SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

This article is part of the series Trends in Brain Computer Interfaces.

Open Access Research Article

Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

E Lalor1*, SP Kelly12, C Finucane3, R Burke4, R Smith1, RB Reilly1 and G McDarby1

Author Affiliations

1 School of Electrical, Electronic and Mechanical Engineering, University College Dublin, Belfield, Dublin 4, Ireland

2 The Cognitive Neurophysiology Laboratory, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA

3 Medical Physics and Bioengineering, St. James's Hospital, P.O. Box 580, Dublin 8, Ireland

4 EOC Operations Center, Microsoft Corporation, Sandyford Industrial Estate, Dublin 18, Ireland

For all author emails, please log on.

EURASIP Journal on Advances in Signal Processing 2005, 2005:706906  doi:10.1155/ASP.2005.3156

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/19/706906

Received:2 February 2004
Revisions received:19 October 2004
Published:17 November 2005

© 2005 Lalor et al.

This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

EEG; BCI; SSVEP; online classification; overt attention

Research Article