EEG classification of word perception using common spatial pattern filter

Author

Woosu Choi ; Jongin Kim ; Boreom Lee

Author_Institution

Dept. of Med. Syst. Eng., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea

fYear

2015

fDate

12-14 Jan. 2015

Firstpage

1

Lastpage

4

Abstract

The purpose of this study is to classify perceptual electroencephalography (EEG) data of words into appropriate class. We recorded EEG data from six native Korean speakers at Gwangju Institute of Science and Technology. Three words (/Lemon/, /Mother/, and /Toilet/) were chosen for stimuli. We applied IIR bandpass filter for extracting alpha bands activities from raw EEG data and common spatial pattern filter to enhance classification performance. We used pairwise classification method and mean classification rates corresponding to /Lemon/ vs /Mother/, /Lemon/ vs /Toilet/, and /Mother/ vs /Toilet/ were 54.31 ± 4.31, 59.66 ± 3.11, and 59.88 ± 5.70 respectively for all the subjects.

Keywords

IIR filters; band-pass filters; electroencephalography; signal classification; speech processing; EEG classification; EEG data; Gwangju Institute of Science and Technology; IIR bandpass filter; Korean speakers; alpha bands activities extracting; common spatial pattern filter; electroencephalography data; mean classification rates; word perception; Band-pass filters; Covariance matrices; Electroencephalography; Independent component analysis; Support vector machines; Time-frequency analysis; Vectors; Common Spatial Pattern; Electroencephalography; Support Vector Machine; Word Percpetion;

fLanguage

English

Publisher

ieee

Conference_Titel

Brain-Computer Interface (BCI), 2015 3rd International Winter Conference on

Conference_Location

Sabuk

Print_ISBN

978-1-4799-7494-8

Type

conf

DOI

10.1109/IWW-BCI.2015.7073032

Filename

7073032