title: Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach.
abstract: A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) can operate as an asynchronous brain switch. When SSVEP is detected with the "on/off" button flickering at a fixed frequency, the subject is identified as in the control state. Otherwise, he is in the idle state. Generally, the detection of the idle/control state is based on a predefined threshold, which is related to power. However, due to the variability of the electroencephalogram (EEG) signal, it is difficult to find an optimal threshold to achieve a high true-positive rate (TPR) in the control state while maintaining a low false-positive rate (FPR) in the idle state. In this paper, a novel pseudo-key-based approach is presented for better discriminating the control and idle states. A dedicated "on/off" button (target key) and several additional buttons (pseudo-keys) are displayed on the graphical user interface (GUI), and all of these buttons flash at different frequencies. The control state is identified from the EEG signal under two conditions. The first is a common thresholding condition, where the power ratio of the target key frequency component to a certain neighboring frequency band is above a predefined threshold. The second is a comparison condition, where the power of the target key frequency component is higher than any of the pseudo-keys. The effectiveness of the proposed approach is validated by several experiments. Further analysis shows that introducing the pseudo-keys can significantly reduce the probability that the SSVEP will be detected in response to the flickering target key in the idle state without substantially affecting the detection in the control state, providing strong evidence in support of our approach.
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
authors.forename: Jiahui
authors.initials: J
authors.lastname: Pan
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Gu
authors.forename: Feng
authors.initials: F
authors.lastname: Li
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003657:Decision Making / Q000502:physiology; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation / Q000379:methods*; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology*; D012680:Sensitivity and Specificity; D014796:Visual Perception / Q000502:physiology*