title: Decoding auditory EEG responses in healthy and clinical populations: A comparative study.
abstract: BACKGROUND
  Analyses of brain responses to external stimuli are typically based on the means computed across conditions. However in many cognitive and clinical applications, taking into account their variability across trials has turned out to be statistically more sensitive than comparing their means.
  
  
  METHODS
  In this study we present a novel implementation of a single-trial topographic analysis (STTA) for discriminating auditory evoked potentials at predefined time-windows. This analysis has been previously introduced for extracting spatio-temporal features at the level of the whole neural response. Adapting the STTA on specific time windows is an essential step for comparing its performance to other time-window based algorithms.
  
  
  RESULTS
  We analyzed responses to standard vs. deviant sounds and showed that the new implementation of the STTA gives above-chance decoding results in all subjects (in comparison to 7 out of 11 with the original method). In comatose patients, the improvement of the decoding performance was even more pronounced than in healthy controls and doubled the number of significant results.
  
  
  CONCLUSIONS
  We compared the results obtained with the new STTA to those based on a logistic regression in healthy controls and patients. We showed that the first of these two comparisons provided a better performance of the logistic regression; however only the new STTA provided significant results in comatose patients at group level.
  
  
  CONCLUSIONS
  Our results provide quantitative evidence that a systematic investigation of the accuracy of established methods in normal and clinical population is an essential step for optimizing decoding performance.
authors.affiliation: Laboratoire de recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, 1011 Lausanne, Switzerland; Radiology Department, Vaudois University Hospital and University of Lausanne, 1011, Switzerland; Center for Biomedical Imaging (CIBM) of Lausanne and Geneva, 1011, Switzerland. Electronic address: Marzia.De-Lucia@chuv.ch.
authors.forename: Marzia
authors.initials: M
authors.lastname: De Lucia
authors.affiliation: Laboratoire de recherche en Neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University and University Hospital, 1011 Lausanne, Switzerland; Radiology Department, Vaudois University Hospital and University of Lausanne, 1011, Switzerland; Center for Biomedical Imaging (CIBM) of Lausanne and Geneva, 1011, Switzerland.
authors.forename: Athina
authors.initials: A
authors.lastname: Tzovara
mesh_terms: D000465:Algorithms; D001307:Auditory Perception / Q000502:physiology*; D001921:Brain / Q000502:physiology* / Q000503:physiopathology*; D003128:Coma / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D005072:Evoked Potentials, Auditory; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted
keywords: Coma; Decoding; EEG; Logistic regression; MMN; Single-trial