Self-calibration algorithm in an asynchronous P300-based brain-computer interface

F. Schettini, F. Aloise, P. Aricò, S. Salinari, D. Mattia, F. Cincotti

Research output: Contribution to journalArticle

Abstract

Objective. Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data. Approach. Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. Main results. Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. Significance. Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.

Original languageEnglish
Article number035004
JournalJournal of Neural Engineering
Volume11
Issue number3
DOIs
Publication statusPublished - 2014

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Calibration
Communication
Computer Systems
Labels
Suspensions
Healthy Volunteers
Classifiers

Keywords

  • Asynchronous control
  • Brain-computer interface (BCI)
  • P300 event-related potential
  • Self-calibration algorithm
  • Unsupervised calibration

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience
  • Medicine(all)

Cite this

Self-calibration algorithm in an asynchronous P300-based brain-computer interface. / Schettini, F.; Aloise, F.; Aricò, P.; Salinari, S.; Mattia, D.; Cincotti, F.

In: Journal of Neural Engineering, Vol. 11, No. 3, 035004, 2014.

Research output: Contribution to journalArticle

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