Multi-Subject Unsupervised Transfer with Weighted Subspace Alignment for Common Spatial Patterns

Motor imagery classification is known to be highly user dependent. Subspace alignment has been somewhat successful in allowing for unsupervised transfer from one training user to a new user. In this paper we develop a method to weight contributions from subspace alignment to multiple training users to give improved unsupervised transfer performance on the new test user. Ablation analyses show that both the subspace alignment and weighting are critical for improved performance. We also discuss how weighting uses the labels of the training users to better interpret subspace alignment.

运动想象分类已知高度依赖用户。子空间对齐在允许从一个训练用户到新用户的无监督迁移方面取得了一定的成功。在本文中，我们开发了一种方法，对来自子空间对齐的多个训练用户的贡献进行加权，以提高在新测试用户上的无监督迁移性能。消融分析表明，子空间对齐和加权对于提高性能都至关重要。我们还讨论了加权如何利用训练用户的标签来更好地解释子空间对齐。