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World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

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Predicting intention and direction of wrist movement from EEG

Valsan, G. and Worrajiran, P. and Lakany, H. and Conway, B.A. (2006) Predicting intention and direction of wrist movement from EEG. In: Advances in Medical, Signal and Information Processing, 2006. MEDSIP 2006. IET 3rd International Conference On. IET Conference Publications (520). Institution of Engineering and Technology, Stevenage, United Kingdom, p. 30. ISBN 978-0-86341-658-3

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Abstract

Brain-computer interfaces (BCI) offer potential for individuals with a variety of movement and sensory disabilities to control their environment, communicate and control mobility aids. However, the key to BCI usability rests in being able to extract relevant time varying signals that can be classified into usable commands. In this study we report on the results of experiments investigating the ability to classify scalp EEG signals on the basis of a users intention to move (and imaging to move) their wrist in different directions. EEG activity recorded from the scalp overlying the sensorimotor cortex was examined in the frequency domain to identify pre-movement patterns of synchronisation and desynchronization. Based on this, a further classification of the EEG epochs was performed based on Principal Component Analysis for feature extraction and Euclidean distance for intention classification. Classification success rates between 70-90% have been obtained using this relatively simple method suggesting that classification of pre-movement potentials can realistically be achieved in real time.