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

Research output: Research - peer-reviewArticle

  • A. Vuckovic
  • M.A. Hasan
  • B. Osuagwu
  • M. Fraser
  • D.B. Allan
  • B.A. Conway
  • B. Nasseroleslami
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.
Original languageEnglish
Pages (from-to)2170-2180
Number of pages11
JournalClinical Neurophysiology
Volume126
Issue number11
Early online date30 Jan 2015
DOIs
StatePublished - 1 Nov 2015

    Research areas

  • EEG, motor imagery, paraplegia, central neuropathic pain, event related synchronisation/desynchronisation

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