scholarly journals A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s

2021 ◽  
Vol 15 ◽  
Author(s):  
Candida Barreto ◽  
Guilherme de Albuquerque Bruneri ◽  
Guilherme Brockington ◽  
Hasan Ayaz ◽  
Joao Ricardo Sato
Keyword(s):  
Near Infrared ◽  
Brain Activity ◽  
Methodological Approach ◽  
Critical Role ◽  
Least Square ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Ordinary Least Square ◽  
Teacher Student ◽  
Interaction Task

Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression (SVR) to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square (OLS) predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads (18 channels 5 dyads = 90 channel-pairs) presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response.

scholarly journals Affiliative bonding between teachers and students through interpersonal synchronisation in brain activity

2020 ◽  
Vol 15 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Lifen Zheng ◽  
Wenda Liu ◽  
Yuhang Long ◽  
Yu Zhai ◽  
Hui Zhao ◽  
...  
Keyword(s):  
Social Interaction ◽  
Resting State ◽  
Near Infrared ◽  
Brain Activity ◽  
Human Beings ◽  
Functional Near Infrared Spectroscopy ◽  
Teacher Student ◽  
Before And After ◽  
A Chain ◽  
The Right

Abstract Human beings organise socially. Theories have posited that interpersonal neural synchronisation might underlie the creation of affiliative bonds. Previous studies tested this hypothesis mainly during a social interaction, making it difficult to determine whether the identified synchronisation is associated with affiliative bonding or with social interaction. This study addressed this issue by focusing on the teacher–student relationship in the resting state both before and after a teaching period. Brain activity was simultaneously measured in both individuals using functional near-infrared spectroscopy. The results showed a significant increase in brain synchronisation at the right sensorimotor cortex between the teacher and student in the resting state after, but not before, the teaching period. Moreover, the synchronisation increased only after a turn-taking mode of teaching but not after a lecturing or video mode of teaching. A chain mediation analysis showed that brain synchronisation during teaching partially mediated the relationship between the brain synchronisation increase in the resting state and strength of the affiliative bond. Finally, both role assignment and social interaction were found to be required for affiliative bonding. Together, these results support the hypothesis that interpersonal synchronisation in brain activity underlies affiliative bonding and that social interaction mechanically mediates the bonding process.

Making social neuroscience less WEIRD: Using fNIRS to measure neural signatures of persuasive influence in a Middle East participant sample

2019 ◽  
Author(s):  
Shannon Burns ◽  
Lianne N. Barnes ◽  
Ian A. McCulloh ◽  
Munqith M. Dagher ◽  
Emily B. Falk ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Near Infrared ◽  
Brain Activity ◽  
Health And Safety ◽  
Social Neuroscience ◽  
Functional Near Infrared Spectroscopy ◽  
Future Directions ◽  
Neuroscience Research ◽  
Neuropsychological Functions ◽  
Arabic Speaking

The large majority of social neuroscience research uses WEIRD populations – participants from Western, educated, industrialized, rich, and democratic locations. This makes it difficult to claim whether neuropsychological functions are universal or culture specific. In this study, we demonstrate one approach to addressing the imbalance by using portable neuroscience equipment in a study of persuasion conducted in Jordan with an Arabic-speaking sample. Participants were shown persuasive videos on various health and safety topics while their brain activity was measured using functional near infrared spectroscopy (fNIRS). Self-reported persuasiveness ratings for each video were then recorded. Consistent with previous research conducted with American subjects, this work found that activity in the dorsomedial and ventromedial prefrontal cortex predicted how persuasive participants found the videos and how much they intended to engage in the messages’ endorsed behaviors. Further, activity in the left ventrolateral prefrontal cortex was associated with persuasiveness ratings, but only in participants for whom the message was personally relevant. Implications for these results on the understanding of the brain basis of persuasion and on future directions for neuroimaging in diverse populations are discussed.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Cortical Activity Linked to Clocking in Deaf Adults: fNIRS Insights with Static and Animated Stimuli Presentation

2021 ◽  
Vol 11 (2) ◽  
pp. 196
Author(s):  
Sébastien Laurent ◽  
Laurence Paire-Ficout ◽  
Jean-Michel Boucheix ◽  
Stéphane Argon ◽  
Antonio Hidalgo-Muñoz
Keyword(s):  
Near Infrared ◽  
Brain Activity ◽  
Time Estimation ◽  
Cognitive Effort ◽  
Deaf People ◽  
Vehicle Speed ◽  
Neural Basis ◽  
Functional Near Infrared Spectroscopy ◽  
The Road ◽  
Deaf Adults

The question of the possible impact of deafness on temporal processing remains unanswered. Different findings, based on behavioral measures, show contradictory results. The goal of the present study is to analyze the brain activity underlying time estimation by using functional near infrared spectroscopy (fNIRS) techniques, which allow examination of the frontal, central and occipital cortical areas. A total of 37 participants (19 deaf) were recruited. The experimental task involved processing a road scene to determine whether the driver had time to safely execute a driving task, such as overtaking. The road scenes were presented in animated format, or in sequences of 3 static images showing the beginning, mid-point, and end of a situation. The latter presentation required a clocking mechanism to estimate the time between the samples to evaluate vehicle speed. The results show greater frontal region activity in deaf people, which suggests that more cognitive effort is needed to process these scenes. The central region, which is involved in clocking according to several studies, is particularly activated by the static presentation in deaf people during the estimation of time lapses. Exploration of the occipital region yielded no conclusive results. Our results on the frontal and central regions encourage further study of the neural basis of time processing and its links with auditory capacity.

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 Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Pilot Study on Gait Classification Using fNIRS Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hedian Jin ◽  
Chunguang Li ◽  
Jiacheng Xu
Keyword(s):  
Near Infrared ◽  
Motor Dysfunction ◽  
Assistive Device ◽  
Support Vector ◽  
Small Step ◽  
Functional Near Infrared Spectroscopy ◽  
Low Speed ◽  
Rehabilitation Training ◽  
Feature Vectors ◽  
Motion Intention

Rehabilitation training is essential for motor dysfunction patients, and the training through their subjective motion intention, comparing to passive training, is more conducive to rehabilitation. This study proposes a method to identify motion intention of different walking states under the normal environment, by using the functional near-infrared spectroscopy (fNIRS) technology. Twenty-two healthy subjects were recruited to walk with three different gaits (including small-step with low-speed, small-step with midspeed, midstep with low-speed). The wavelet packet decomposition was used to find out the main characteristic channels in different motion states, and these channels with links in frequency and space were combined to define as feature vectors. According to different permutations and combinations of all feature vectors, a library for support vector machines (libSVM) was used to achieve the best recognition model. Finally, the accuracy rate of these three walking states was 78.79%. This study implemented the classification of different states’ motion intention by using the fNIRS technology. It laid a foundation to apply the classified motion intention of different states timely, to help severe motor dysfunction patients control a walking-assistive device for rehabilitation training, so as to help them restore independent walking abilities and reduce the economic burdens on society.

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