Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component a...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2017-01, Vol.8 (8)
Main Authors: Diana, Oana, Mihaela, Anca
Format: Article
Language:English
Subjects:
Classification
Classifiers
Discriminant analysis
Evaluation
Feature extraction
Human-computer interface
Independent component analysis
Locking
Phase lag
Support vector machines
Synchronism
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Description
Summary:A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2017.080834