title: Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems.
abstract: We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.
authors.forename: Lenis M
authors.initials: LM
authors.lastname: Merino
authors.forename: Tapsya
authors.initials: T
authors.lastname: Nayak
authors.forename: Garrett
authors.initials: G
authors.lastname: Hall
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Pack
authors.lastname: Yufei Huang
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005071:Evoked Potentials; D005074:Evoked Potentials, Visual; D006801:Humans