![](/images/graphics-bg.png)
Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface
Joint Authors
Naseer, Noman
Qureshi, Nauman Khalid
Noori, Farzan Majeed
Hong, Keum-Shik
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals.
The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system.
After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals.
Two- and three-dimensional combinations of those features were used for classification of mental tasks.
In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized.
With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively.
ANN showed the maximum classification accuracies: 91.4 and 96.3%.
In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals.
American Psychological Association (APA)
Naseer, Noman& Qureshi, Nauman Khalid& Noori, Farzan Majeed& Hong, Keum-Shik. 2016. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099703
Modern Language Association (MLA)
Naseer, Noman…[et al.]. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099703
American Medical Association (AMA)
Naseer, Noman& Qureshi, Nauman Khalid& Noori, Farzan Majeed& Hong, Keum-Shik. Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099703
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099703