Title: Transfer Learning for Brain-Computer Interfaces: An Euclidean Space Data Alignment Approach,
Abstract: Almost all EEG-based brain-computer interfaces (BCIs) need some labeled
subject-specific data to calibrate a new subject, as neural responses are
different across subjects to even the same stimulus. So, a major challenge in
developing high-performance and user-friendly BCIs is to cope with such
individual differences so that the calibration can be reduced or even
completely eliminated. This paper focuses on the latter. More specifically, we
consider an offline application scenario, in which we have unlabeled EEG trials
from a new subject, and would like to accurately label them by leveraging
auxiliary labeled EEG trials from other subjects in the same task. To
accommodate the individual differences, we propose a novel unsupervised
approach to align the EEG trials from different subjects in the Euclidean space
to make them more consistent. It has three desirable properties: 1) the aligned
trial lie in the Euclidean space, which can be used by any Euclidean space
signal processing and machine learning approach; 2) it can be computed very
efficiently; and, 3) it does not need any labeled trials from the new subject.
Experiments on motor imagery and event-related potentials demonstrated the
effectiveness and efficiency of our approach.