title: A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection.
abstract: Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier was designed as the ERP (Event Related Potential) detector for its simplicity. Different dimensionality reduction and feature selection methods were applied and compared in a greedy wrapper framework. Experimental results showed that PCA with the first 10 principal components for each channel performed best and could be used in both online and offline systems.
authors.affiliation: Department of Science and Engineering, Oregon Health & Science University, Beaverton, Oregan, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Lan
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Lois
authors.initials: L
authors.lastname: Black
authors.forename: Jan
authors.initials: J
authors.lastname: Van Santen
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface