title: Learning from feedback training data at a self-paced brain-computer interface.
abstract: Inherent changes that appear in brain signals when transferring from calibration to feedback sessions are a challenging but critical issue in brain-computer interface (BCI) applications. While previous studies have mostly focused on the adaptation of classifiers, in this paper we study the feasibility and the importance of the adaptation of feature extraction in a self-paced BCI paradigm. First, we conduct calibration and feedback training on able-bodied naïve subjects using a new self-paced motor imagery BCI including the idle state. The online results suggest that the feature space constructed from calibration data may become ineffective during feedback sessions. Hence, we propose a new supervised method that learns from a feedback session to construct a more appropriate feature space, on the basis of the maximum mutual information principle between feedback signal, target signal and EEG. Specifically, we formulate the learning objective as maximizing a kernel-based mutual information estimate with respect to the spatial-spectral filtering parameters. We then derive a gradient-based optimization algorithm for the learning task. An experimental study is conducted using offline simulation. The results show that the proposed method is able to construct effective feature spaces to capture the discriminative information in feedback training data and, consequently, the prediction error can be significantly reduced using the new features.
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632. hhzhang@i2r.a-star.edu.sg
authors.forename: Haihong
authors.initials: H
authors.lastname: Zhang
authors.forename: Sidath Ravindra
authors.initials: SR
authors.lastname: Liyanage
authors.forename: Chuanchu
authors.initials: C
authors.lastname: Wang
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D002138:Calibration; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D005240:Feasibility Studies; D005246:Feedback; D006801:Humans; D009862:Online Systems; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface