Leveraging anatomical information to improve transfer learning in brain-computer interfaces.
Leveraging anatomical information to improve transfer learning in brain-computer interfaces.
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DOI：
10.1088/1741-2560/12/4/046027
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发表时间：
2015-08
影响因子：
4
通讯作者：
Lee AK
中科院分区：
文献类型：
--
作者：
Wronkiewicz M;Larson E;Lee AK
脑机接口（bci）代表了一种具有修复一系列创伤性和退行性神经系统疾病潜力的技术，但需要一个耗时的训练过程来校准。脑机接口（BCI）研究的一个领域被称为迁移学习，旨在通过循环利用以前记录的跨会议或科目的训练数据来加速训练。然而，训练数据通常从一种电极配置转移到另一种电极配置，而不考虑个人头部解剖结构或电极定位，这可能会充分利用回收数据。我们利用源成像来探索迁移学习，源成像估计皮层中的神经活动。与头皮记录相比，皮质活动的转移估计提供了一种补偿电极定位和头部形态在受试者和会话中的可变性的方法。基于模拟和测量的脑电图活动，我们使用专门从其他受试者转移的数据训练分类器，并实现了与基准分类器（代表现实世界的脑机接口）相当或超过的准确性。我们的结果表明，分类的改进取决于转移的试验数量和感兴趣的皮层区域。这些发现表明，基于皮质源的迁移学习是一种原则性的数据迁移方法，可以提高脑机接口分类性能，并为减少脑机接口校准时间提供了一条途径。
Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Based on simulated and measured EEG activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.