scholarly journals Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1476
Author(s):  
Bulmaro A. Valdés ◽  
Kim Lajoie ◽  
Daniel S. Marigold ◽  
Carlo Menon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brain Activity ◽  
Vestibular Stimulation ◽  
Galvanic Vestibular Stimulation ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Cognitive Behaviors ◽  
Sham Stimulation

Noisy galvanic vestibular stimulation (nGVS) can improve different motor, sensory, and cognitive behaviors. However, it is unclear how this stimulation affects brain activity to facilitate these improvements. Functional near-infrared spectroscopy (fNIRS) is inexpensive, portable, and less prone to motion artifacts than other neuroimaging technology. Thus, fNIRS has the potential to provide insight into how nGVS affects cortical activity during a variety of natural behaviors. Here we sought to: (1) determine if fNIRS can detect cortical changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin with application of subthreshold nGVS, and (2) determine how subthreshold nGVS affects this fNIRS-derived hemodynamic response. A total of twelve healthy participants received nGVS and sham stimulation during a seated, resting-state paradigm. To determine whether nGVS altered activity in select cortical regions of interest (BA40, BA39), we compared differences between nGVS and sham HbO and HbR concentrations. We found a greater HbR response during nGVS compared to sham stimulation in left BA40, a region previously associated with vestibular processing, and with all left hemisphere channels combined (p < 0.05). We did not detect differences in HbO responses for any region during nGVS (p > 0.05). Our results suggest that fNIRS may be suitable for understanding the cortical effects of nGVS.

scholarly journals Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2362 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vadim Grubov ◽  
Artem Badarin ◽  
Vladimir A. Maksimenko ◽  
Alexander N. Pisarchik
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Efficiency ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
Hemispheric Lateralization ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Measurement of Train Driver’s Brain Activity by Functional Near-Infrared Spectroscopy (fNIRS)

2005 ◽  
Vol 37 (13-15) ◽  
pp. 1319-1338 ◽  
Author(s):  
Takashi Kojima ◽  
Hitoshi Tsunashima ◽  
Tomoki Shiozawa ◽  
Hiroki Takada ◽  
Takuji Sakai
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brain Activity ◽  
Functional Near Infrared Spectroscopy

scholarly journals New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

2020 ◽  
Vol 10 (6) ◽  
pp. 342 ◽  
Author(s):  
Fabian Herold ◽  
Thomas Gronwald ◽  
Felix Scholkmann ◽  
Hamoon Zohdi ◽  
Dominik Wyser ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Physical Exercise ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Exercise Prescription ◽  
Brain Activity ◽  
Brain Health ◽  
Functional Near Infrared Spectroscopy ◽  
Internal Load

In the literature, it is well established that regular physical exercise is a powerful strategy to promote brain health and to improve cognitive performance. However, exact knowledge about which exercise prescription would be optimal in the setting of exercise–cognition science is lacking. While there is a strong theoretical rationale for using indicators of internal load (e.g., heart rate) in exercise prescription, the most suitable parameters have yet to be determined. In this perspective article, we discuss the role of brain-derived parameters (e.g., brain activity) as valuable indicators of internal load which can be beneficial for individualizing the exercise prescription in exercise–cognition research. Therefore, we focus on the application of functional near-infrared spectroscopy (fNIRS), since this neuroimaging modality provides specific advantages, making it well suited for monitoring cortical hemodynamics as a proxy of brain activity during physical exercise.

scholarly journals Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Correction Method ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Performance Evaluation Index

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

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