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Towards Asynchronous Brain-computer Interfaces: A P300-based Approach with Statistical Models

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3 Author(s)
Haihong Zhang ; Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. Email: hhzhang@i2r.a-star.edu.sg ; Chuanchu Wang ; Cuntai Guan

Asynchronous control is a critical issue in developing brain-computer interfaces for real-life applications, where the machine should be able to detect the occurrence of a mental command. In this paper we propose a computational approach for robust asynchronous control using the P300 signal, in a variant of oddball paradigm. First, we use Gaussian models in the support vector margin space to describe various types of EEG signals that are present in an asynchronous P300-based BCI. This allows us to derive a probability measure of control state given EEG observations. Second, we devise a recursive algorithm to detect and locate control states in ongoing EEG. Experimental results indicate that our system allows information transfer at approx. 20 bit/min at low false alarm rate (1/min).

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007