Identifying oscillations under multi-site sensory stimulation for high-level peripheral nerve injured patients: a pilot study. (1st June 2022)

• Record Type: Journal Article
• Authors: Geng, Yanjuan; Qin, Liuni; Li, Yongcheng; Yu, Zhebin; Li, Linling; Asogbon, Mojisola Grace; Zhan, Yang; Yan, Nan; Guo, Xin; Li, Guanglin
• Is Part Of: Journal of neural engineering. Volume 19:Number 3(2022)
• Publication Date: 2022-06-01
• Subjects: peripheral nerve injury (PNI) -- sensory dysfunction -- brain oscillations -- EEG -- sensory stimulation -- transcutaneous electrical nerve stimulation (TENS); Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac7079
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. For high-level peripheral nerve injuryed (PNI) patients with severe sensory dysfunction of upper extremities, identifying the multi-site tactile stimulation is of great importance to provide neurorehabilitation with sensory feedback. In this pilot study, we showed the feasibility of identifying multi-site and multi-intensity tactile stimulation in terms of electroencephalography (EEG). Approach. Three high-level PNI patients and eight non-PNI participants were recruited in this study. Four different sites over the upper arm, forearm, thumb finger and little finger were randomly stimulated at two intensities (both sensory-level) based on the transcutaneous electrical nerve stimulation. Meanwhile, 64-channel EEG signals were recorded during the passive tactile sense stimulation on each side. Main results. The spatial-spectral distribution of brain oscillations underlying multi-site sensory stimulation showed dominant power attenuation over the somatosensory and prefrontal cortices in both alpha-band (8–12 Hz) and beta-band (13–30 Hz). But there was no significant difference among different stimulation sites in terms of the averaged power spectral density over the region of interest. By further identifying different stimulation sites using temporal-spectral features, we found the classification accuracies were all above 89% for the affected arm of PNI patients, comparable to that from their intact side and that from the non-PNI group. When the stimulation site-intensity combinations were treated as eight separate classes, the classification accuracies were ranging from 88.89% to 99.30% for the affected side of PNI subjects, similar to that from their non-affected side and that from the non-PNI group. Other performance metrics, including specificity, precision, and F1-score, also showed a sound identification performance for both PNI patients and non-PNI subjects. Significance. These results suggest that reliable brain oscillations could be evoked and identified well, even though induced tactile sense could not be discerned by the PNI patients. This study have implication for facilitating bidirectional neurorehabilitation systems with sensory feedback.