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 Detection of primary RGB colors projected on a screen using fNIRS

2017 ◽  
Vol 10 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Xiaolong Liu ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Visual Cortex ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant ◽  
Stimulation Period ◽  
Skewness And Kurtosis

In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the [Formula: see text]-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7[Formula: see text]s window for a given 10[Formula: see text]s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

scholarly journals A DAQ-Device-Based Continuous Wave Near-Infrared Spectroscopy System for Measuring Human Functional Brain Activity

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Gang Xu ◽  
Xiaoli Li ◽  
Duan Li ◽  
Xiaomin Liu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Mental Arithmetic ◽  
Valsalva Maneuver ◽  
Light Sources ◽  
Functional Near Infrared Spectroscopy ◽  
Quadrature Demodulation ◽  
Custom Made

In the last two decades, functional near-infrared spectroscopy (fNIRS) is getting more and more popular as a neuroimaging technique. The fNIRS instrument can be used to measure local hemodynamic response, which indirectly reflects the functional neural activities in human brain. In this study, an easily implemented way to establish DAQ-device-based fNIRS system was proposed. Basic instrumentation components (light sources driving, signal conditioning, sensors, and optical fiber) of the fNIRS system were described. The digital in-phase and quadrature demodulation method was applied in LabVIEW software to distinguish light sources from different emitters. The effectiveness of the custom-made system was verified by simultaneous measurement with a commercial instrument ETG-4000 during Valsalva maneuver experiment. The light intensity data acquired from two systems were highly correlated for lower wavelength (Pearson’s correlation coefficientr= 0.92,P< 0.01) and higher wavelength (r= 0.84,P< 0.01). Further, another mental arithmetic experiment was implemented to detect neural activation in the prefrontal cortex. For 9 participants, significant cerebral activation was detected in 6 subjects (P< 0.05) for oxyhemoglobin and in 8 subjects (P< 0.01) for deoxyhemoglobin.

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.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

2010 ◽  
Vol 03 (01) ◽  
pp. 69-74 ◽  
Author(s):  
YE ZHU ◽  
TIANZI JIANG ◽  
YUAN ZHOU ◽  
LISHA ZHAO
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Psychiatric Patients ◽  
Clinical Tool ◽  
Functional Near Infrared Spectroscopy ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Discriminative Model ◽  
Neuroimaging Biomarkers

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

scholarly journals Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Zhang ◽  
Xin Liu ◽  
Dan Liu ◽  
Chunling Yang ◽  
Qisong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Auditory Stimulus ◽  
Near Infrared Spectroscopy ◽  
Adaptive Filtering ◽  
Near Infrared ◽  
Continuous Wave ◽  
Hemodynamic Response ◽  
Recursive Least Squares ◽  
Functional Near Infrared Spectroscopy

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

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.

Algorithm Establishment with Model Experiment for Continuous-Wave Functional Near-Infrared Spectroscopy

2011 ◽  
Vol 138-139 ◽  
pp. 553-559
Author(s):  
Ting Li ◽  
Zhi Li Zhang ◽  
Yi Zheng
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Model Experiment ◽  
Near Infrared ◽  
Continuous Wave ◽  
Valsalva Maneuver ◽  
Hemoglobin Concentration ◽  
Functional Near Infrared Spectroscopy ◽  
Concentration Changes

Although functional near-infrared spectroscopy (fNIRS) has been developing as a useful tool for monitoring functional brain activity since the early 1990s, the quantification of hemoglobin concentration changes is still controversial and there are few detailed reports especially for continuous-wave (CW) instruments. By means of a two-layer model experiment mimicking hemodynamic changes in brain and mathematical analysis based on the modified Beer-Lambert law, we established an algorithm for a CW functional near-infrared spectroscopy (CW-fNIRS). The accuracy of this algorithm was validated both in comparison with direct measurements on brain tissue model and in vivo measurement upon human valsalva maneuver. This described method can also be utilized for other CW-fNIRS instruments to establish measuring algorithm.

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