title: Optimal EEG feature selection from average distance between events and non-events.
abstract: Biosignal classification systems often have to deal with extraneous features, highly imbalanced datasets, and a low SNR. A robust feature selection/reduction method is a crucial step in this process. Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15-Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from a total of eight subjects were used in the generation of the artificial data. Several feature reduction and classifier structures were investigated. Taking only a single feature corresponding to the maximum of average distance between events and non-events (ADEN) on unbalanced data yielded a phi correlation of 0.94 on the mock data with an SNR of 0.3, compared with a phi coefficient of 0.00 for principal component analysis (PCA). ADEN consistently outperformed alternative system configurations, independent of the classifier utilized. While ADEN's high performance may be due to the nature of the artificial dataset, this simulation has demonstrated strong potential compared to other feature selection/reduction methods.
authors.forename: John
authors.initials: J
authors.lastname: LaRocco
authors.forename: Carrie R H
authors.initials: CR
authors.lastname: Innes
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Bones
authors.forename: Stephen
authors.initials: S
authors.lastname: Weddell
authors.forename: Richard D
authors.initials: RD
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D059629:Signal-To-Noise Ratio; D012890:Sleep / Q000502:physiology