Paper Info
Title
Selective Subject Pooling Strategy To Improve Model Generalization For A Motor Imagery Bci
Abstract
Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.
Year
DOI
Venue
2021
10.3390/s21165436
SENSORS
Keywords
DocType
Volume
BCI, motor imagery, zero-training, selective training
Journal
21
Issue
ISSN
Citations 
16
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Kyungho Won101.69
Moonyoung Kwon202.37
Minkyu Ahn300.68
Sung Chan Jun401.35