{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:31:06Z","timestamp":1762918266728,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1539068"],"award-info":[{"award-number":["1539068"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Portable functional near-infrared spectroscopy (fNIRS) systems have the potential to image the brain in naturalistic settings. Experimental studies are essential to validate such fNIRS systems. Working memory (WM) is a short-term active memory that is associated with the temporary storage and manipulation of information. The prefrontal cortex (PFC) brain area is involved in the processing of WM. We assessed the PFC brain during n-back WM tasks in a group of 25 college students using our laboratory-developed portable fNIRS system, WearLight. We designed an experimental protocol with 32 n-back WM task blocks with four different pseudo-randomized task difficulty levels. The hemodynamic response of the brain was computed from the experimental data and the evaluated brain responses due to these tasks. We observed the incremental mean hemodynamic activation induced by the increasing WM load. The left-PFC area was more activated in the WM task compared to the right-PFC. The task performance was seen to be related to the hemodynamic responses. The experimental results proved the functioning of the WearLight system in cognitive load imaging. Since the portable fNIRS system was wearable and operated wirelessly, it was possible to measure the cognitive load in the naturalistic environment, which could also lead to the development of a user-friendly brain\u2013computer interface system.<\/jats:p>","DOI":"10.3390\/s21113810","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T21:42:06Z","timestamp":1622497326000},"page":"3810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["The Validation of a Portable Functional NIRS System for Assessing Mental Workload"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-4333","authenticated-orcid":false,"given":"Manob Jyoti","family":"Saikia","sequence":"first","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2752-5483","authenticated-orcid":false,"given":"Walter G.","family":"Besio","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6423-0823","authenticated-orcid":false,"given":"Kunal","family":"Mankodiya","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1038\/nrn1201","article-title":"Working memory: Looking back and looking forward","volume":"4","author":"Baddeley","year":"2003","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_2","unstructured":"Fuster, J.M. (1995). Memory in the Cerebral Cortex: An Empirical Approach to Neural Networks in the Human and Nonhuman Primate, The MIT Press."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Goldman-Rakic, P.S. (1995). Architecture of the Prefrontal Cortex and the Central Executive. Structure and Functions of the Human Prefrontal Cortex, New York Academy of Sciences.","DOI":"10.1111\/j.1749-6632.1995.tb38132.x"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1037\/h0043158","article-title":"The magical number seven, plus or minus two: Some limits on our capacity for processing information","volume":"63","author":"Miller","year":"1956","journal-title":"Psychol. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1006\/brcg.1999.1096","article-title":"Maintenance versus Manipulation of Information Held in Working Memory: An Event-Related fMRI Study","volume":"41","author":"Postle","year":"1999","journal-title":"Brain Cogn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1002\/hbm.22337","article-title":"Maintenance and manipulation of somatosensory information in ventrolateral prefrontal cortex","volume":"35","author":"Spitzer","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1006\/cogp.1997.0658","article-title":"Working Memory: A View from Neuroimaging","volume":"33","author":"Smith","year":"1997","journal-title":"Cogn. Psychol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1016\/j.neuropsychologia.2005.11.019","article-title":"Development of a superior frontal\u2013intraparietal network for visuo-spatial working memory","volume":"44","author":"Klingberg","year":"2006","journal-title":"Neuropsychologia"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neuroscience.2005.07.003","article-title":"Prefrontal cortex and working memory processes","volume":"139","author":"Funahashi","year":"2006","journal-title":"Neuroscience"},{"key":"ref_10","unstructured":"Pessoa, L., and Ungerleider, L.G. (2004). Top-Down Mechanisms for Working Memory and Attentional Processes. The Cognitive Neurosciences, MIT Press. [3rd ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pereira, T., Castro, M.A., Villafaina, S., Carvalho Santos, A., and Fuentes-Garc\u00eda, J.P. (2020). Dynamics of the Prefrontal Cortex during Chess-Based Problem-Solving Tasks in Competition-Experienced Chess Players: An fNIR Study. Sensors, 20.","DOI":"10.3390\/s20143917"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1152\/jn.1989.61.2.331","article-title":"Mnemonic coding of visual space in the monkey\u2019s dorsolateral prefrontal cortex","volume":"61","author":"Funahashi","year":"1989","journal-title":"J. Neurophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.mri.2005.12.034","article-title":"Illuminating the BOLD signal: Combined fMRI\u2013fNIRS studies","volume":"24","author":"Steinbrink","year":"2006","journal-title":"Magn. Reson. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3389\/fnhum.2017.00359","article-title":"Measuring Mental Workload with EEG+fNIRS","volume":"11","author":"Aghajani","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"724","DOI":"10.3389\/fnins.2020.00724","article-title":"Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions","volume":"14","author":"Chen","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1126\/science.929199","article-title":"Noninvasive, Infrared Monitoring of Cerebral and Myocardial Oxygen Sufficiency and Circulatory Parameters","volume":"198","year":"1977","journal-title":"Science"},{"key":"ref_17","first-page":"100272","article-title":"Towards Neuroscience of the Everyday World (NEW) using functional Near Infrared Spectroscopy","volume":"2021","author":"Zheng","year":"2021","journal-title":"Curr. Opin. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1111\/jpr.12206","article-title":"A Review on the Use of Wearable Functional Near-Infrared Spectroscopy in Naturalistic Environments","volume":"60","author":"Pinti","year":"2018","journal-title":"Jpn. Psychol. Res."},{"key":"ref_19","first-page":"143","article-title":"Internet of things-based functional near-infrared spectroscopy headband for mental workload assessment","volume":"Volume 11629","author":"Saikia","year":"2021","journal-title":"Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics"},{"key":"ref_20","first-page":"91","article-title":"WearLight: Toward a Wearable, Configurable Functional NIR Spectroscopy System for Noninvasive Neuroimaging","volume":"13","author":"Saikia","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Saikia, M., and Mankodiya, K. (2019). 3D-printed human-centered design of fNIRS optode for the portable neuroimaging. Progress in Biomedical Optics and Imaging\u2014Proceedings of SPIE, SPIE.","DOI":"10.1117\/12.2510955"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.biopsycho.2008.03.006","article-title":"Quantifying the heritability of task-related brain activation and performance during the N-back working memory task: A twin fMRI study","volume":"79","author":"Blokland","year":"2008","journal-title":"Biol. Psychol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Siddiquee, M.R., Atri, R., Marquez, J.S., Hasan, S.M.S., Ramon, R., and Bai, O. (2020). Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability. Sensors, 20.","DOI":"10.3390\/s20185082"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117795","DOI":"10.1016\/j.neuroimage.2021.117795","article-title":"Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach","volume":"230","author":"Meidenbauer","year":"2021","journal-title":"NeuroImage"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saikia, M.J., Kuanar, S., Borthakur, D., Vinti, M., and Tendhar, T. (2021). A Machine Learning Approach to Classify Working Memory Load from Optical Neuroimaging Data, SPIE-International Society for Optics and Photonics.","DOI":"10.1117\/12.2578952"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fernandez Rojas, R., Liao, M., Romero, J., Huang, X., and Ou, K.L. (2019). Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study. Sensors, 19.","DOI":"10.3390\/s19020394"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Baker, J.M., Bruno, J.L., Gundran, A., Hosseini, S.M.H., and Reiss, A.L. (2018). fNIRS measurement of cortical activation and functional connectivity during a visuospatial working memory task. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0203233"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62104","DOI":"10.1117\/1.2804899","article-title":"Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications","volume":"12","author":"Wolf","year":"2007","journal-title":"J. Biomed. Opt."},{"key":"ref_29","first-page":"159","article-title":"K-means clustering for unsupervised participant grouping from fNIRS brain signal in working memory task","volume":"Volume 11629","author":"Saikia","year":"2021","journal-title":"Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics"},{"key":"ref_30","first-page":"1","article-title":"An embedded system based digital onboard hardware  calibration for low-cost functional diffuse optical tomography system","volume":"Volume 11632","author":"Saikia","year":"2021","journal-title":"Optics and Biophotonics in Low-Resource Settings VII"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Saikia, M.J., and Kanhirodan, R. (2019, January 14\u201317). Development of handheld near-infrared spectroscopic medical imaging system. Proceedings of the Biophotonics Congress: Optics in the Life Sciences Congress 2019 (BODA,BRAIN,NTM,OMA,OMP), Tucson, AZ, USA.","DOI":"10.1364\/BODA.2019.DS1A.6"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saikia, M., Mankodiya, K., and Kanhirodan, R. (2019). A point-of-care handheld region-of-interest (ROI) 3D functional diffuse optical tomography (fDOT) system. Progress in Biomedical Optics and Imaging\u2014Proceedings of SPIE, SPIE.","DOI":"10.1117\/12.2510926"},{"key":"ref_33","first-page":"CM3E.5","article-title":"Depth sensitivity improvement of region-of-interest diffuse optical tomography from superficial signal regression","volume":"Volume Part F99-C","author":"Saikia","year":"2018","journal-title":"Optics InfoBase Conference Papers"},{"key":"ref_34","first-page":"219","article-title":"Square-waves for frequency multiplexing for fully parallel 3D diffuse optical tomography measurement","volume":"Volume 11639","author":"Fantini","year":"2021","journal-title":"Optical Tomography and Spectroscopy of Tissue XIV"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saikia, M.J. (2021). A spectroscopic diffuse optical tomography system for the continuous 3D functional imaging of tissue -a phantom study. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2021.3082314"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Saikia, M., and Kanhirodan, R. (2014, January 27\u201329). High performance single and multi-GPU acceleration for Diffuse Optical Tomography. Proceedings of the 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014, Mysuru, India.","DOI":"10.1109\/IC3I.2014.7019809"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125001","DOI":"10.1117\/1.JBO.22.12.125001","article-title":"Toward real-time diffuse optical tomography: Accelerating light propagation modeling employing parallel computing on GPU and CPU","volume":"22","author":"Doulgerakis","year":"2017","journal-title":"J. Biomed. Opt."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"376456","DOI":"10.1155\/2014\/376456","article-title":"High-speed GPU-based fully three-dimensional diffuse optical tomographic system","volume":"2014","author":"Saikia","year":"2014","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Saikia, M., Manjappa, R., and Kanhirodan, R. (2017). A cost-effective LED and photodetector based fast direct 3D diffuse optical imaging system. Optics InfoBase Conference Papers, OSA\u2014The Optical Society.","DOI":"10.1117\/12.2285940"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1088\/0031-9155\/33\/12\/008","article-title":"Estimation of optical pathlength through tissue from direct time of flight measurement","volume":"33","author":"Delpy","year":"1988","journal-title":"Phys. Med. Biol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"D280","DOI":"10.1364\/AO.48.00D280","article-title":"HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain","volume":"48","author":"Huppert","year":"2009","journal-title":"Appl. Opt."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"N255","DOI":"10.1088\/0031-9155\/49\/14\/N07","article-title":"Comment on the modified Beer-Lambert law for scattering media","volume":"49","author":"Sassaroli","year":"2004","journal-title":"Phys. Med. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"035005","DOI":"10.1117\/1.NPh.6.3.035005","article-title":"Differential pathlength factor in continuous wave functional near-infrared spectroscopy: Reducing hemoglobin\u2019s cross talk in high-density recordings","volume":"6","author":"Chiarelli","year":"2019","journal-title":"Neurophotonics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"013701","DOI":"10.1063\/1.4939054","article-title":"Region-of-interest diffuse optical tomography system","volume":"87","author":"Saikia","year":"2016","journal-title":"Rev. Sci. Instruments"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"035007","DOI":"10.1117\/1.NPh.6.3.035007","article-title":"High-density functional diffuse optical tomography based on frequency-domain measurements improves image quality and spatial resolution","volume":"6","author":"Doulgerakis","year":"2019","journal-title":"Neurophotonics"},{"key":"ref_46","first-page":"213","article-title":"Design and development of a functional diffuse optical tomography probe for real-time 3D imaging of tissue","volume":"Volume 11639","author":"Saikia","year":"2021","journal-title":"Optical Tomography and Spectroscopy of Tissue XIV"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Saikia, M.J., and Kanhirodan, R. (2014, January 13\u201316). Development of DOT system for ROI scanning. Proceedings of the International Conference on Fibre Optics and Photonics, Kharagpur, India.","DOI":"10.1364\/PHOTONICS.2014.T3A.4"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1002\/hbm.20131","article-title":"N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies","volume":"25","author":"Owen","year":"2005","journal-title":"Hum. Brain Mapp."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3899","DOI":"10.1364\/BOE.10.003899","article-title":"Systematic study of the effect of ultrasound gel on the performances of time-domain diffuse optics and diffuse correlation spectroscopy","volume":"10","author":"Sieno","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lee, G., Jin, S.H., and An, J. (2018). Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network. Sensors, 18.","DOI":"10.3390\/s18092957"},{"key":"ref_51","first-page":"025009","article-title":"Investigation of the sensitivity-specificity of canonical- and deconvolution-based linear models in evoked functional near-infrared spectroscopy","volume":"6","author":"Santosa","year":"2019","journal-title":"Neurophotonics"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"102507","DOI":"10.1016\/j.ajp.2020.102507","article-title":"Prefrontal cortex activation during working memory task in schizophrenia: A fNIRS study","volume":"56","author":"Kumar","year":"2021","journal-title":"Asian J. Psychiatry"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3389\/fnhum.2017.00389","article-title":"Multisubject \u201cLearning\u201d for Mental Workload Classification Using Concurrent EEG, fNIRS, and Physiological Measures","volume":"11","author":"Liu","year":"2017","journal-title":"Front. Hum. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3810\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:09:28Z","timestamp":1760162968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,31]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113810"],"URL":"https:\/\/doi.org\/10.3390\/s21113810","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,5,31]]}}}