Research article summary (published 30 May 2008):

Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models.

Full Abstract

Asynchronous control is an important issue for brain-computer interfaces (BCIs) working in real-life settings, where the machine should determine from brain signals not only the desired command but also when the user wants to input it. In this paper, we propose a novel computational approach for robust asynchronous control using electroencephalogram (EEG) and a P300-based oddball paradigm. In this approach, we first address the mathematical modeling of target P300, nontarget P300, and noncontrol signals, by using Gaussian distribution models in a support vector margin space. Furthermore, we derive a method to compute the likelihood of control state in a time window of EEG. Finally, we devise a recursive algorithm to detect control states in ongoing EEG for online application. We conducted experiments with four subjects to study both the asynchronous BCI's receiver operating characteristics and its performance in actual online tests. The results show that the BCI is able to achieve an averaged information transfer rate of approximately 20 b/min at a low false positive rate (one event per minute).

Author information

Author/s: Zhang, Haihong (H); Guan, Cuntai (C); Wang, Chuanchu (C);

Affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 119613, Singapore. hhzhang(-atsign-)i2r.a-star.edu.sg

Journal and publication information

Publication Type: Evaluation Studies; Journal Article

Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng), published in United States. (Language: eng)

Reference: 2008-Jun; vol 55 (issue 6) : pp 1754-63

Dates: Created 2008/08/21; Completed 2008/09/17;

PMID: 18714840, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Artificial Intelligence Brain - physiology Brain Mapping - methods Computer Simulation Data Interpretation, Statistical Electroencephalography - methods

Event-Related Potentials, P300 - physiology Humans Models, Neurological Models, Statistical Pattern Recognition, Automated - methods User-Computer Interface

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