alexa A Comparison Study on Machine Learning Algorithms Utili
ISSN: 2157-7420

Journal of Health & Medical Informatics
Open Access

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Research Article

A Comparison Study on Machine Learning Algorithms Utilized in P300- based BCI

Rami J Oweis*, Naser Hamdi, Adham Ghazali and Khaldoun Lwissy

Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan

*Corresponding Author:
Rami J Oweis
Biomedical Engineering Department
Faculty of Engineering
Jordan University of Science and Technology
P.O. Box 3030, Irbid 22110, Jordan
Tel: 0096227201000
Fax: 0096227201074
E-mail: [email protected]

Received date: May 18, 2013; Accepted date: June 15, 2013; Published date: June 19, 2013

Citation: Oweis RJ, Hamdi N, Ghazali A, Lwissy K (2013) A Comparison Study on Machine Learning Algorithms Utilized in P300-based BCI. J Health Med Informat 4:126. doi: 10.4172/2157-7420.1000126

Copyright: © 2013 Oweis RJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.


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