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.

Social hyperscanning with fNIRS

Gesture ◽  
2020 ◽  
Vol 19 (2-3) ◽  
pp. 196-222
Author(s):  
Michela Balconi ◽  
Angela Bartolo ◽  
Giulia Fronda
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Brain Connectivity ◽  
Gestural Communication ◽  
Functional Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal ◽  
Posterior Parietal

Abstract The interest of neuroscience has been aimed at the investigation of the neural bases underlying gestural communication. This research explored the intra- and inter-brain connectivity between encoder and decoder. Specifically, adopting a “hyperscanning paradigm” with the functional Near-infrared Spectroscopy (fNIRS) cerebral connectivity in oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin levels were revealed during the reproduction of affective, social, and informative gestures of different valence. Results showed an increase of intra- and inter-brain connectivity in dorsolateral prefrontal cortex for affective gestures, in superior frontal gyrus for social gestures and in frontal eyes field for informative gestures. Moreover, encoder showed a higher intra-brain connectivity in posterior parietal areas more than decoder. Finally, an increasing of inter-brain connectivity more than intra-brain (ConIndex) was observed in left regions for positive gestures. The present research has explored how the individuals neural tuning mechanisms turn out to be strongly influenced by the nature of specific gestures.

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 What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep

2021 ◽  
Vol 3 ◽  
Author(s):  
Zilu Liang
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Stress Indicators ◽  
Measured Stress ◽  
Dorsolateral Prefrontal

People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.

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.

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