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

OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: Presence of CNP improves BCI classification accuracy due to stronger and more distinct ERD. SIGNIFICANCE: Results of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD.