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

Zhining Chen, Mahta Mousavi, Virginia R. de Sa

Abstract: 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.