scholarly journals PR-SVM algorithm for recognition of human hand tapping using functional near infrared spectroscopy

2013 ◽  
Vol 16 (3) ◽  
pp. 5-17
Author(s):  
Hai Thanh Nguyen ◽  
Cuong Quoc Ngo ◽  
Hung Viet Nguyen
Keyword(s):  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Invasive Technique ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Right Hand ◽  
Svm Algorithm ◽  
Hand Tapping

Researches of human Brain Computer Interface (BCI) for the objective of diagnosis and rehabilitation have been recently increased. Cerebral oxygenation and blood flow on particular regions of human brain can be measured using a non-invasive technique – fNIRS (functional Near Infrared Spectroscopy). In this paper, a study of recognition algorithm will be described for recognizing whether one taps his/her left hand or right hand. Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky- Golay filter for getting more smoothly fNIRS data. Characteristics of the filtered signals during left and right hand tapping process will be extracted using a Polynomial Regression (PR)-Support Vector Machine (SVM) algorithm. Coefficients of the polynomial determined by the PR algorithm, which correspond to Oxygen-Hemoglobin (Oxy- Hb) concentration changes, will be applied for the recognition of hand tapping. Then the SVM will be employed to validate the obtained coefficient data for the hand tapping recognition. Experimental results have been done many trials on 3 subjects to illustrate the effectiveness of the proposed method.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

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.

scholarly journals Imaging System Based on Silicon Photomultipliers and Light Emitting Diodes for Functional Near-Infrared Spectroscopy

2020 ◽  
Vol 10 (3) ◽  
pp. 1068 ◽  
Author(s):  
Giovanni Maira ◽  
Antonio M. Chiarelli ◽  
Stefano Brafa ◽  
Sebania Libertino ◽  
Giorgio Fallica ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Light Emitting Diodes ◽  
Optical Tomography ◽  
System Optimization ◽  
Infrared Light ◽  
Functional Near Infrared Spectroscopy ◽  
Light Emitting

We built a fiber-less prototype of an optical system with 156 channels each one consisting of an optode made of a silicon photomultiplier (SiPM) and a pair of light emitting diodes (LEDs) operating at 700 nm and 830 nm. The system uses functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) imaging of the cortical activity of the human brain at frequencies above 1 Hz. In this paper, we discuss testing and system optimization performed through measurements on a multi-layered optical phantom with mechanically movable parts that simulate near-infrared light scattering inhomogeneities. The baseline optical characteristics of the phantom are carefully characterized and compared to those of human tissues. Here we discuss several technical aspects of the system development, such as LED light output drift and its possible compensation, SiPM linearity, corrections of channel signal differences, and signal-to-noise ratio (SNR). We implement an imaging algorithm that investigates large phantom regions. Thanks to the use of SiPMs, very large source-to-detector distances are acquired with a high SNR and 2 Hz time resolution. The overall results demonstrate the high potentialities of a system based on SiPMs for fNIRS/DOT human brain imaging applications.

scholarly journals Classification of Game Demand and the Presence of Experimental Pain Using Functional Near-Infrared Spectroscopy

2021 ◽  
Vol 2 ◽  
Author(s):  
Stephen H. Fairclough ◽  
Chelsea Dobbins ◽  
Kellyann Stamp
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cold Pressor Test ◽  
Experimental Pain ◽  
Cold Pressor ◽  
Pain Tolerance ◽  
Support Vector ◽  
Test Protocol ◽  
Functional Near Infrared Spectroscopy

Pain tolerance can be increased by the introduction of an active distraction, such as a computer game. This effect has been found to be moderated by game demand, i.e., increased game demand = higher pain tolerance. A study was performed to classify the level of game demand and the presence of pain using implicit measures from functional Near-InfraRed Spectroscopy (fNIRS) and heart rate features from an electrocardiogram (ECG). Twenty participants played a racing game that was configured to induce low (Easy) or high (Hard) levels of demand. Both Easy and Hard levels of game demand were played with or without the presence of experimental pain using the cold pressor test protocol. Eight channels of fNIRS data were recorded from a montage of frontal and central-parietal sites located on the midline. Features were generated from these data, a subset of which were selected for classification using the RELIEFF method. Classifiers for game demand (Easy vs. Hard) and pain (pain vs. no-pain) were developed using five methods: Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Naive Bayes (NB) and Random Forest (RF). These models were validated using a ten fold cross-validation procedure. The SVM approach using features derived from fNIRS was the only method that classified game demand at higher than chance levels (accuracy = 0.66, F1 = 0.68). It was not possible to classify pain vs. no-pain at higher than chance level. The results demonstrate the viability of utilising fNIRS data to classify levels of game demand and the difficulty of classifying pain when another task is present.

Design and Development of Two Channel Portable Near Infrared Spectroscopy System for Studies of Cerebral Oxygenation in Prefrontal Cortex of Human Brain

2014 ◽  
Vol 573 ◽  
pp. 814-818
Author(s):  
S. Bagyaraj ◽  
G. Ravindran ◽  
S. Shenbaga Devi
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Cerebral Oxygenation ◽  
Wave Length ◽  
Spatial Task ◽  
Functional Near Infrared Spectroscopy

Functional near infrared spectroscopy is a noninvasive, non harmful, low cost and safe optical technique that can be used to study the functional activities in the human brain. This paper describes the development of two channel Near InfraRed Spectroscopy (NIRS) system and the results of the cerebral oxygenation changes during the different cognitive tasks. The objective of the study is to design, develop a portable non-invasive continuous wave NIRS system with dual wave length for determining the hemoglobin content of the blood chromophores during different activities of the prefrontal cortex of the brain. The two channel NIRS system designed and it was tested with 20 healthy, ie.,15 males and 5 females with an average age group of 21±2.25, they were given a 2 different mental tasks such as sequential subtraction (mathematical task) and spot the difference (Visuo-spatial task) and their Oxy & de-Oxy hemoglobin concentration was measured which showed more changes during the task period when compared to relaxation in both left and right part of pre-frontal cortex.

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