3.6
Impact Factor
This article is part of the Research Topic Closing the Loop Around Neural Systems

Original Research ARTICLE

Front. Neural Circuits, 25 February 2013 | http://dx.doi.org/10.3389/fncir.2013.00027

Assisted closed-loop optimization of SSVEP-BCI efficiency

  • Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

Keywords: brain-computer interface, brain-machine interface, activity-dependent stimulation, resting state EEG, resting state network, individual alpha frequency, BCI illiteracy, BCI performance predictor

Citation: Fernandez-Vargas J, Pfaff HU, Rodríguez FB and Varona P (2013) Assisted closed-loop optimization of SSVEP-BCI efficiency. Front. Neural Circuits 7:27. doi: 10.3389/fncir.2013.00027

Received: 31 October 2012; Paper pending published: 22 December 2012;
Accepted: 06 February 2013; Published online: 25 February 2013.

Edited by:

Steve M. Potter, Georgia Institute of Technology, USA

Reviewed by:

Attila Szücs, Balaton Limnological Research Institute HAS, Hungary
Pablo F. Diez, Universidad Nacional de San Juan, Argentina

Copyright © 2013 Fernandez-Vargas, Pfaff, Rodríguez and Varona. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Pablo Varona, Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 11, 28049 Madrid, Spain. e-mail: pablo.varona@uam.es