title: Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.
abstract: OBJECTIVE
  The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments.
  
  
  METHODS
  The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model.
  
  
  RESULTS
  Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context.
  
  
  CONCLUSIONS
  The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.
authors.affiliation: Aix-Marseille Université, CNRS, Centrale Marseille, I2M, UMR 7373, 13453 Marseille, France. Aix-Marseille Université, CNRS, LNC, UMR 7291, 13331 Marseille, France.
authors.forename: J
authors.initials: J
authors.lastname: Spinnato
authors.forename: M-C
authors.initials: MC
authors.lastname: Roubaud
authors.forename: B
authors.initials: B
authors.lastname: Burle
authors.forename: B
authors.initials: B
authors.lastname: Torrésani
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D015225:Magnetoencephalography / Q000379:methods; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis