Electroencephalography Based Feature Selection for Multi-intelligence Activity

Author

Xiuli, Huang ; Wei, Wang

Author_Institution

Machine Learning & Cognition Lab., Sci. Nanjing Normal Univ., Nanjing, China

Volume

2

fYear

2010

fDate

6-7 March 2010

Firstpage

808

Lastpage

810

Abstract

In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence classification.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; medical diagnostic computing; support vector machines; BCI; SVM; brain-computer interface; electroencephalography; feature selection; imagery intelligent tasks; multiintelligence classification; support vector machine; Arithmetic; Cognition; Computer science education; Electroencephalography; Eyes; Fingers; Learning systems; Machine learning; Support vector machine classification; Support vector machines; EEG; SVM; classification; feature selection; multi intellifences;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science (ETCS), 2010 Second International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6388-6

Electronic_ISBN

978-1-4244-6389-3

Type

conf

DOI

10.1109/ETCS.2010.629

Filename

5459849