title: Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.
authors.forename: L
authors.initials: L
authors.lastname: Hu
authors.forename: Z G
authors.initials: ZG
authors.lastname: Zhang
authors.forename: Y S
authors.initials: YS
authors.lastname: Hung
authors.forename: K D K
authors.initials: KD
authors.lastname: Luk
authors.forename: G D
authors.initials: GD
authors.lastname: Iannetti
authors.forename: Y
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010775:Photic Stimulation; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D058067:Wavelet Analysis; D055815:Young Adult