BMFLC with neural network and DE for better event classification

Author

Yubo Wang ; Gonuguntla, V. ; Shafiq, G. ; Veluvolu, Kalyana C.

Author_Institution

Coll. of IT Eng., Kyungpook Nat. Univ., Daegu, South Korea

fYear

2013

fDate

18-20 Feb. 2013

Firstpage

34

Lastpage

35

Abstract

The event-related desynchronization(ERD) is a well known phenomenon that is commonly used for classification in brain-computer interface(BCI) applications. The classification accuracy of ERD based BCI can be improved by selection of subject-specific reactive band rather than complete μ-band. After obtaining time-frequency(TF) mapping of EEG signal with a Fourier based adaptive method, differential evolution(DE) is used for the identification of the reactive band. Compared to classical band-power based method, the proposed method based on subject-specific reactive band yields better accuracy with BCI competition dataset IV.

Keywords

brain-computer interfaces; electroencephalography; evolutionary computation; medical signal processing; neural nets; signal classification; BCI; BCI competition dataset; BMFLC; EEG signal; ERD; Fourier based adaptive method; TF mapping; band-power based method; brain-computer interface; differential evolution; electroencephalography; event classification; event-related desynchronization; neural network; subject-specific reactive band; time-frequency mapping; Accuracy; Artificial neural networks; Brain-computer interfaces; Classification algorithms; Electroencephalography; Time-frequency analysis; Vectors; Classification; Optimal Band; motor imagery;

fLanguage

English

Publisher

ieee

Conference_Titel

Brain-Computer Interface (BCI), 2013 International Winter Workshop on

Conference_Location

Gangwo

Print_ISBN

978-1-4673-5973-3

Type

conf

DOI

10.1109/IWW-BCI.2013.6506621

Filename

6506621