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Computational Intelligence and Neuroscience
Volume 2009, Article ID 864564, 11 pages
Research Article

A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication

1Bioengineering Department, Politecnico di Milano University, 20133 Milan, Italy
2INDACO Department, Politecnico di Milano University, 20133 Milan, Italy
3IRCCS Eugenio Medea “La Nostra Famiglia”, 23842 Bosisio Parini, Lecco, Italy

Received 11 August 2008; Revised 30 December 2008; Accepted 5 February 2009

Academic Editor: Li Yuanqing

Copyright © 2009 Sergio Parini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.