title: An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.
abstract: Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.
authors.affiliation: Department of Psychology, Tsinghua University, Room 334, Ming Zhai Building, Beijing 100084, P. R. China.
authors.forename: Dan
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P. R. China.
authors.forename: Bisheng
authors.initials: B
authors.lastname: Huang
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, No. 3 Building, Wushan Road, Tianhe District, Guang Zhou City, P. R. China.|Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA 94304, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P. R. China.
authors.forename: Siliang
authors.initials: S
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation; D012372:ROC Curve; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult
keywords: Steady-state visual evoked potentials; idle state detection; maximum evoked response; spatial filtering