Paper Info
Title
A Two-Step Idle-State Detection Method For Ssvep Bci
Abstract
The role of detecting work/idle state in asynchronous Steady-state visual evoked potential (SSVEP) Brain-computer interface (BCI) or a self-paced SSVEP BCI has received increased attention in recent years. This study proposed a tree structure method which identifies the work/idle state based on the frequency recognition to detect work/idle state. Firstly, a frequency recognition estimated with task-related component analysis (TRCA). Then, the work/idle state is classified with step-wise linear discriminant analysis (SWLDA) using the data fusion of TRCA scores and power spectral density (PSD) as features. This method was evaluated by Electroencephalography (EEG) data from fourteen healthy participants with eight frequencies as work states and three idle state conditions. The averaged AUC of this method achieved 0.89 with data lengths of one second, which was significantly higher than that of the conventional power spectrum-based algorithm. The proposed method could identify the work/idle state fast and accurately, making the SSVEP BCI better suited for practical application.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857024
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
Electroencephalogram (EEG), steady-state visual evoked potential (SSVEP), data fusion, two-step, idle state, Brain-computer interface (BCI)
Asynchronous communication,Computer vision,Pattern recognition,Computer science,Idle,Brain–computer interface,Sensor fusion,Spectral density,Artificial intelligence,Tree structure,Linear discriminant analysis,Statistical classification
Conference
Volume
ISSN
Citations 
2019
1557-170X
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Jiale Du101.01
yufeng ke217.78
Pengxiao Liu301.01
Wentao Liu411014.31
Linghan Kong501.01
Ningci Wang601.01
Minpeng Xu72717.17
Xingwei An82111.88
Dong Ming910551.47