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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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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.