Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI
Joint Authors
Park, Seung-Min
Lee, Tae-Ju
Sim, Kwee-Bo
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system.
EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult.
In addition, traditional EEG analysis cannot handle multiple stimuli.
On the other hand, the classification method using the EEG signal has a low accuracy.
To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification.
In this study, we build a group of stimuli using the HS algorithm.
Then, the features from common spatial patterns are classified by the HS classifier.
To confirm the proposed method, we perform experiments using 64-channel EEG equipment.
The subjects are subjected to three kinds of stimuli: audio, visual, and motion.
Each stimulus is applied alone or in combination with the others.
The acquired signals are processed by the proposed method.
The classification results in an accuracy of approximately 63%.
We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.
American Psychological Association (APA)
Lee, Tae-Ju& Park, Seung-Min& Sim, Kwee-Bo. 2013. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-496164
Modern Language Association (MLA)
Lee, Tae-Ju…[et al.]. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI. Journal of Applied Mathematics No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-496164
American Medical Association (AMA)
Lee, Tae-Ju& Park, Seung-Min& Sim, Kwee-Bo. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-496164
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496164