Control of Axially Moving Systems: A Review

Hong, Keum‐Shik; Pham, Phuong-Tung

doi:10.1007/s12555-019-0592-5

Cited by 67 publications

(17 citation statements)

References 202 publications

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“…In comparison with other non-invasive neuroimaging modalities (i.e., fMRI, EEG, and MEG), fNIRS has the advantages of safety, lower cost, portability, tolerance of motion artifacts, good temporal resolution, and moderate spatial resolution [16]. Additionally, the development of initial dip detection [17][18][19], bundled-optodes configurations [20][21][22][23], and adaptive algorithms [24][25][26][27] have offered further opportunities to improve the temporal and spatial resolution of fNIRS.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Yang

Hong

2021

JAD

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Background: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Objective: This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. Methods: In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. Results: There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% % and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. Conclusion: The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

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Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Yang

Hong

2021

JAD

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“…In contrast, fNIRS is a novel neuroimaging modality with the following advantages: it is non-invasive, safe, less costly, portable, and tolerant of motion artifacts (Perrey, 2008); it also has great temporal resolution and moderate spatial resolution (Ghafoor et al, 2017;Zafar and Hong, 2020). In addition, fNIRS is in progress to improve the spatial and temporal resolutions with the development of bundled-optodes configurations , detection of the initial dip (Zafar and Hong, 2017;Hong and Zafar, 2018), and combination of adaptive method (Iqbal et al, 2018;Hong and Pham, 2019;Pamosoaji et al, 2019) to improve information transfer rate.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Yang

Huang

Yoo

et al. 2020

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (HbO) in the region of interest, HbO maps at 13 specific time points (i.e.

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“…Nevertheless, EEG is limited in simultaneous use with the brain stimulation device because recorded signals affect the electric and magnetic fields. However, specific signal-processing algorithms can remove interference components, such as principal component analysis, independent component analysis, and adaptive filter algorithms ( Deng et al, 2019 ; Hong and Pham, 2019 ; Yoo, 2019 ; Park et al, 2020 ). Thus, a technique free of limitation, such as a noninvasive neuroimaging modality (i.e., fNIRS), as a promising method, should be considered the first choice.…”

Section: Discussionmentioning

confidence: 99%

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

2021

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BackgroundBrain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.ObjectiveThis study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.MethodA systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.ResultsSixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.ConclusionAn appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

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Control of Axially Moving Systems: A Review

Cited by 67 publications

References 202 publications

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

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