Transfer Learning in Brain-Computer Interfaces
Transfer Learning in Brain-Computer Interfaces
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DOI：
10.1109/mci.2015.2501545
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发表时间：
2016-02-01
影响因子：
9
通讯作者：
Grosse-Wentrup, Moritz
中科院分区：
文献类型：
--
作者：
Jayaram, Vinay;Alamgir, Morteza;Grosse-Wentrup, Moritz
脑机接口（BCI）的性能随着可用训练数据的数量而提高;然而，这些数据的统计分布在不同的受试者之间以及个体受试者的不同会话之间有所不同，限制了训练数据或训练模型在它们之间的可转移性。在这篇文章中，我们回顾了当前BCI中的迁移学习技术，这些技术利用多个主题和/或会话的训练数据之间的共享结构来提高性能。然后，我们提出了一个框架，用于迁移学习的背景下，脑机接口，可以应用于任何任意的特征空间，以及一种新的回归估计方法，是专门设计的系统的结构基于脑电图（EEG）。我们证明了我们的框架和方法的效用主题到主题的转移在运动图像范例，以及会话到会话的转移在一名患者诊断为肌萎缩侧索硬化症（ALS），表明它是能够优于其他可比的方法在同一数据集。
The performance of brain-computer interfaces (BCIs) improves with the amount of available training data; the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.