title: Single trial P300 detection based on the Empirical Mode Decomposition.
abstract: We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic analysis is used to evaluate our method's performance.
authors.affiliation: Department of Electrical Engineering, University Autonoma Metropolitana-Itzapalapa, Mexico City, Mexico.
authors.forename: Teodoro
authors.initials: T
authors.lastname: Solis-Escalante
authors.forename: Gerardo Gabriel
authors.initials: GG
authors.lastname: Gentiletti
authors.forename: Oscar
authors.initials: O
authors.lastname: Yañez-Suarez
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity