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Ben Yedder H, Cardoen B, Shokoufi M, Golnaraghi F, Hamarneh G. Deep orthogonal multi-wavelength fusion for tomogram-free diagnosis in diffuse optical imaging. Comput Biol Med 2024; 178:108676. [PMID: 38878395 DOI: 10.1016/j.compbiomed.2024.108676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 07/24/2024]
Abstract
Novel portable diffuse optical tomography (DOT) devices for breast cancer lesions hold great promise for non-invasive, non-ionizing breast cancer screening. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately reconstruct the highly heterogeneous tissue of a cancer lesion in healthy breast tissue using DOT, multiple wavelengths can be leveraged to maximize signal penetration while minimizing sensitivity to noise. However, these wavelength responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss regularizes multi-wavelength DOT leading to improved reconstruction and accuracy of end-to-end discrimination of malignant versus benign lesions. We further show that our raw-to-task model significantly reduces computational complexity without sacrificing accuracy, making it ideal for real-time throughput, desired in medical settings where handheld devices have severely restricted power budgets. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classification of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, respectively, using the raw-to-task model. Code is available at https://github.com/sfu-mial/FuseNet.
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Affiliation(s)
- Hanene Ben Yedder
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6.
| | - Ben Cardoen
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6
| | - Majid Shokoufi
- School of Mechatronic Systems Engineering, Simon Fraser University, BC Canada V5A 1S6
| | - Farid Golnaraghi
- School of Mechatronic Systems Engineering, Simon Fraser University, BC Canada V5A 1S6
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6.
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2
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Xu E, Vanegas M, Mireles M, Dementyev A, McCann A, Yücel M, Carp SA, Fang Q. Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings. NEUROPHOTONICS 2024; 11:035002. [PMID: 38975286 PMCID: PMC11224775 DOI: 10.1117/1.nph.11.3.035002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/31/2024] [Accepted: 06/07/2024] [Indexed: 07/09/2024]
Abstract
Significance Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. Aim The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. Approach The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Results Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18 g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.
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Affiliation(s)
- Edward Xu
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Morris Vanegas
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Artem Dementyev
- Massachusetts Institute of Technology, Media Lab, Cambridge, Massachusetts, United States
| | - Ashlyn McCann
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Meryem Yücel
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
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D'Souza H, D'Souza D. Stop trying to carve Nature at its joints! The importance of a process-based developmental science for understanding neurodiversity. ADVANCES IN CHILD DEVELOPMENT AND BEHAVIOR 2024; 66:233-268. [PMID: 39074923 DOI: 10.1016/bs.acdb.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Nature is dynamic and interdependent. Yet we typically study and understand it as a hierarchy of independent static things (objects, factors, capacities, traits, attributes) with well-defined boundaries. Hence, since Plato, the dominant research practice has been to 'carve Nature at its joints' (Phaedrus 265e), rooted in the view that the world comes to us pre-divided - into static forms or essences - and that the goal of science is to simply discover (identify and classify) them. This things-based approach dominates developmental science, and especially the study of neurodevelopmental conditions. The goal of this paper is to amplify the marginalised process-based approach: that Nature has no joints. It is a hierarchy of interacting processes from which emerging functions (with fuzzy boundaries) softly assemble, become actively maintained, and dissipate over various timescales. We further argue (with a specific focus on children with Down syndrome) that the prevailing focus on identifying, isolating, and analysing things rather than understanding dynamic interdependent processes is obstructing progress in developmental science and particularly our understanding of neurodiversity. We explain how re-examining the very foundation of traditional Western thought is necessary to progress our research on neurodiversity, and we provide specific recommendations on how to steer developmental science towards the process-based approach.
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Affiliation(s)
- Hana D'Souza
- Centre for Human Developmental Science, School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Dean D'Souza
- Centre for Human Developmental Science, School of Psychology, Cardiff University, Cardiff, United Kingdom
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Srinivasan S, Acharya D, Butters E, Collins-Jones L, Mancini F, Bale G. Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography. FRONTIERS IN NEUROERGONOMICS 2024; 5:1283290. [PMID: 38444841 PMCID: PMC10910052 DOI: 10.3389/fnrgo.2024.1283290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.
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Affiliation(s)
- Sruthi Srinivasan
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Deepshikha Acharya
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Emilia Butters
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Liam Collins-Jones
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Flavia Mancini
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Gemma Bale
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
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Algarawi M, Saraswatula JS, Pathare RR, Zhang Y, Shah GA, Eresen A, Gulsen G, Nouizi F. Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach. Bioengineering (Basel) 2024; 11:126. [PMID: 38391612 PMCID: PMC10886351 DOI: 10.3390/bioengineering11020126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.
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Affiliation(s)
- Maha Algarawi
- Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Janaki S Saraswatula
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Rajas R Pathare
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Yang Zhang
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Gyanesh A Shah
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Aydin Eresen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
| | - Gultekin Gulsen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA 92697, USA
| | - Farouk Nouizi
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA 92697, USA
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Yang D, Ghafoor U, Eggebrecht AT, Hong KS. Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement. Health Inf Sci Syst 2023; 11:35. [PMID: 37545487 PMCID: PMC10397167 DOI: 10.1007/s13755-023-00233-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Transcranial alternating current stimulation (tACS) exhibits the capability to interact with endogenous brain oscillations using an external low-intensity sinusoidal current and influences cerebral function. Despite its potential benefits, the physiological mechanisms and effectiveness of tACS are currently a subject of debate and disagreement. The aims of our study are to (i) evaluate the neurological and behavioral impact of tACS by conducting repetitive sham-controlled experiments and (ii) propose criteria to evaluate effectiveness, which can serve as a benchmark to determine optimal individual-based tACS protocols. In this study, 15 healthy adults participated in the experiment over two visiting: sham and tACS (i.e., 5 Hz, 1 mA). During each visit, we used multimodal recordings of the participants' brain, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), along with a working memory (WM) score to quantify neurological effects and cognitive changes immediately after each repetitive sham/tACS session. Our results indicate increased WM scores, hemodynamic response strength, and EEG power in theta and delta bands both during and after the tACS period. Additionally, the observed effects do not increase with prolonged stimulation time, as the effects plateau towards the end of the experiment. In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Moreover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
| | - Adam Thomas Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130 USA
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Institute for Future, School of Automation, Qingdao University, Qingdao, 266071 Shandong China
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7
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Xia Y, Wang K, Billing A, Billing M, Cooper RJ, Zhao H. Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications. NEUROPHOTONICS 2023; 10:046601. [PMID: 37876984 PMCID: PMC10593123 DOI: 10.1117/1.nph.10.4.046601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
Significance To effectively apply functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT) devices, a three-dimensional (3D) model of the position of each optode on a subject's scalp and the positions of that subject's cranial landmarks are critical. Obtaining this information accurately in infants, who rarely stop moving, is an ongoing challenge. Aim We propose a smartphone-based registration system that can potentially achieve a full-head 3D scan of a 6-month-old infant instantly. Approach The proposed system is remotely controlled by a custom-designed Bluetooth controller. The scanned images can either be manually or automatically aligned to generate a 3D head surface model. Results A full-head 3D scan of a 6-month-old infant can be achieved within 2 s via this system. In testing on a realistic but static infant head model, the average Euclidean error of optode position using this device was 1.8 mm. Conclusions This low-cost 3D registration system therefore has the potential to permit accurate and near-instant fNIRS/DOT spatial registration.
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Affiliation(s)
- Yunjia Xia
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Kui Wang
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Addison Billing
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- University of Cambridge, Prediction and Learning Laboratory, Department of Psychology, Cambridge, United Kingdom
| | - Matthew Billing
- London South Bank University, School of Engineering, London, United Kingdom
| | - Robert J. Cooper
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Hubin Zhao
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
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8
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Dopierała AAW, López Pérez D, Mercure E, Pluta A, Malinowska-Korczak A, Evans S, Wolak T, Tomalski P. Watching talking faces: The development of cortical representation of visual syllables in infancy. BRAIN AND LANGUAGE 2023; 244:105304. [PMID: 37481794 DOI: 10.1016/j.bandl.2023.105304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 07/25/2023]
Abstract
From birth, we perceive speech by hearing and seeing people talk. In adults cortical representations of visual speech are processed in the putative temporal visual speech area (TVSA), but it remains unknown how these representations develop. We measured infants' cortical responses to silent visual syllables and non-communicative mouth movements using functional Near-Infrared Spectroscopy. Our results indicate that cortical specialisation for visual speech may emerge during infancy. The putative TVSA was active to both visual syllables and gurning around 5 months of age, and more active to gurning than to visual syllables around 10 months of age. Multivariate pattern analysis classification of distinct cortical responses to visual speech and gurning was successful at 10, but not at 5 months of age. These findings imply that cortical representations of visual speech change between 5 and 10 months of age, showing that the putative TVSA is initially broadly tuned and becomes selective with age.
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Affiliation(s)
- Aleksandra A W Dopierała
- Faculty of Psychology, University of Warsaw, Warsaw, Poland; Department of Psychology, University of British Columbia, Vancouver, Canada.
| | - David López Pérez
- Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland.
| | | | - Agnieszka Pluta
- Faculty of Psychology, University of Warsaw, Warsaw, Poland; Institute of Physiology and Pathology of Hearing, Bioimaging Research Center, World Hearing Centre, Warsaw, Poland.
| | | | - Samuel Evans
- University of Westminister, London, UK; Kings College London, London, UK.
| | - Tomasz Wolak
- Institute of Physiology and Pathology of Hearing, Bioimaging Research Center, World Hearing Centre, Warsaw, Poland.
| | - Przemysław Tomalski
- Faculty of Psychology, University of Warsaw, Warsaw, Poland; Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland.
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9
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Anaya D, Batra G, Bracewell P, Catoen R, Chakraborty D, Chevillet M, Damodara P, Dominguez A, Emms L, Jiang Z, Kim E, Klumb K, Lau F, Le R, Li J, Mateo B, Matloff L, Mehta A, Mugler EM, Murthy A, Nakagome S, Orendorff R, Saung EF, Schwarz R, Sethi R, Sevile R, Srivastava A, Sundberg J, Yang Y, Yin A. Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight). JOURNAL OF BIOMEDICAL OPTICS 2023; 28:065003. [PMID: 37325190 PMCID: PMC10261976 DOI: 10.1117/1.jbo.28.6.065003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/26/2023] [Accepted: 05/01/2023] [Indexed: 06/17/2023]
Abstract
Significance We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions The advances presented here should serve to make fNIRS more accessible for BCI applications.
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Affiliation(s)
- Daniel Anaya
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Gautam Batra
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Ryan Catoen
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Mark Chevillet
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | | | - Laurence Emms
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Zifan Jiang
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ealgoo Kim
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Keith Klumb
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Frances Lau
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Rosemary Le
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Jamie Li
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Brett Mateo
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Laura Matloff
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Asha Mehta
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Akansh Murthy
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Sho Nakagome
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ryan Orendorff
- Meta Platforms, Inc., Menlo Park, California, United States
| | - E-Fann Saung
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Roland Schwarz
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ruben Sethi
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Rudy Sevile
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - John Sundberg
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ying Yang
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Allen Yin
- Meta Platforms, Inc., Menlo Park, California, United States
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10
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Huang R, Hong KS, Yang D, Huang G. Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review. Front Neurosci 2022; 16:878750. [PMID: 36263362 PMCID: PMC9576156 DOI: 10.3389/fnins.2022.878750] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/04/2022] Open
Abstract
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
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Affiliation(s)
- Ruisen Huang
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
- *Correspondence: Keum-Shik Hong,
| | - Dalin Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao, China
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11
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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Development of a miniaturized and modular probe for fNIRS instrument. Lasers Med Sci 2022; 37:2269-2277. [PMID: 35028765 DOI: 10.1007/s10103-021-03493-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/29/2021] [Indexed: 10/19/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive and promising method for continuously monitoring hemodynamic and metabolic changes in tissues. However, the existing fNIRS equipment uses optical fiber, which is bulky, expensive, and time-consuming. We present a miniaturized, modular, novel silicon photomultiplier (SiPM) detector and develop a fNIRS instrument aimed at investigating the cerebral hemodynamic response for patients with epilepsy. Light emitting probe is a circle with a diameter of 5 mm. Independent and modular light source and detector are more flexible in placement. The system can be expanded to high-density measurement with 16 light sources, 16 detectors, and 52 channels. The sampling rate of each channel is 25 Hz. Instrument performance was evaluated using brain tissue phantom and in vivo experiments. High signal-to-noise ratio (60 dB) in source detector separation (SDS) of 30 mm, good stability (0.1%), noise equivalent power (0.89 pW), and system drift (0.56%) were achieved in the phantom experiment. Forearm blood-flow occlusion experiments were performed on the forearm of three healthy volunteers to demonstrate the ability to track rapid hemodynamic changes. Breath holding experiments on the forehead of healthy volunteers demonstrated the system can well detect brain function activity. The computer software was developed to display the original light signal intensity and the concentration changes of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in real time. This system paves the way for our further diagnosis of epilepsy.
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13
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Vanegas M, Mireles M, Fang Q. MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes. NEUROPHOTONICS 2022; 9:017801. [PMID: 36278785 PMCID: PMC8823693 DOI: 10.1117/1.nph.9.1.017801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 01/11/2022] [Indexed: 05/20/2023]
Abstract
SIGNIFICANCE The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe's performance a daunting task. AIM We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes. APPROACH Our software-modular optode configuration analyzer (MOCA)-implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities. RESULTS Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts. CONCLUSION Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.
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Affiliation(s)
- Morris Vanegas
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Address all correspondence to Qianqian Fang,
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14
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Fan W, Dehghani H, Eggebrecht AT. Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality. NEUROPHOTONICS 2021; 8:045002. [PMID: 34849379 PMCID: PMC8612746 DOI: 10.1117/1.nph.8.4.045002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 11/04/2021] [Indexed: 05/16/2023]
Abstract
Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.
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Affiliation(s)
- Weihao Fan
- Washington University, Department of Physics, St. Louis, Missouri, United States
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Adam T. Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
- Washington University, Department of Biomedical Engineering, St. Louis, Missouri, United States
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15
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Wearable, Integrated EEG-fNIRS Technologies: A Review. SENSORS 2021; 21:s21186106. [PMID: 34577313 PMCID: PMC8469799 DOI: 10.3390/s21186106] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 02/04/2023]
Abstract
There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.
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