Adaptive Detection of Idle State in Motor Imagery Based Brain Computer Interface

Author

Lv Jun

Author_Institution

South China Univ. of Technol., Guangzhou, China

Volume

1

fYear

2010

fDate

11-12 May 2010

Firstpage

417

Lastpage

420

Abstract

Asynchronous control is an important issue for brain-computer interfaces (BCIs) working in real life. To date, most of asynchronous BCI systems need predefined decision thresholds to tell when the user is idle. In this paper, we proposed an adaptive method for off-line idle state detection in motor imagery (MI) based BCIs. This method can automatically adjust the decision thresholds according to the separation and compactness ratio of event-related desynchronization (ERD) features in 2-class MI tasks. And it treats the prediction labels by a fuzzy way. Experimental results of BCI competition III dataset IVc show that the proposed method can decrease the prediction mean square error (MSE) to a level below 0.3 and improve the detection rate of idle state to 50%.

Keywords

brain-computer interfaces; image processing; induction motors; mean square error methods; 2-class MI tasks; BCI competition III dataset; ERD features; MI-based BCI; MSE; asynchronous BCI systems; asynchronous control; brain-computer interfaces; decision thresholds; event-related desynchronization; mean square error; motor imagery; offline idle state detection; Brain computer interfaces; Computer interfaces; Feature extraction; Foot; Linear discriminant analysis; Mean square error methods; Synchronous motors; Testing; Thyristors; Vectors; Brain computer interface; adaptive detection; idle state;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.171

Filename

5523533