Online learning using projections onto shrinkage closed balls for adaptive brain-computer interface

摘要

Wearable/portable brain-computer interfaces (BCIs) for the long-term end use are a focus of recent BCI research. A challenge is how to update the BCI to meet changes in electroencephalography (EEG) signals, since the resource are so limited that retraining of traditional well-performed models, such as a support vector machine, is nearly impossible. To cope with this challenge, less-demanding adaptive online learning can be considered. We investigated an adaptive projected sub-gradient method (APSM) that is originated from the set theoretic estimation formulation and the projections onto convex sets theory. APSM provides a unifying framework for both adaptive classification and regression tasks. Coefficients of APSM are adjusted online as data arrive sequentially, with a regularization constraint made by projections onto a fixed closed ball. We extended the general APSM to a shrinkage form, where shrinkage closed balls were used instead of the original fixed one, expecting a more controllable fading effect and better adaptability. The convergence of shrinkage APSM was proved. It was also demonstrated that as shrinkage factor approached to 1, the limit point of shrinkage APSM would approach to the optimal solution with the least norm, which could be especially beneficial for generalization of the classifier. The performance of the proposed method was evaluated, and compared with those of the general APSM, the incremental support vector machine, and the passive aggressive algorithm, through an event-related potential-based BCI experiment. Results showed the advantage of the proposed method over the others on both the online classification performance and the easiness of tuning. Our study revealed the effectiveness of the proposed method for adaptive EEG classification, making it a promising tool for on-device training and updating of wearable/portable BCIs, as well as for application in other related fields, such as EEG-based biometrics. (C) 2019 Elsevier Ltd. All rights reserved.