Paper Info
Title
Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI.
Abstract
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.
Year
DOI
Venue
2013
10.1155/2013/754539
JOURNAL OF APPLIED MATHEMATICS
Field
DocType
Volume
Brain–computer interface,Artificial intelligence,HS algorithm,Classifier (linguistics),Electroencephalography,Heuristic,Mathematical optimization,Pattern recognition,Harmony search,Eeg analysis,Eeg signal analysis,Mathematics,Machine learning
Journal
2013
ISSN
Citations 
PageRank 
1110-757X
3
0.39
References 
Authors
17
3
Name
Order
Citations
PageRank
Taeju Lee141.46
Seung-Min Park216120.24
Kwee-Bo Sim324944.07