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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 731046, 9 pages
Research Article

Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface

1Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy
2I.R.C.C.S. San Camillo Hospital Foundation, Via Alberoni 70, 30126 Venice, Italy

Received 17 April 2014; Revised 18 June 2014; Accepted 18 June 2014; Published 7 July 2014

Academic Editor: Fabio Babiloni

Copyright © 2014 Anahita Goljahani 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.


A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system’s setup and maintenance by lowering the number of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing to 1 without affecting the system’s accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number of channels encourages further development of the present study, for example, in an online setting.