现实世界BCI:跨领域学习和实际应用

S. Gordon, Matthew Jaswa, Amelia J. Solon, Vernon J. Lawhern
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引用次数: 14

摘要

为了开发现实世界的BCI解决方案,机器学习模型不仅要泛化到不可见的用户,还要泛化到不可见的场景。在这篇概念论文中,我们描述了我们对深度学习工具的初步研究,以创建跨学科和跨领域学习的广义模型。我们使用两个不同的实验室级数据集来训练学习模型,然后将其应用于第三个更复杂的场景。虽然我们的研究结果表明跨领域学习是可能的,但我们也确定了进一步研究和发展的潜在途径(例如解开空间或时间重叠的响应)。最后,我们描述了我们实现一个系统的工作,该系统使用跨领域学习来开发一个实时应用程序,用于执行基于bci的以人为中心的场景分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real World BCI: Cross-Domain Learning and Practical Applications
In order to develop real-world BCI solutions machine learning models must generalize not only to unseen users but also to unseen scenarios. In this concept paper we describe our initial investigation into Deep Learning tools to create generalized models for both cross-subject and cross-domain learning. We demonstrate our approach using two different, laboratory grade data sets to train a learning model that we then apply to a third more complex scenario. While our results indicate that cross-domain learning is possible, we also identify potential avenues for further research and development (such as disentangling spatially or temporally overlapping responses). Finally, we describe our work to implement a system that uses cross-domain learning to develop a real-time application for performing BCI-based Human-Centric Scene Analysis.
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