P300-based Brain Computer Interface Mouse with Genetically-optimised Analogue Control.pdf

P300-based Brain Computer Interface Mouse with Genetically-optimised Analogue Control.pdf

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P300-based Brain Computer Interface Mouse with Genetically-optimised Analogue Control

P300-based Brain Computer Interface Mouse with Genetically-optimised Analogue Control Luca Citi Department of Computer Science University of Essex, UK and Scuola Sant’Anna Pisa, Italy Riccardo Poli Department of Computer Science University of Essex, UK Caterina Cinel Department of Psychology University of Essex, UK Francisco Sepulveda Department of Computer Science University of Essex, UK Technical Report CSM-451 Department of Computer Science University of Essex ISSN: 1744-8050 May 2006 Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering 1 Abstract In this paper we propose a BCI mouse based on the manipulation of P300 waves in EEG signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2–D motion of the pointer on the screen, using a novel BCI paradigm, was controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm. In the paper we illustrate our approach, describe our system, and report the results of the testing and validation of the system with 6 participants. The results are very promising, with all 6 participants being able to control the mouse with excellent accuracy only minutes after wearing the electrode cap despite their having no previous training with this system. 1 Introduction Over the past few years an increasing number of studies have evaluated the possibility of converting signals generated from the brain (especially EEG signals) into appropriate sig- nals for applications in various disciplines, from virtual reality to hands-free control of aug- mentative communication technologies for individuals with disabilities. Brain-Computer Interface (BCI) systems can measure specific signals of brain activity intentionally and unintentionally induced by the part

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