Transfer Learning in Brain-Computer Interfaces
This work addresses the challenge of data variability in BCIs, which is crucial for patients with conditions like ALS, but it appears incremental as it builds on existing transfer learning techniques.
The paper tackles the problem of limited transferability of training data or models across subjects and sessions in brain-computer interfaces (BCIs) by proposing a novel framework and regression method, demonstrating improved performance in subject-to-subject and session-to-session transfer tasks.
The performance of brain-computer interfaces (BCIs) improves with the amount of available training data, the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.