Title
Author-s
Contact lnfo
Department
Major
citation
Year of
Publication
Publisher
Sponsor
Type of
Publication
ISSN
Analytical Evaluation of hybrid classification methods for EEG based Brain
Computer Interface
Ijaz Ali Shoukat, M. Iftikhar,
mohsinunsw@gmail.com
Computer science
Accepted in WORKSHOP ON UNESCO-HP “BRAIN GAIN INITIATIVE” In
Conjunction with 2nd Kuwait Conference on E-Services & E-Systems, April 5-7,
2011, Kuwait University, Kuwait. Proceedings of ACM Inc. 2011
2011
2nd Kuwait Conference on E-Services & E-Systems
Conference
URI/DOI
Full Text
(Yes,No)
Key words
Abstract
No
Joint Hybrid Classification Methods, Learning Classifiers, Brain Computer Interface, EEG
Significance: Brain Computer Interfacing research has made possible that a machine can be
controlled by human thinking with such a passion we just think and tasks are done by the machine
without any physical effort. Classification methods are the backbone of any kind of BCI Frame work
especially for Electroencephalographic signal’s based systems.
Motive: Electroencephalographic signals contain variety of problems like noise, multi
dimensionality, outlier, non- stationary, varying size of training sets, time variations and artifacts that
cause diverse training and classification to make less accurate decision. Hybrid classifier is better
than single classifier for getting effective classification accuracy. Many hybrid classifiers persist but
it is a question mark which combination of classifiers is better. This paper provides analytical
comparison under theoretical set of rules to conclude which hybrid method can provide wrathful
learning and classification services for EEG based Brain Computer Interfacing systems.
Approaches: We implemented quantitative grading criteria which follows analytical comparison
under theoretical set of rules to judge the hypothetical capability of different joint assembly of
classifiers.
Findings: Joint Hybrid Method (HMM + SVM + LDA + CSP) is superior to overcome maximum
capacity of problems related to learning and classification of EEG signals for EEG based Brain
Compute Interface.