Paper Info
Title
A Cross-Subject Ssvep-Bci Based On Task Related Component Analysis
Abstract
SSVEP-BCIs have attracted extensive attention because of high information transfer rate. High-speed BCIs need to collect sufficient user's own data to train optimal subject-specific parameters. However, one of the challenges which limits the real-life application of BCIs is the time-consuming and tiring calibration process. This study developed two cross-subject frameworks. One of them uses data from all training subjects to train task-related component analysis based spatial filters (all-to-one, A2O), and the other uses data from each training subject to train task-related component analysis based spatial filters (one-to-one, O2O). Both of them do not need calibration process for a new user. The study further proposed O2O with threshold (O2O-Thr) to increase the reliability of recognition process. The proposed strategies can exploit information from existing subjects' SSVEP data and transfer it to new users. The performance of these methods was compared using an 8-class SSVEP dataset recorded from 10 subjects. O2O-Thr achieves average accuracy of 94.6% with data length of 1.5 seconds. The proposed methods have great potential for building subject-independent BCI that do not require any calibration data from new users, which make BCI more practical and user-friendly.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857064
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Training set,Computer vision,Information transfer,Computer science,Brain–computer interface,Exploit,Artificial intelligence,Decoding methods,Component analysis,Calibration,Machine learning
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Wentao Liu111014.31
Yufeng Ke200.68
Pengxiao Liu301.01
Jiale Du401.01
Linghan Kong501.01
Shuang Liu63622.95
Xingwei An72111.88
Dong Ming810551.47