Enhancement of Signal-to-noise Ratio of Peroneal Nerve Somatosensory Evoked Potential Using Independent Component Analysis and Time-Frequency Template

This study aims to recover the somatosensory evoked potentials (SSEPs) from the smearing electroencephalography (EEG) recordings using independent component analysis (ICA) in conjunction with the proposed timefrequency SSEP template (TF-SSEP). The SSEPs induced from patients with the impaired motor functions exhibit longer latency and lower amplitude than the normal SSEPs and are inevitably contaminated by artifacts and environmental noise. Although ICA has been demonstrated as a novel technique to segregate the EEG into independent sources, the selection of task-related components needs to be further elaborated. The TF-SSEP template, generated by the Morelet wavelet transformation of the averaged SSEPs from three normal subjects, was used to automatically extract the SSEP-related features. The performance of the TF-SSEP template was further validated using EEGs through the left and right peroneal nerve stimulation of four stroke patients. After ICA decomposition, the sources were selected for reconstruction if their correlation coefficients with the TF-SSEP template were higher than the predetermined threshold. On the other hand, the unselected sources were considered as the event-unrelated components or artifacts. Among all patients, the topography maps at four peak times, namely P40, N45, P60 and N75, showed higher contrast in the vicinity of the foot-associated motor area, and the resolved SSEPs demonstrated uncontaminated waveforms in comparison with the conventionally averaging method. This indicated that the proposed method can remarkably suppress artifacts and effectively extracted the SSEP-related features.