title: A parametric feature extraction and classification strategy for brain-computer interfacing.
abstract: Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.
authors.affiliation: Department of Electronic and Electrical Engineering, University College Dublin, Dublin 4, Ireland. david.burke@ucd.ie
authors.forename: Dave P
authors.initials: DP
authors.lastname: Burke
authors.forename: Simon P
authors.initials: SP
authors.lastname: Kelly
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Richard B
authors.initials: RB
authors.lastname: Reilly
authors.forename: Ciarán
authors.initials: C
authors.lastname: Finucane
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003143:Communication Devices for People with Disabilities; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface