cTL在效价分类方面将26名个体的整体分类性能提高了约15%，在唤醒分类方面提高了约12%。本研究明显证明了所提出的cTL框架在给定数据存储库的情况下提高个体默认情绪分类性能的可行性。cTL框架可能会为使用更少的标记个体特定数据开发强大的情绪分类模型以实现现实生活中的情感脑机接口（ABCI）提供启示。
