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Thinker invariance: enabling deep neural networks for BCI across more people.
Journal of Neural Engineering ( IF 4 ) Pub Date : 2020-10-12 , DOI: 10.1088/1741-2552/abb7a7
Demetres Kostas 1 , Frank Rudzicz 1, 2
Affiliation  

Objective. Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal of this work is to frame these as a unified challenge and reconsider how transfer learning is used to overcome these difficulties. Approach . We present two variations of a holistic approach to transfer learning with DNNs for BCI that rely on a deeper network called TIDNet. Our approaches use multiple subjects for training in the interest of creating a more universal classifier that is applicable for new (unseen) subjects. The first approach is purely subject-invariant and the second targets specific subjects, without loss of generality. We use five publicly accessible datasets covering a range of tasks and compare our approaches to state-of-the-art alternatives in detail. Main results . We ob...

中文翻译:

思想家不变性:为更多人的 BCI 启用深度神经网络。

客观的。大多数用作脑机接口 (BCI) 分类器的深度神经网络 (DNN) 很少适用于一个以上的人,并且与更广泛的机器学习文献中的最新技术相比,它们相对较浅。这项工作的目标是将这些作为一个统一的挑战,并重新考虑如何使用迁移学习来克服这些困难。方法 。我们提出了两种使用 DNN 进行 BCI 迁移学习的整体方法的变体,它们依赖于称为 TIDNet 的更深网络。我们的方法使用多个主题进行训练,以创建适用于新(未见过)主题的更通用的分类器。第一种方法纯粹是主题不变的,第二种方法针对特定主题,不失一般性。我们使用了五个可公开访问的数据集,涵盖了一系列任务,并将我们的方法与最先进的替代方案进行了详细比较。主要结果。我们观察...
更新日期:2020-10-13
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