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Estimation of optical pathlength through tissue from direct time of flight measurement

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A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy

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Use of the water absorption spectrum to quantify tissue chromophore concentration changes in near-infrared spectroscopy

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Influence of a superficial layer in the quantitative spectroscopic study of strongly scattering media

Franceschini, M. A., Fantini, S., Paunescu, L. A., Maier, J. S., & Gratton, E. (1998) · Applied Optics · doi:10.1364/ao.37.007447 · Cited by 172

Linear Aspects of Changes in Deoxygenated Hemoglobin Concentration and Cytochrome Oxidase Oxidation during Brain Activation

Wobst, P., Wenzel, R., Kohl‐Bareis, M., Obrig, H., & Villringer, A. (2001) · NeuroImage · doi:10.1006/nimg.2000.0706 · Cited by 117

Determination of the wavelength dependence of the differential pathlength factor from near-infrared pulse signals

Kohl‐Bareis, M., Nolte, C. H., Heekeren, H. R., Horst, S., Scholz, U., Obrig, H., & Villringer, A. (1998) · Physics in Medicine and Biology · doi:10.1088/0031-9155/43/6/028 · Cited by 112

Photon path distribution and optical responses of turbid media: theoretical analysis based on the microscopic Beer-Lambert law

Tsuchiya, Y. (2001) · Physics in Medicine and Biology · doi:10.1088/0031-9155/46/8/303 · Cited by 43

Affective or cognitive interpersonal emotion regulation in couples: an fNIRS hyperscanning study

Zhang, W., Qiu, L., Tang, F., & Li, H. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad091 · Cited by 30

Continuous Recognition of Affective States by Functional Near Infrared Spectroscopy Signals

Heger, D., Mutter, R., Herff, C., Putze, F., & Schultz, T. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.156 · Cited by 27

Fluctuations in intracranial pressure can be estimated non-invasively using near-infrared spectroscopy in non-human primates

Ruesch, A., Schmitt, S. E., Yang, J., Smith, M. A., & Kainerstorfer, J. M. (2019) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x19891359 · Cited by 26

Speaking mode recognition from functional Near Infrared Spectroscopy

Herff, C., Putze, F., Heger, D., Guan, C., & Schultz, T. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346279 · Cited by 26

NEAR INFRARED IMAGING AND SPECTROSCOPY FOR BRAIN ACTIVITY MONITORING

Son, I.-Y., & Yazıcı, B. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/1-4020-4295-7_15 · Cited by 25

Workplace design-related stress effects on prefrontal cortex connectivity and neurovascular coupling

Alyan, E., Saad, M. N. M., Kamel, N., & Rahman, M. A. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103497 · Cited by 24

Precise theoretical model for quantum-dot color conversion

Xu, S., Yang, T., Lin, J., Shen, Q., Li, J., Ye, Y., Wang, L., Zhou, X., Chen, E., Ye, Y., et al. (2021) · Optics Express · doi:10.1364/oe.425556 · Cited by 24

Diffuse reflectance spectroscopy characterization of hemoglobin and intralipid solutions: in vitro measurements with continuous variation of absorption and scattering

Hernández, S. E., Rodríguez, V. D., Pérez, J. C., Martı́n, F. A., Castellano, M. A., & González–Mora, J. L. (2009) · Journal of Biomedical Optics · doi:10.1117/1.3149864 · Cited by 24

An ABC of near Infrared Photon Migration in Tissues: The Diffusive Regime of Propagation

Martelli, F. (2012) · Journal of Near Infrared Spectroscopy · doi:10.1255/jnirs.974 · Cited by 24

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Shirvan, R. A., Setarehdan, S. K., & Nasrabadi, A. M. (2018) · International Clinical Neuroscience Journal · doi:10.15171/icnj.2018.11 · Cited by 23

A novel model extended from the Bouguer-Lambert-Beer law can describe the non-linear absorbance of potassium dichromate solutions and microalgae suspensions

Yeh, Y., Haasdonk, B., Schmid‐Staiger, U., Stier, M., & Tovar, G. E. M. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1116735 · Cited by 23

Transformational change in the field of diffuse optics: From going bananas to going nuts

Fantini, S., Blaney, G. P., & Sassaroli, A. (2019) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545819300131 · Cited by 23

Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people

Berger, A., Horst, F., Steinberg, F., Thomas, F., Müller-Eising, C., Schöllhorn, W. I., & Doppelmayr, M. M. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0636-3 · Cited by 22

Algorithm for mapping cutaneous tissue oxygen concentration using hyperspectral imaging

Miclos, S. I., Parasca, S. V., Călin, M. A., Savastru, D. M., & Manea, D. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.003420 · Cited by 22

Multi-Distance Frequency-Domain Optical Measurements of Coherent Cerebral Hemodynamics

Blaney, G. P., Sassaroli, A., Pham, T., Krishnamurthy, N., & Fantini, S. (2019) · Photonics · doi:10.3390/photonics6030083 · Cited by 22

Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion

Zhang, Y., Qiu, S., & He, H. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acbfdf · Cited by 23

Changes in diffusion path length with old age in diffuse optical tomography

Bonnéry, C., Desjardins, M., Pouliot, P., Lesage, F., Leclerc, P.-O., Hoge, R. D., & Bherer, L. (2012) · Journal of Biomedical Optics · doi:10.1117/1.jbo.17.5.056002 · Cited by 21

Assessment of mental workload by EEG+FNIRS

Aghajani, H., & Omurtag, A. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591549 · Cited by 22

Broadband time-resolved multi-channel functional near-infrared spectroscopy system to monitor in vivo physiological changes of human brain activity

Lange, F., Peyrin, F., & Montcel, B. (2018) · Applied Optics · doi:10.1364/ao.57.006417 · Cited by 21

Mental Workload Classification via Hierarchical Latent Dictionary Learning: A Functional Near Infrared Spectroscopy Study

Parshi, S., Amin, M. R., Azgomi, H. F., & Faghih, R. T. (2019) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2019.8834636 · Cited by 21

Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

Alyan, E., Saad, M. N. M., Kamel, N., & Rahman, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3040540 · Cited by 22

Comparison of spectral CT imaging methods based a photon-counting detector: Experimental study

Lee, Y., Lee, S., & Kim, H.-J. (2016) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2016.01.069 · Cited by 19

Gender differences in cognitive and affective interpersonal emotion regulation in couples: an fNIRS hyperscanning

Zhang, W., Qiu, L., Tang, F., & Sun, H. (2023) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsad057 · Cited by 21

A Promising Optical Bio‐Interface: Graded‐Index Hydrogel Fiber

Zhuo, X., Han, Y., Bian, Y., Xu, A., & Shen, H. (2023) · Advanced Optical Materials · doi:10.1002/adom.202301613 · Cited by 19

Endoscopic Sheffield Index for Unsupervised In Vivo Spectral Band Selection

Wirkert, S., Clancy, N. T., Stoyanov, D. V., Arya, S., Hanna, G. B., Schlemmer, H.-P., Sauer, P., Elson, D. S., & Maier‐Hein, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13410-9_11 · Cited by 19

Physiological influence of basic perturbations assessed by non-invasive optical techniques in humans

Svanberg, E. K., Wollmer, P., Andersson‐Engels, S., & Åkeson, J. (2011) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/h11-119 · Cited by 20

Metamaterial based sucrose detection sensor using transmission spectroscopy

Agarwal, S., & Prajapati, Y. K. (2020) · Optik · doi:10.1016/j.ijleo.2020.164276 · Cited by 19

Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial IntelligencePreprint

Al-Shargie, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08511 · Cited by 24

Fusion of fNIRS and EEG Signals: Mental Stress StudyPreprint

Al-Shargie, F. (2019) · doi:10.31224/osf.io/kaqew · Cited by 19

A review on time domain diffuse optics: principles and applications on human biological tissues

Re, R., Spinelli, L., Martelli, F., Sieno, L. D., Bargigia, I., Amendola, C., Maffeis, G., & Torricelli, A. M. (2025) · Rivista Del Nuovo Cimento · doi:10.1007/s40766-025-00067-2 · Cited by 21

The importance of monitoring cerebral oxygenation in non brain injured patients

Robba, C., Battaglini, D., Rasulo, F. A., Lobo, F. A., & MATTA, B. F. (2023) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-023-01002-8 · Cited by 18

Neural Correlates of Age-Related Changes in Precise Grip Force Regulation: A Combined EEG-fNIRS Study

Berger, A., Steinberg, F., Thomas, F., & Doppelmayr, M. M. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.594810 · Cited by 18

Exploring the impact and influence of melanin on frequency-domain near-infrared spectroscopy measurements

ROY, S. K., Wu, J., Cao, J., Disu, J. D. K., Bharadwaj, S., Meinert-Spyker, E., Grover, P., Kainerstorfer, J. M., & Wood, S. (2024) · Journal of Biomedical Optics · doi:10.1117/1.jbo.29.s3.s33310 · Cited by 17

Depth dependence of coherent hemodynamics in the human head

Khaksari, K., Blaney, G. P., Sassaroli, A., Krishnamurthy, N., Pham, T., & Fantini, S. (2018) · Journal of Biomedical Optics · doi:10.1117/1.jbo.23.12.121615 · Cited by 17

Hybrid diffusion and two-flux approximation for multilayered tissue light propagation modeling

Yudovsky, D., & Durkin, A. J. (2011) · Applied Optics · doi:10.1364/ao.50.004237 · Cited by 17

Changes in neurovascular coupling with cerebral perfusion pressure indicate a link to cerebral autoregulation

Acharya, D., Ruesch, A., Schmitt, S. E., Yang, J., Smith, M. A., & Kainerstorfer, J. M. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221076566 · Cited by 17

A pilot exploration on the use of NIR monitored haemodynamics in gesture recognition for transradial prosthesis control

Nsugbe, E. (2021) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2021.200045 · Cited by 16

A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI

Hosni, S. I., Borgheai, S. B., McLinden, J., Zhu, S., Huang, X., Ostadabbas, S., & Shahriari, Y. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09595-2 · Cited by 16

Assessment of variations in air-dry wood density using time-of-flight near-infrared spectroscopy

Hans, G., Kitamura, R., Inagaki, T., Leblon, B., & Tsuchikawa, S. (2014) · Wood Material Science and Engineering · doi:10.1080/17480272.2014.923937 · Cited by 16

Transmission versus reflectance spectroscopy for quantitation

Gardner, C. M. (2018) · Journal of Biomedical Optics · doi:10.1117/1.jbo.23.1.018001 · Cited by 17

Race‐specific differences in the phase coherence between blood flow and oxygenation: A simultaneous NIRS, white light spectroscopy and LDF study

Abdulhameed, Y. A., McClintock, P. V. E., & Stefanovska, A. (2020) · Journal of Biophotonics · doi:10.1002/jbio.201960131 · Cited by 16

Evaluation of Haemoglobin and Cytochrome Responses During Forearm Ischaemia Using Multi-wavelength Time Domain NIRS

Lange, F., Dunne, L., & Tachtsidis, I. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-55231-6_10 · Cited by 16

Cross-Subject Classification of Speaking Modes Using fNIRS

Herff, C., Heger, D., Putze, F., Guan, C., & Schultz, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34481-7_51 · Cited by 16

Quantification of Mental Stress using fNIRS SignalsPreprint

Al-Shargie, F. (2019) · doi:10.31224/osf.io/um762 · Cited by 16

Prefrontal hypoactivation induced via social stress is more strongly associated with state rumination than depressive symptomatology

Int-Veen, I., Fallgatter, A. J., Ehlis, A., & Rosenbaum, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-41403-y · Cited by 16

Natural reducing agents for electroless nanoparticle deposition: Mild synthesis of metal/carbon nanostructured microspheres

Duffy, P., Reynolds, L. A., Sanders, S. E., Metz, K. M., & Colavita, P. E. (2013) · Materials Chemistry and Physics · doi:10.1016/j.matchemphys.2013.03.047 · Cited by 15

Quantification of the Transparency of the Transparent Soil in Geotechnical Modeling

Yi, L.-D., Lv, H., Ye, T., & Zhang, Y. (2018) · Advances in Civil Engineering · doi:10.1155/2018/2915924 · Cited by 15

Continuous affective states recognition using functional near infrared spectroscopy

Heger, D., Herff, C., Putze, F., Mutter, R., & Schultz, T. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2014.912884 · Cited by 15

Task-Related Hemodynamic Changes Induced by High-Definition Transcranial Direct Current Stimulation in Chronic Stroke Patients: An Uncontrolled Pilot fNIRS Study

Kim, H., Kim, J., Lee, G., Lee, J., & Kim, Y. (2022) · Brain Sciences · doi:10.3390/brainsci12040453 · Cited by 14

A Bayesian Algorithm for Anxiety Index Prediction Based on Cerebral Blood Oxygenation in the Prefrontal Cortex Measured by Near Infrared Spectroscopy

Fukuda, Y., Ida, Y., Matsumoto, T., Takemura, N., & Sakatani, K. (2014) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2014.2361757 · Cited by 16

A new approach to estimating the evoked hemodynamic response applied to dual channel functional near infrared spectroscopy

Shirvan, R. A., Setarehdan, S. K., & Nasrabadi, A. M. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.03.010 · Cited by 14

Athlete-Specific Neural Strategies under Pressure: A fNIRS Pilot Study

Park, I., Kim, Y., & Kim, S. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17228464 · Cited by 12

A numerical model and scaling relationship for energetic electron beams propagating in air

Fernsler, R. F., Slinker, S. P., & Lambrakos, S. G. (2008) · Journal of Applied Physics · doi:10.1063/1.2986657 · Cited by 14

The Utility of near Infrared Imaging in Intra-Operative Prediction of Flap Outcome: A Reverse McFarlane Skin Flap Model Study

Sowa, M. G., Friesen, J. R., Levasseur, M. A., Schattka, B. J., Sigurdson, L. J., & Hayakawa, T. E. J. (2012) · Journal of Near Infrared Spectroscopy · doi:10.1255/jnirs.1007 · Cited by 14

DEVELOPMENT AND VALIDATION OF UV-VIS SPECTROSCOPIC METHOD OF ASSAY OF CARBAMAZEPINE IN MICROPARTICLES

Mawazi, S. M., Hadi, H. A., Al-Mahmood, S. M. A., & Doolaanea, A. A. (2019) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2019v11i1.26256 · Cited by 13

Crosstalk and error analysis of fat layer on continuous wave near-infrared spectroscopy measurements

Şayli̇, Ö., Aksel, E. B., & Akın, A. (2008) · Journal of Biomedical Optics · doi:10.1117/1.3028008 · Cited by 13

In vivo detection of atherosclerotic plaque using non-contact and label-free near-infrared hyperspectral imaging

Chihara, H., Oishi, N., Ishii, A., Munemitsu, T., Arai, D., Ikeda, H., & Miyamoto, S. (2016) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2016.04.029 · Cited by 13

Light extinction coefficient and particle size correlation for real-time prediction and quantitative measurement of suspended dust concentrations

Sserunjogi, M., & Ambrose, K. (2024) · Advanced Powder Technology · doi:10.1016/j.apt.2024.104354 · Cited by 12

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Unni, A., Trende, A., Pauley, C., Weber, L., Biebl, B., Kacianka, S., Lüdtke, A., Bengler, K. J., Pretschner, A., Fränzle, M., et al. (2022) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.836518 · Cited by 12

Generalized curved beam back-projection method for near-infrared imaging using banana function

Das, T., Dileep, B. P. V., & Dutta, P. K. (2018) · Applied Optics · doi:10.1364/ao.57.001838 · Cited by 12

Supercontinuum source in the investigation of laser-tissue interactions: “ex vivo” study

Fornaini, C., Sozzi, M., Merigo, E., Pasotti, P., Selleri, S., & Cucinotta, A. (2016) · Journal of Biomedicine · doi:10.7150/jbm.17059 · Cited by 12

Algorithm for automated selection of application-specific fiber-optic reflectance probes

Gomes, A., & Backman, V. (2013) · Journal of Biomedical Optics · doi:10.1117/1.jbo.18.2.027012 · Cited by 11

Towards the integration and evaluation of online workload measures in a cognitive architecture

Wortelen, B., Unni, A., Rieger, J. W., & Lüdtke, A. (2016) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2016.7804516 · Cited by 11

Near-Infrared Spectroscopy as a Tool for Marine Mammal Research and Care

Ruesch, A., McKnight, J. C., Fahlman, A., Shinn‐Cunningham, B. G., & Kainerstorfer, J. M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.816701 · Cited by 11

Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements

Volkening, N., Unni, A., Löffler, B. S., Fudickar, S. J. F., Rieger, J. W., & Hein, A. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.08.013 · Cited by 11

Evaluating the Potential of fNIRS Neuroimaging to Study Engineering Problem Solving and Design

Grohs, J., Shealy, T., Maczka, D., Hu, M., Panneton, R., & Yang, X. (2018) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--28305 · Cited by 11

Reduction of flexible container induced error for the composition analysis of in-flexible-container liquid based on differential optical-path spectrum

Hou, X., Li, G., Zhang, S., Song, S., Tian, H., & Lin, L. (2018) · Infrared Physics & Technology · doi:10.1016/j.infrared.2018.05.003 · Cited by 11

Psychophysiological indices of cognitive style: A triangulated study incorporating neuroimaging, eye-tracking, psychometric and behavioral measures

Bendall, R. C. A., Lambert, S., Galpin, A. J., Marrow, L. P., & Cassidy, S. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.02.034 · Cited by 11

Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis

Ayala, L. A., Wirkert, S., Gröhl, J., Herrera, M. A., Hernández-Aguilera, A., Vemuri, A., Santos, E., & Maier‐Hein, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32695-1_5 · Cited by 11

A Multichannel fNIRS System for Prefrontal Mental Task Classification with Dual-level Excitation and Deep Forest Algorithm

Chen, C., Wen, Y., Cui, S., Qi, X., Liu, Z., Zhou, L., Chen, M., Zhao, J. P., & Wang, G. (2020) · Journal of Sensors · doi:10.1155/2020/1567567 · Cited by 11

A methodology to improve estimation of stimulus-evoked hemodynamic response from fNIRS measurements

Scarpa, F., Brigadoi, S., Cutini, S., Scatturin, P., Zorzi, M., DellrAcqua, R., & Sparacino, G. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090180 · Cited by 11

Critical analysis of the relationship between arterial saturation and the ratio-of-ratios used in pulse oximetry

Blaney, G. P., Sassaroli, A., & Fantini, S. (2024) · Journal of Biomedical Optics · doi:10.1117/1.jbo.29.s3.s33313 · Cited by 11

Observation of Atherosclerotic Plaque Phantoms through Saline or Blood Layers by Near-Infrared Hyperspectral Imaging

Ishii, K., Kitayabu, A., Nagao, R., & Awazu, K. (2014) · Optics and Photonics Journal · doi:10.4236/opj.2014.410026 · Cited by 10

Intramuscular Near-Infrared Spectroscopy for Muscle Flap Monitoring in a Porcine Model

Bai, W., Guo, H., Ouyang, W., Weng, Y., Wu, C., Liu, Y., Zang, H., Jacobson, L., Xu, Y., Lu, D., et al. (2021) · Journal of Reconstructive Microsurgery · doi:10.1055/s-0041-1732361 · Cited by 10

Mapping the Distribution of Melanin Concentration in Different Fitzpatrick Skin Types Using Hyperspectral Imaging Technique

Călin, M. A., Manea, D., Savastru, R., & Parasca, S. V. (2022) · Photochemistry and Photobiology · doi:10.1111/php.13725 · Cited by 10

Using an Ultra-Compact Optical System to Improve Lateral Flow Immunoassay Results Quantitatively

Chiu, W.-H., Kong, W.-Y., Chueh, Y., Wen, J.-W., Tsai, C.-M., Hong, C., Chen, P.-Y., & Ko, C. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e12116 · Cited by 10

Quantitative spectrophotometry of scattering media via frequency-domain and constant-intensity measurements

Crowe, J. A., Morris, D. E., Woolfson, M. S., Rodmell, P. I., & Walker, J. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.001969 · Cited by 10

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

Hernández‐Martín, E., Marcano, F., Casanova, Ó., Modroño, C., Bello, J. P., & González–Mora, J. L. (2017) · Neurophotonics · doi:10.1117/1.nph.4.1.015003 · Cited by 10

Neuroimaging with functional near infrared spectroscopy: From formation to interpretation

Herrera-Vega, J., Treviño-Palacios, C. G., & Orihuela‐Espina, F. (2017) · Infrared Physics & Technology · doi:10.1016/j.infrared.2017.06.011 · Cited by 10

Monitoring and detection of EEG signals before and after yoga during depression in human brain using MATLAB

Tiwari, A., & Tiwari, R. (2017) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2017.8282702 · Cited by 10

Analytical light reflectance models for overlapping illumination and collection area geometries

Gomes, A., & Backman, V. (2012) · Applied Optics · doi:10.1364/ao.51.008013 · Cited by 9

Exploring the impact of 3D movie watching on the brain source activities and energy consumption by ESI and fNIRS

Yao, L., Zhou, L., Qian, Z., Zhu, Q., Liu, Y., Zhang, Y., Li, W., & Xing, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103194 · Cited by 9

A novel experimental approach to measure nebulized droplet deposition pattern and deposition fraction in an idealized mouth-to-throat model

Jin, Y. Y., Chen, X., Feng, Y., Jia, Z., Zhang, J., Xie, X., & Zhang, Y. (2023) · Physics of Fluids · doi:10.1063/5.0163935 · Cited by 9

Optical Properties and Molar Hemoglobin Concentration of Skeletal Muscles Measured In Vivo With Wearable Near Infrared Spectroscopy

Pollonini, L. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2794969 · Cited by 9

The meaning of “coherent” and its quantification in coherent hemodynamics spectroscopy

Sassaroli, A., Tgavalekos, K. T., & Fantini, S. (2018) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545818500360 · Cited by 9

The changes of cerebral hemodynamics during ketamine induced anesthesia in a rat model

Bae, J., Shin, T. J., Kim, S., Choi, D., Cho, D., Ham, J., Manca, M., Jeong, S., Lee, B., & Kim, J. G. (2018) · Journal of Biophotonics · doi:10.1002/jbio.201800081 · Cited by 9

Simultaneous Discrimination of Multiple Chromophores With Frequency Division Multiplexed Four-Color Functional Near-Infrared Spectroscopy

Park, C. H., Woo, S.-W., Kim, N., Jang, H., Kim, H. H., Kim, Y. J., Lee, Y. M., Hong, K., & Kim, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3279877 · Cited by 9

Premotor and Posterior Parietal Cortex Activity is Increased for Slow, as well as Fast Walking Poststroke: An fNIRS Study

Lim, S. B., Peters, S., Yang, C.-L., Boyd, L. A., Liu-Ambrose, T. Y. L., & Eng, J. J. (2023) · Neural Plasticity · doi:10.1155/2023/2403175 · Cited by 8

Can thick metal-halide perovskite single crystals have narrower optical bandgaps with near-infrared absorption?

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