Decoding movement direction using phase-space analysis of hemodynamic responses to arm movements based on functional near-infrared spectroscopy

2016 ◽  
Author(s):  
Nicoladie Tam ◽  
Luca Pollonini ◽  
George Zouridakis
Keyword(s):  
Infrared Spectroscopy ◽  
Phase Space ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Arm Movements ◽  
Movement Direction ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy ◽  
Phase Space Analysis ◽  
Space Analysis

scholarly journals Assessment of Brain Cortical Activation in Passive Movement During Wrist Task Using Functional Near Infrared Spectroscopy (fNIRS)

2019 ◽  
Author(s):  
Maziar Jalalvandi ◽  
Hamid Sharini ◽  
Yousof Naderi ◽  
Nader RiahiAlam
Keyword(s):  
Infrared Spectroscopy ◽  
Motor Cortex ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Primary Motor Cortex ◽  
Passive Movement ◽  
Cortical Activation ◽  
Movement Direction ◽  
Functional Near Infrared Spectroscopy ◽  
Activation Patterns

Purpose: Nowadays, the number of people diagnosed with movement disorders is increasing. Therefore, the evaluation of brain activity during motor task performance has attracted the attention of researchers in recent years. Functional Near-Infrared Spectroscopy (fNIRS) is a useful method that measures hemodynamic changes in the brain cortex based on optical principles. The purpose of this study was to evaluate the brain’s cortical activation in passive movement of the wrist. Materials and Methods: In current study, the activation of the brain's motor cortex during passive movement of the right wrist was investigated. To perform this study, ten healthy young right-handed volunteers were chosen. The required data were collected using a commercial 48-channel continuous wave fNIRS machine, using two different wavelengths of 765 and 855 nm at 10 Hz sampling rate. Results: Analysis of collected data showed that the brain's motor cortex during passive motion was significantly activated (p≤0.05) compared to rest. Motor cortex activation patterns depending on passive movement direction were separated. In different directions of wrist movement, the maximum activation was recorded at the primary motor cortex (M1). Conclusion: The present study has investigated the ability of fNIRS to evaluate cortical activation during passive movement of the wrist. Analysis of recording signals showed that different directions of movement have specific activation patterns in the motor cortex.

scholarly journals Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Kunqiang Qing ◽  
Ruisen Huang ◽  
Keum-Shik Hong
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Classification Performance ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Measuring Prefrontal Hemodynamic Responses Using Functional Near-Infrared Spectroscopy During Mobility for a Child With Motor Impairment

2017 ◽  
Vol 71 (4_Supplement_1) ◽  
pp. 7111505120p1
Author(s):  
Kelly Cusick ◽  
Alexandra DiStasi ◽  
Stephanie Holowinski ◽  
Olivia Fitzpatrick ◽  
Megan Davis ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motor Impairment ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy

scholarly journals Investigation of human visual cortex responses to flickering light using functional near infrared spectroscopy and constrained ICA

2014 ◽  
Vol 07 (06) ◽  
pp. 1450031 ◽  
Author(s):  
Nguyen Duc Thang ◽  
Vo Van Toi ◽  
Le Giang Tran ◽  
Nguyen Huynh Minh Tam ◽  
Lan Anh Trinh
Keyword(s):  
Infrared Spectroscopy ◽  
Visual Cortex ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hemoglobin Concentration ◽  
Visual Sensitivity ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy ◽  
Physiological Factors ◽  
Flickering Light

The human visual sensitivity to the flickering light has been under investigation for decades. The finding of research in this area can contribute to the understanding of human visual system mechanism and visual disorders, and establishing diagnosis and treatment of diseases. The aim of this study is to investigate the effects of the flickering light to the visual cortex by monitoring the hemodynamic responses of the brain with the functional near infrared spectroscopy (fNIRS) method. Since the acquired fNIRS signals are affected by physiological factors and measurement artifacts, constrained independent component analysis (cICA) was applied to extract the actual fNIRS responses from the obtained data. The experimental results revealed significant changes (p < 0.0001) of the hemodynamic responses of the visual cortex from the baseline when the flickering stimulation was activated. With the uses of cICA, the contrast to noise ratio (CNR), reflecting the contrast of hemodynamic concentration between rest and task, became larger. This indicated the improvement of the fNIRS signals when the noise was eliminated. In subsequent studies, statistical analysis was used to infer the correlation between the fNIRS signals and the visual stimulus. We found that there was a slight decrease of the oxygenated hemoglobin concentration (about 5.69%) over four frequencies when the modulation increased. However, the variations of oxy and deoxy-hemoglobin were not statistically significant.

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