title: Single trial analysis of slow cortical potentials: a study on anticipation related potentials.
abstract: OBJECTIVE
  Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering.
  
  
  METHODS
  We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days.
  
  
  RESULTS
  We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers.
  
  
  CONCLUSIONS
  We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.
authors.affiliation: Chair on Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne, Station 11, 1015 Lausanne, Switzerland. Gangadhar.Garipelli@gmail.com
authors.forename: Gangadhar
authors.initials: G
authors.lastname: Garipelli
authors.forename: Ricardo
authors.initials: R
authors.lastname: Chavarriaga
authors.forename: José del R
authors.initials: Jdel R
authors.lastname: Millán
mesh_terms: D000328:Adult; D000465:Algorithms; D059031:Anticipation, Psychological / Q000502:physiology*; D001288:Attention / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology*; D003463:Cues; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted