1
|
Acuña K, Sapahia R, Jiménez IN, Antonietti M, Anzola I, Cruz M, García MT, Krishnan V, Leveille LA, Resch MD, Galor A, Habash R, DeBuc DC. Functional Near-Infrared Spectrometry as a Useful Diagnostic Tool for Understanding the Visual System: A Review. J Clin Med 2024; 13:282. [PMID: 38202288 PMCID: PMC10779649 DOI: 10.3390/jcm13010282] [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: 11/17/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
This comprehensive review explores the role of Functional Near-Infrared Spectroscopy (fNIRS) in advancing our understanding of the visual system. Beginning with an introduction to fNIRS, we delve into its historical development, highlighting how this technology has evolved over time. The core of the review critically examines the advantages and disadvantages of fNIRS, offering a balanced view of its capabilities and limitations in research and clinical settings. We extend our discussion to the diverse applications of fNIRS beyond its traditional use, emphasizing its versatility across various fields. In the context of the visual system, this review provides an in-depth analysis of how fNIRS contributes to our understanding of eye function, including eye diseases. We discuss the intricacies of the visual cortex, how it responds to visual stimuli and the implications of these findings in both health and disease. A unique aspect of this review is the exploration of the intersection between fNIRS, virtual reality (VR), augmented reality (AR) and artificial intelligence (AI). We discuss how these cutting-edge technologies are synergizing with fNIRS to open new frontiers in visual system research. The review concludes with a forward-looking perspective, envisioning the future of fNIRS in a rapidly evolving technological landscape and its potential to revolutionize our approach to studying and understanding the visual system.
Collapse
Affiliation(s)
- Kelly Acuña
- School of Medicine, Georgetown University, Washington, DC 20007, USA;
| | - Rishav Sapahia
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Irene Newman Jiménez
- Department of Cognitive Science, Faculty of Arts & Science, McGill University, Montreal, QC H4A 3J1, Canada;
| | - Michael Antonietti
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ignacio Anzola
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Marvin Cruz
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Michael T. García
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Varun Krishnan
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Lynn A. Leveille
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Miklós D. Resch
- Department of Ophthalmology, Semmelweis University, 1085 Budapest, Hungary;
| | - Anat Galor
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ranya Habash
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Delia Cabrera DeBuc
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| |
Collapse
|
2
|
Williams SD, Setzer B, Fultz NE, Valdiviezo Z, Tacugue N, Diamandis Z, Lewis LD. Neural activity induced by sensory stimulation can drive large-scale cerebrospinal fluid flow during wakefulness in humans. PLoS Biol 2023; 21:e3002035. [PMID: 36996009 PMCID: PMC10062585 DOI: 10.1371/journal.pbio.3002035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 02/15/2023] [Indexed: 03/31/2023] Open
Abstract
Cerebrospinal fluid (CSF) flow maintains healthy brain homeostasis, facilitating solute transport and the exchange of brain waste products. CSF flow is thus important for brain health, but the mechanisms that control its large-scale movement through the ventricles are not well understood. While it is well established that CSF flow is modulated by respiratory and cardiovascular dynamics, recent work has also demonstrated that neural activity is coupled to large waves of CSF flow in the ventricles during sleep. To test whether the temporal coupling between neural activity and CSF flow is in part due to a causal relationship, we investigated whether CSF flow could be induced by driving neural activity with intense visual stimulation. We manipulated neural activity with a flickering checkerboard visual stimulus and found that we could drive macroscopic CSF flow in the human brain. The timing and amplitude of CSF flow was matched to the visually evoked hemodynamic responses, suggesting neural activity can modulate CSF flow via neurovascular coupling. These results demonstrate that neural activity can contribute to driving CSF flow in the human brain and that the temporal dynamics of neurovascular coupling can explain this effect.
Collapse
Affiliation(s)
- Stephanie D. Williams
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States of America
| | - Beverly Setzer
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Graduate Program in Neuroscience, Boston University, Boston, Massachusetts, United States of America
| | - Nina E. Fultz
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Zenia Valdiviezo
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Nicole Tacugue
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Zachary Diamandis
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| |
Collapse
|
3
|
Pasanta D, He JL, Ford T, Oeltzschner G, Lythgoe DJ, Puts NA. Functional MRS studies of GABA and glutamate/Glx - A systematic review and meta-analysis. Neurosci Biobehav Rev 2023; 144:104940. [PMID: 36332780 PMCID: PMC9846867 DOI: 10.1016/j.neubiorev.2022.104940] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Functional magnetic resonance spectroscopy (fMRS) can be used to investigate neurometabolic responses to external stimuli in-vivo, but findings are inconsistent. We performed a systematic review and meta-analysis on fMRS studies of the primary neurotransmitters Glutamate (Glu), Glx (Glutamate + Glutamine), and GABA. Data were extracted, grouped by metabolite, stimulus domain, and brain region, and analysed by determining standardized effect sizes. The quality of individual studies was rated. When results were analysed by metabolite type small to moderate effect sizes of 0.29-0.47 (p < 0.05) were observed for changes in Glu and Glx regardless of stimulus domain and brain region, but no significant effects were observed for GABA. Further analysis suggests that Glu, Glx and GABA responses differ by stimulus domain or task and vary depending on the time course of stimulation and data acquisition. Here, we establish effect sizes and directionality of GABA, Glu and Glx response in fMRS. This work highlights the importance of standardised reporting and minimal best practice for fMRS research.
Collapse
Affiliation(s)
- Duanghathai Pasanta
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, London SE5 8AB, United Kingdom,Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Jason L. He
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, London SE5 8AB, United Kingdom
| | - Talitha Ford
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Locked Bag 20000, Geelong, Victoria 3220, Australia,Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Georg Oeltzschner
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 700. N. Broadway, 21207, Baltimore, United States,Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Wolfe Street, 21205, Baltimore, United States
| | - David J. Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, London SE5 8AB, United Kingdom
| | - Nicolaas A. Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, London SE5 8AB, United Kingdom,MRC Centre for Neurodevelopmental Disorders, New Hunt's House, Guy's Campus, King's College London, London SE1 1UL, London, United Kingdom
| |
Collapse
|
4
|
Blaney G, Fernandez C, Sassaroli A, Fantini S. Dual-slope imaging of cerebral hemodynamics with frequency-domain near-infrared spectroscopy. NEUROPHOTONICS 2023; 10:013508. [PMID: 36601543 PMCID: PMC9807277 DOI: 10.1117/1.nph.10.1.013508] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Significance This work targets the contamination of optical signals by superficial hemodynamics, which is one of the chief hurdles in non-invasive optical measurements of the human brain. Aim To identify optimal source-detector distances for dual-slope (DS) measurements in frequency-domain (FD) near-infrared spectroscopy (NIRS) and demonstrate preferential sensitivity of DS imaging to deeper tissue (brain) versus superficial tissue (scalp). Approach Theoretical studies (in-silico) based on diffusion theory in two-layered and in homogeneous scattering media. In-vivo demonstrations of DS imaging of the human brain during visual stimulation and during systemic blood pressure oscillations. Results The mean distance (between the two source-detector distances needed for DS) is the key factor for depth sensitivity. In-vivo imaging of the human occipital lobe with FD NIRS and a mean distance of 31 mm indicated: (1) greater hemodynamic response to visual stimulation from FD phase versus intensity, and from DS versus single-distance (SD); (2) hemodynamics from FD phase and DS mainly driven by blood flow, and hemodynamics from SD intensity mainly driven by blood volume. Conclusions DS imaging with FD NIRS may suppress confounding contributions from superficial hemodynamics without relying on data at short source-detector distances. This capability can have significant implications for non-invasive optical measurements of the human brain.
Collapse
Affiliation(s)
- Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Cristianne Fernandez
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| |
Collapse
|
5
|
Zhang Y, Liu D, Zhang P, Li T, Li Z, Gao F. Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks. Front Neurosci 2022; 16:938518. [PMID: 36300170 PMCID: PMC9589108 DOI: 10.3389/fnins.2022.938518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/12/2022] [Indexed: 11/18/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.
Collapse
Affiliation(s)
- Yao Zhang
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Dongyuan Liu
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Pengrui Zhang
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Tieni Li
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Zhiyong Li
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Feng Gao
- College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
- *Correspondence: Feng Gao
| |
Collapse
|
6
|
Wojtkiewicz S, Bejm K, Liebert A. Lock-in functional near-infrared spectroscopy for measurement of the haemodynamic brain response. BIOMEDICAL OPTICS EXPRESS 2022; 13:1869-1887. [PMID: 35519260 PMCID: PMC9045899 DOI: 10.1364/boe.448038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/13/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
Here we show a method of the lock-in amplifying near-infrared signals originating within a human brain. It implies using two 90-degree rotated source-detector pairs fixed on a head surface. Both pairs have a joint sensitivity region located towards the brain. A direct application of the lock-in technique on both signals results in amplifying common frequency components, e.g. related to brain cortex stimulation and attenuating the rest, including all components not related to the stimulation: e.g. pulse, instrumental and biological noise or movement artefacts. This is a self-driven method as no prior assumptions are needed and the noise model is provided by the interfering signals themselves. We show the theory (classical modified Beer-Lambert law and diffuse optical tomography approaches), the algorithm implementation and tests on a finite element mathematical model and in-vivo on healthy volunteers during visual cortex stimulation. The proposed hardware and algorithm complexity suit the entire spectrum of (continuous wave, frequency domain, time-resolved) near-infrared spectroscopy systems featuring real-time, direct, robust and low-noise brain activity registration tool. As such, this can be of special interest in optical brain computer interfaces and high reliability/stability monitors of tissue oxygenation.
Collapse
Affiliation(s)
- Stanislaw Wojtkiewicz
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Trojdena 4, 02-109, Poland
| | - Karolina Bejm
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Trojdena 4, 02-109, Poland
| | - Adam Liebert
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Trojdena 4, 02-109, Poland
| |
Collapse
|
7
|
Re R, Messenio D, Marano G, Spinelli L, Pirovano I, Contini D, Colombo R, Boracchi P, Biganzoli E, Cubeddu R, Torricelli A. Monitoring the haemodynamic response to visual stimulation in glaucoma patients. Sci Rep 2021; 11:13567. [PMID: 34193904 PMCID: PMC8245402 DOI: 10.1038/s41598-021-92857-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 06/16/2021] [Indexed: 11/23/2022] Open
Abstract
In this paper, we used time-domain functional near infrared spectroscopy (TD-fNIRS) to evaluate the haemodynamic response function (HRF) in the occipital cortex following visual stimulation in glaucomatous eyes as compared to healthy eyes. A total of 98 subjects were enrolled in the study and clinically classified as healthy subjects, glaucoma patients (primary open-angle glaucoma) and mixed subjects (i.e. with a different classification for the two eyes). After quality check data were used from HRF of 73 healthy and 62 glaucomatous eyes. The amplitudes of the oxygenated and deoxygenated haemoglobin concentrations, together with their latencies with respect to the stimulus onset, were estimated by fitting their time course with a canonical HRF. Statistical analysis showed that the amplitudes of both haemodynamic parameters show a significant association with the pathology and a significant discriminating ability, while no significant result was found for latencies. Overall, our findings together with the ease of use and noninvasiveness of TD-NIRS, make this technique a promising candidate as a supporting tool for a better evaluation of the glaucoma pathology.
Collapse
Affiliation(s)
- R Re
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy. .,Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - D Messenio
- Department of Clinical Sciences, Eye Clinic, ASST Fatebenefratelli Sacco Hospital, University of Milan, Milan, Italy
| | - G Marano
- Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G.A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Via Vanzetti 5, Milan, Italy
| | - L Spinelli
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - I Pirovano
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.,Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, via Fratelli Cervi 93, 20090, Segrate, MI, Italy
| | - D Contini
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - R Colombo
- Department of Clinical Sciences, Eye Clinic, ASST Fatebenefratelli Sacco Hospital, University of Milan, Milan, Italy
| | - P Boracchi
- Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G.A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Via Vanzetti 5, Milan, Italy
| | - E Biganzoli
- Laboratorio di Statistica Medica, Biometria ed Epidemiologia "G.A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Via Vanzetti 5, Milan, Italy.,Unità di Statistica Medica, Biometria e Bioinformatica, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Vanzetti 5, Milan, Italy
| | - R Cubeddu
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - A Torricelli
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.,Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| |
Collapse
|
8
|
Lyu B, Pham T, Blaney G, Haga Z, Sassaroli A, Fantini S, Aeron S. Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200209SSRR. [PMID: 33415849 PMCID: PMC7790507 DOI: 10.1117/1.jbo.26.2.022908] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/25/2020] [Indexed: 06/12/2023]
Abstract
SIGNIFICANCE We demonstrated the potential of using domain adaptation on functional near-infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory. AIM Domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects. To address this problem, two domain adaptation approaches-Gromov-Wasserstein (G-W) and fused Gromov-Wasserstein (FG-W) were used. APPROACH Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels. We compared these approaches with three supervised methods: multiclass support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN). RESULTS In a sample of six subjects, G-W resulted in an alignment accuracy of 68 % ± 4 % (weighted mean ± standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 % ± 2 % for subject-by-subject alignment. In each of these cases, 25% accuracy represents chance. Alignment accuracy results from both G-W and FG-W are significantly greater than those from SVM, CNN, and RNN. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. CONCLUSIONS Domain adaptation has potential for session-by-session and subject-by-subject alignment of mental workload by using fNIRS data.
Collapse
Affiliation(s)
- Boyang Lyu
- Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts, United States
| | - Thao Pham
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Zachary Haga
- Tufts University, Department of Computer Science, Medford, Massachusetts, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Shuchin Aeron
- Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts, United States
| |
Collapse
|