title: The t-CWT: a new ERP detection and quantification method based on the continuous wavelet transform and Student's t-statistics.
abstract: OBJECTIVE
  This study was aimed at developing a method for extraction and assessment of event-related brain potentials (ERP) from single-trials. This method should be applicable in the assessment of single persons' ERPs and should be able to handle both single ERP components and whole waveforms.
  
  
  METHODS
  We adopted a recently developed ERP feature extraction method, the t-CWT, for the purposes of hypothesis testing in the statistical assessment of ERPs. The t-CWT is based on the continuous wavelet transform (CWT) and Student's t-statistics. The method was tested in two ERP paradigms, oddball and semantic priming, by assessing individual-participant data on a single-trial basis, and testing the significance of selected ERP components, P300 and N400, as well as of whole ERP waveforms. The t-CWT was also compared to other univariate and multivariate ERP assessment methods: peak picking, area computation, discrete wavelet transform (DWT) and principal component analysis (PCA).
  
  
  RESULTS
  The t-CWT produced better results than all of the other assessment methods it was compared with.
  
  
  CONCLUSIONS
  The t-CWT can be used as a reliable and powerful method for ERP-component detection and testing of statistical hypotheses concerning both single ERP components and whole waveforms extracted from either single persons' or group data.
  
  
  CONCLUSIONS
  The t-CWT is the first such method based explicitly on the criteria of maximal statistical difference between two average ERPs in the time-frequency domain and is particularly suitable for ERP assessment of individual data (e.g. in clinical settings), but also for the investigation of small and/or novel ERP effects from group data.
authors.affiliation: Institute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Gartenstr. 29, D-72074 Tubingen, Germany. bostanov@uni-tuebingen.de <bostanov@uni-tuebingen.de>
authors.forename: Vladimir
authors.initials: V
authors.lastname: Bostanov
authors.forename: Boris
authors.initials: B
authors.lastname: Kotchoubey
mesh_terms: D000161:Acoustic Stimulation / Q000379:methods; D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors