» Articles » PMID: 23673460

Discrimination Between Control and Idle States in Asynchronous SSVEP-based Brain Switches: a Pseudo-key-based Approach

Overview
Publisher IEEE
Date 2013 May 16
PMID 23673460
Citations 13
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface.

Sun Y, Liang L, Li Y, Chen X, Gao X Gigascience. 2024; 13.

PMID: 39110623 PMC: 11304967. DOI: 10.1093/gigascience/giae041.


Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.

Kim H, Won K, Ahn M, Jun S Biomed Eng Lett. 2024; 14(3):617-630.

PMID: 38645586 PMC: 11026332. DOI: 10.1007/s13534-024-00357-4.


A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.

Zhang Z, Li D, Zhao Y, Fan Z, Xiang J, Wang X Front Comput Neurosci. 2023; 17:1101726.

PMID: 36817318 PMC: 9929550. DOI: 10.3389/fncom.2023.1101726.


A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control.

Ha J, Park S, Im C, Kim L Sensors (Basel). 2021; 21(13).

PMID: 34283122 PMC: 8271393. DOI: 10.3390/s21134578.


A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control.

Zhu Y, Li Y, Lu J, Li P Front Neurorobot. 2020; 14:583641.

PMID: 33328950 PMC: 7714925. DOI: 10.3389/fnbot.2020.583641.