Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering

Clin Neurophysiol. 2011 Jul;122(7):1429-39. doi: 10.1016/j.clinph.2010.12.052. Epub 2011 Feb 4.

Abstract

Objective: To develop an effective approach for enhancing the signal-to-noise ratio (SNR) and identifying single-trial short-latency somatosensory evoked potentials (SEPs) from multi-channel electroencephalography (EEG).

Methods: 128-channel SEPs elicited by electrical stimuli of the left posterior tibial nerve were recorded from 11 healthy subjects. Probabilistic independent component analysis (PICA) was used as a spatial filter to isolate SEP-related independent components (ICs), and wavelet filtering was used as a time-frequency filter to further enhance the SNR of single-trial SEPs.

Results: SEP-related ICs, identified using PICA, showed typical patterns of cortical SEP complex (P39-N50-P60) and scalp topography (centrally distributed with the spatial peak located near vertex). In addition, wavelet filtering significantly enhanced the SNR of single-trial SEPs (p=0.001).

Conclusions: Combining PICA and wavelet filtering offers a space-time-frequency filter that can be used to enhance the SNR of single-trial SEPs greatly, thus providing a reliable estimation of single-trial SEPs.

Significance: This method can be used to detect single-trial SEPs and other types of evoked potentials (EPs) in various sensory modalities, thus facilitating the exploration of single-trial dynamics between EPs, behavioural variables (e.g., intensity of perception), as well as abnormalities in intraoperative neurophysiological monitoring.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Bayes Theorem
  • Data Interpretation, Statistical
  • Electroencephalography / statistics & numerical data
  • Evoked Potentials, Somatosensory / physiology*
  • Female
  • Humans
  • Male
  • Models, Statistical
  • Photic Stimulation
  • Principal Component Analysis
  • Reproducibility of Results
  • Wavelet Analysis*
  • Young Adult