title: Boosting linear logistic regression for single trial ERP detection in rapid serial visual presentation tasks.
abstract: In this paper, we employ the AdaBoost algorithm to the linear logistic regression model to detect encephalography (EEG) signatures, called evoked response potentials of visual recognition events in a single trial. In the experiments, a large amount of images were displayed at a very high presentation rate, named rapid serial visual presentation. The EEG was recorded using 32 electrodes during the rapid image presentation. Subjects were instructed to click the mouse when they recognize a target image. The results demonstrated that the boosting method improves the detection performance compared with the base classifier by approximately 3% as measured by area under the ROC curve.
authors.affiliation: Comput. Sci. & Electr. Eng. Dept., Oregon Health & Sci. Univ., Portland, OR 97239, USA. huang@csee.ogi.edu
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Huang
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Santosh
authors.initials: S
authors.lastname: Mathan
authors.forename: Misha
authors.initials: M
authors.lastname: Pavel
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods / Q000706:statistics & numerical data; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008959:Models, Neurological; D010775:Photic Stimulation; D012372:ROC Curve