Authors:
Amirhossein Jafarian
1
;
Mohammadhassan Moradi
2
;
Vahid Abootalebi
3
and
Mostafa Jafarian
4
Affiliations:
1
Cardiff University and Amirkabir University, United Kingdom
;
2
Amirkabir University, Iran, Islamic Republic of
;
3
Amirkabir University and Yazd University, Iran, Islamic Republic of
;
4
University of Manchester, United Kingdom
Keyword(s):
Probabilistic fuzzy classifier (PFC), P300, Genetic Algorithm (GA), Linear Discriminate Analysis (LDA), Fuzzy classifier.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Fuzzy Systems and Signals
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
P300 is an endogenous brain response to meaningful stimuli in oddball paradigm. Here the aim is to estimate whether this component exists in the recorded electroencephalogram (EEG) segment. A Probabilistic Fuzzy Classifier (PFC) followed by Genetic Algorithm (GA) has been developed in this paper. The main motivation of using PFC is that it not merely has the advantages of fuzzy systems, but also can exploit the stochastic properties of the underlying data. Moreover, by selecting the best set of time-frequency features utilizing GA the classification accuracy is enhanced. A comparison between the performance of the classifier and those based on stochastic properties of the data, like LDA (Linear Discriminate Analysis) and conventional fuzzy classifier verifies the superior performance of using this system.