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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 Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based brain computer interface

Vuckovic, A. and Hasan, M.A. and Osuagwu, B. and Fraser, M. and Allan, D.B. and Conway, B.A. and Nasseroleslami, B. (2015) The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based brain computer interface. Clinical Neurophysiology, 126 (11). pp. 2170-2180. ISSN 1388-2457

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The aim of this study was to test how the presence of central neuropathic pain (CNP) influences the performance of a motor imagery based Brain Computer Interface (BCI). In this electroencephalography (EEG) based study, we tested BCI classification accuracy and analysed event related desynchronisation (ERD) in 3 groups of volunteers during imagined movements of their arms and legs. The groups comprised of nine able-bodied people, ten paraplegic patients with CNP (lower abdomen and legs) and nine paraplegic patients without CNP. We tested two types of classifiers: a 3 channel bipolar montage and classifiers based on common spatial patterns (CSPs), with varying number of channels and CSPs. Paraplegic patients with CNP achieved higher classification accuracy and had stronger ERD than paraplegic patients with no pain for all classifier configurations. Highest 2-class classification accuracy was achieved for CSP classifier covering wider cortical area: 82 ± 7% for patients with CNP, 82 ± 4% for able-bodied and 78 ± 5% for patients with no pain. Presence of CNP improves BCI classification accuracy due to stronger and more distinct ERD. Results of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD.