Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

TL;DR

This paper introduces MEKT, a novel manifold-based transfer learning method for EEG classification in BCIs, effectively aligning data across subjects and reducing computational costs while maintaining high accuracy.

Contribution

The paper presents MEKT, a new manifold embedded knowledge transfer approach for cross-subject EEG classification, along with DTE for source domain selection, improving efficiency and performance.

Findings

MEKT outperforms state-of-the-art transfer learning methods.

DTE reduces computational cost by over 50%.

MEKT achieves high classification accuracy across multiple EEG datasets.

Abstract

Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject electroencephalogram (EEG) classification, i.e., we have labeled EEG trials from one or more source subjects, but only unlabeled EEG trials from the target subject. We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between the source and the target domains, while preserving their geometric structures. MEKT can cope with one or multiple source domains, and can be computed efficiently. We also propose a domain transferability estimation (DTE) approach to identify the most beneficial source domains, in case there are a large number of source domains. Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.