Paper Info
Title
Enhancing Performance Of Ssvep-Based Bci By Unsupervised Learning Information From Test Trials
Abstract
Steady-State Visual Evoked Potentials (SSVEPs) have become one of the most used neural signals for brain-computer interfaces (BCIs) due to their stability and high signal-to-noise rate. However, the performance of SSVEP-based BCIs would degrade with a few training samples. This study was proposed to enhance the detection of SSVEP by combining the supervised learning information from training samples and the unsupervised learning information from the trial to be tested. A new method, i.e. cyclic shift trials (CST), was proposed to generate new calibration samples from the test data, which were furtherly used to create the templates and spatial filters of task-related component analysis (TRCA). The test-trial templates and spatial filters were combined with training-sample templates and spatial filters to recognize SSVEP. The proposed algorithm was tested on a benchmark dataset. As a result, it reached significantly higher classification accuracy than traditional TRCA when only two training samples were used. Specifically, the accuracy was improved by 9.5% for 0.7s data. Therefore, this study demonstrates CST is effective to improve the performance of SSVEP-BCI.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176851
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
Keywords
DocType
Volume
Steady-State Visual Evoked Potentials (SSVEPs), Brain-Computer Interfaces (BCIs), cyclic shift trials(CST), task-related component analysis (TRCA)
Conference
2020
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Lijie Wang100.68
Minpeng Xu22717.17
Jie Mei300.68
Jing Han4228.06
Yijun Wang530846.68
Tzyy-Ping Jung61410202.52
Dong Ming710551.47