The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface

AbstractObjectiveThe 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).MethodsIn 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.ResultsParaplegic 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.ConclusionPresence of CNP improves BCI classification accuracy due to stronger and more distinct ERD.SignificanceResults of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD