scholarly journals Comparison of functional activation responses from the auditory cortex derived using multi-distance frequency domain and continuous wave near-infrared spectroscopy

2021 ◽  
Vol 8 (04) ◽  
Author(s):  
Penaz Parveen Sultana Mohammad ◽  
Sittiprapa Isarangura ◽  
Ann Eddins ◽  
Ashwin B. Parthasarathy
Keyword(s):  
Infrared Spectroscopy ◽  
Auditory Cortex ◽  
Frequency Domain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave

scholarly journals Frequency-domain vs continuous-wave near-infrared spectroscopy devices: a comparison of clinically viable monitors in controlled hypoxia

2016 ◽  
Vol 31 (5) ◽  
pp. 967-974 ◽  
Author(s):  
David James Davies ◽  
Michael Clancy ◽  
Daniel Lighter ◽  
George M. Balanos ◽  
Samuel John Edwin Lucas ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Frequency Domain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave

scholarly journals Comparison of frequency-domain and continuous-wave near-infrared spectroscopy devices during the immediate transition

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Tanja van Essen ◽  
Tom G. Goos ◽  
Liza van Ballegooijen ◽  
Gerhard Pichler ◽  
Berndt Urlesberger ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Frequency Domain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Immediate Transition

scholarly journals Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Noman Naseer ◽  
Nauman Khalid Qureshi ◽  
Farzan Majeed Noori ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Statistical Significance ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant

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 thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

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