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Calibration time reduction for motor imagery-based BCI using batch mode active learning

Version 2 2024-06-05, 02:10
Version 1 2019-06-19, 15:07
conference contribution
posted on 2024-06-05, 02:10 authored by Ibrahim HossainIbrahim Hossain, Abbas KhosraviAbbas Khosravi, Imali HettiarachchiImali Hettiarachchi, S Nahavandi
Brain-computer interface (BCI) has enormous potential applications from rehabilitation of neural disease patient to driver's drowsiness prediction. However, the feasibility of BCI in the practical application other than laboratory prototype is dependent on the usability of a human subject on the go. Most of the time, it needs long and in-depth calibration of the system for training purpose. Different novel methods have been proposed to shorten the calibration time maintaining the robust performance. One of them is active learning (AL) which asks for labeling the training samples and it has the potential to reach robust performance using reduced informative training set. In this work, one of the AL methods, query by committee (QBC) is applied by three state-of-the-art feature extraction methods coupled with linear discriminant analysis classifier for motor imagery-based BCI. The joint accuracy by the members of QBC has obtained the baselines using maximum 33% of the whole training set. It also shows a significant difference at the 5% significance level from contemporary AL methods and random sampling method. Thus, QBC has reduced the labeling effort as well as the training data collection effort significantly more than that of random labeling process. It infers that QBC is a potential candidate for abridging overall calibration time of BCI systems.

History

Pagination

1-8

Location

Rio de Janeiro, Brazil

Start date

2018-07-08

End date

2018-07-13

ISBN-13

9781509060146

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IJCNN : Proceedings of the 2018 International Joint Conference on Neural Networks

Event

International Neural Network Society. Conference (2018 : Rio de Janeiro, Brazil)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

International Neural Network Society Conference