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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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Xu E, Vanegas M, Mireles M, Dementyev A, Yucel M, Carp S, Fang Q. Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.01.24302838. [PMID: 38496598 PMCID: PMC10942511 DOI: 10.1101/2024.03.01.24302838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
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 condition remains a significant challenge. Aim The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D 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 3-D 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 (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to 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 3-D 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, 360 Huntington Avenue, Boston, USA, 02115
| | - Morris Vanegas
- Northeastern University, Department of Bioengineering, 360 Huntington Avenue, Boston, USA, 02115
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, 360 Huntington Avenue, Boston, USA, 02115
| | - Artem Dementyev
- Massachusetts Institute of Technology, Media Lab, 77 Massachusetts Avenue, Cambridge, USA, 02139
| | - Meryem Yucel
- Boston University, Neurophotonics Center, 233 Bay State Road, Boston, USA, 02215
| | - Stefan Carp
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Boston, USA, 02129
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, 360 Huntington Avenue, Boston, USA, 02115
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Gossé LK, Pinti P, Wiesemann F, Elwell CE, Jones EJH. Developing customized NIRS-EEG for infant sleep research: methodological considerations. NEUROPHOTONICS 2023; 10:035010. [PMID: 37753324 PMCID: PMC10519625 DOI: 10.1117/1.nph.10.3.035010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 09/28/2023]
Abstract
Significance Studies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed. Aim We describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG. Approach N = 49 5- to 9-month-old infants participated. In phase 1, N = 26 (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N = 23 infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage. Results The infant nap rate during phase 1 was ∼ 40 % (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant. Conclusions We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants' comfort, longer sleep duration, and opportunities for caregiver-child interaction).
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Affiliation(s)
- Louisa K. Gossé
- Birkbeck, University of London, Centre for Brain and Cognitive Development, London, United Kingdom
| | - Paola Pinti
- Birkbeck, University of London, Centre for Brain and Cognitive Development, London, United Kingdom
| | - Frank Wiesemann
- Research and Development, Procter & Gamble, Schwalbach am Taunus, Germany
| | - Clare E. Elwell
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Emily J. H. Jones
- Birkbeck, University of London, Centre for Brain and Cognitive Development, London, United Kingdom
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Paulmurugan K, Vijayaragavan V, Ghosh S, Padmanabhan P, Gulyás B. Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS). BIOSENSORS 2021; 11:bios11100389. [PMID: 34677345 PMCID: PMC8534036 DOI: 10.3390/bios11100389] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 11/17/2022]
Abstract
Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.
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Affiliation(s)
- Kogulan Paulmurugan
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (K.P.); (B.G.)
| | - Vimalan Vijayaragavan
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (K.P.); (B.G.)
- Correspondence: (V.V.); (P.P.)
| | - Sayantan Ghosh
- Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, India;
| | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (K.P.); (B.G.)
- Imaging Probe Development Platform, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore
- Correspondence: (V.V.); (P.P.)
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (K.P.); (B.G.)
- Imaging Probe Development Platform, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
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Tsow F, Kumar A, Hosseini SMH, Bowden A. A low-cost, wearable, do-it-yourself functional near-infrared spectroscopy (DIY-fNIRS) headband. HARDWAREX 2021; 10:e00204. [PMID: 34734152 PMCID: PMC8562714 DOI: 10.1016/j.ohx.2021.e00204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 05/27/2023]
Abstract
Neuromonitoring in naturalistic environments is of increasing interest for a variety of research fields including psychology, economics, and productivity. Among functional neuromonitoring modalities, functional near-infrared spectroscopy (fNIRS) is well regarded for its potential for miniaturization, good spatial and temporal resolutions, and resilience to motion artifacts. Historically, the large size and high cost of fNIRS systems have precluded widespread adoption of the technology. In this article, we describe the first open source, fully integrated wireless fNIRS headband system with a single LED-pair source and four detectors. With ease of operation and comfort in mind, the system is encased in a soft, lightweight cloth and silicone enclosure. Accompanying computer and smartphone data collection software have also been provided, and the hardware has been validated using classic fNIRS tasks. This wear-and-go design can easily be scaled to accommodate a larger number of fNIRS channels and opens the door to easily collecting fNIRS data during routine activities in naturalistic conditions.
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Affiliation(s)
- Francis Tsow
- Biophotonics Center, Vanderbilt University, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
| | - Anupam Kumar
- Biophotonics Center, Vanderbilt University, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
| | - SM Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Audrey Bowden
- Biophotonics Center, Vanderbilt University, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN 37232, United States
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Asgher U, Khan MJ, Asif Nizami MH, Khalil K, Ahmad R, Ayaz Y, Naseer N. Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI). Front Neurorobot 2021; 15:605751. [PMID: 33815084 PMCID: PMC8012849 DOI: 10.3389/fnbot.2021.605751] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 02/05/2021] [Indexed: 11/24/2022] Open
Abstract
Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.
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Affiliation(s)
- Umer Asgher
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Hamza Asif Nizami
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Florida State University College of Engineering, Florida A&M University, Tallahassee, FL, United States
| | - Khurram Khalil
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Riaz Ahmad
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Directorate of Quality Assurance and International Collaboration, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Yasar Ayaz
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI), National University of Sciences and Technology, Islamabad, Pakistan
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
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Soekadar SR, Kohl SH, Mihara M, von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin 2021; 30:102577. [PMID: 33545580 PMCID: PMC7868728 DOI: 10.1016/j.nicl.2021.102577] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/30/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
Besides passive recording of brain electric or magnetic activity, also non-ionizing electromagnetic or optical radiation can be used for real-time brain imaging. Here, changes in the radiation's absorption or scattering allow for continuous in vivo assessment of regional neurometabolic and neurovascular activity. Besides magnetic resonance imaging (MRI), over the last years, also functional near-infrared spectroscopy (fNIRS) was successfully established in real-time metabolic brain imaging. In contrast to MRI, fNIRS is portable and can be applied at bedside or in everyday life environments, e.g., to restore communication and movement. Here we provide a comprehensive overview of the history and state-of-the-art of real-time optical brain imaging with a special emphasis on its clinical use towards neurofeedback and brain-computer interface (BCI) applications. Besides pointing to the most critical challenges in clinical use, also novel approaches that combine real-time optical neuroimaging with other recording modalities (e.g. electro- or magnetoencephalography) are described, and their use in the context of neuroergonomics, neuroenhancement or neuroadaptive systems discussed.
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Affiliation(s)
- Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Psychotherapy, Neuroscience Research Center, Campus Charité Mitte (CCM), Charité - University Medicine of Berlin, Berlin, Germany.
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany; Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Germany
| | - Masahito Mihara
- Department of Neurology, Kawasaki Medical School, Kurashiki-City, Okayama, Japan
| | - Alexander von Lühmann
- Machine Learning Department, Computer Science, Technische Universität Berlin, Berlin, Germany; Neurophotonics Center, Biomedical Engineering, Boston University, Boston, USA
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Khan H, Naseer N, Yazidi A, Eide PK, Hassan HW, Mirtaheri P. Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review. Front Hum Neurosci 2021; 14:613254. [PMID: 33568979 PMCID: PMC7868344 DOI: 10.3389/fnhum.2020.613254] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/15/2020] [Indexed: 11/21/2022] Open
Abstract
Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.
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Affiliation(s)
- Haroon Khan
- Department of Mechanical, Electronics and Chemical Engineering, OsloMet—Oslo Metropolitan University, Oslo, Norway
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
| | - Anis Yazidi
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Hafiz Wajahat Hassan
- Department of Mechanical, Electronics and Chemical Engineering, OsloMet—Oslo Metropolitan University, Oslo, Norway
| | - Peyman Mirtaheri
- Department of Mechanical, Electronics and Chemical Engineering, OsloMet—Oslo Metropolitan University, Oslo, Norway
- Department of Biomedical Engineering, Michigan Technological University, Michigan, MI, United States
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Yücel MA, Lühmann AV, Scholkmann F, Gervain J, Dan I, Ayaz H, Boas D, Cooper RJ, Culver J, Elwell CE, Eggebrecht A, Franceschini MA, Grova C, Homae F, Lesage F, Obrig H, Tachtsidis I, Tak S, Tong Y, Torricelli A, Wabnitz H, Wolf M. Best practices for fNIRS publications. NEUROPHOTONICS 2021; 8:012101. [PMID: 33442557 PMCID: PMC7793571 DOI: 10.1117/1.nph.8.1.012101] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 05/09/2023]
Abstract
The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers.
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Affiliation(s)
- Meryem A. Yücel
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Alexander v. Lühmann
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Felix Scholkmann
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
- University of Bern, Institute for Complementary and Integrative Medicine, Bern, Switzerland
| | - Judit Gervain
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
- Università di Padova, Department of Social and Developmental Psychology, Padua, Italy
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Applied Cognitive Neuroscience Laboratory, Tokyo, Japan
| | - 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 Psychology, Philadelphia, Pennsylvania, United States
- Drexel University, Drexel Solutions Institute, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Department of Family and Community Health, Philadelphia, Pennsylvania, United States
- Children’s Hospital of Philadelphia, Center for Injury Research and Prevention, Philadelphia, Pennsylvania, United States
| | - David Boas
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
| | - Robert J. Cooper
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Joseph Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Clare E. Elwell
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Adam Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Maria A. Franceschini
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Christophe Grova
- Concordia University, Department of Physics and PERFORM Centre, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
- McGill University, Biomedical Engineering Department, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
| | - Fumitaka Homae
- Tokyo Metropolitan University, Department of Language Sciences, Tokyo, Japan
| | - Frédéric Lesage
- Polytechnique Montréal, Department Electrical Engineering, Montreal, Canada
| | - Hellmuth Obrig
- University Hospital Leipzig, Max-Planck-Institute for Human Cognitive and Brain Sciences and Clinic for Cognitive Neurology, Leipzig, Germany
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Sungho Tak
- Korea Basic Science Institute, Research Center for Bioconvergence Analysis, Ochang, Cheongju, Republic of Korea
| | - Yunjie Tong
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- Consiglio Nazionale delle Ricerche, Istituto di Fotonica e Nanotecnologie, Milan, Italy
| | | | - Martin Wolf
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
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Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186522] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In the last three decades, the development and steady improvement of various optical technologies at the near-infrared region of the electromagnetic spectrum has inspired a large number of scientists around the world to design and develop functional near-infrared spectroscopy (fNIRS) systems for various medical applications. This has been driven further by the availability of new sources and detectors that support very compact and wearable system designs. In this article, we review fNIRS systems from the instrumentation point of view, discussing the associated challenges and state-of-the-art approaches. In the beginning, the fundamentals of fNIRS systems as well as light-tissue interaction at NIR are briefly introduced. After that, we present the basics of NIR systems instrumentation. Next, the recent development of continuous-wave, frequency-domain, and time-domain fNIRS systems are discussed. Finally, we provide a summary of these three modalities and an outlook into the future of fNIRS technology.
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Kohl SH, Mehler DMA, Lührs M, Thibault RT, Konrad K, Sorger B. The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice. Front Neurosci 2020; 14:594. [PMID: 32848528 PMCID: PMC7396619 DOI: 10.3389/fnins.2020.00594] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/14/2020] [Indexed: 01/04/2023] Open
Abstract
Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts. Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N = 441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1–2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.
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Affiliation(s)
- Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - David M A Mehler
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Michael Lührs
- Brain Innovation B.V., Research Department, Maastricht, Netherlands.,Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Robert T Thibault
- School of Psychological Science, University of Bristol, Bristol, United Kingdom.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Kerstin Konrad
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Bettina Sorger
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
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Arivudaiyanambi J, Mohan S, Cherian SM, Natesan K. Design of a wearable four-channel near-infrared spectroscopy system for the measurement of brain hemodynamic responses. BIOMED ENG-BIOMED TE 2020; 66:/j/bmte.ahead-of-print/bmt-2019-0291/bmt-2019-0291.xml. [PMID: 32649290 DOI: 10.1515/bmt-2019-0291] [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: 02/25/2019] [Accepted: 04/30/2020] [Indexed: 02/28/2024]
Abstract
Objectives This work describes the design and development of a four-channel near-infrared spectroscopy system to detect the oxygenated and deoxygenated hemoglobin concentration changes in the brain during various motor tasks. Methods The system uses light-emitting diodes corresponding to two wavelengths of 760 nm and 850 nm sensitive to deoxygenated and oxygenated hemoglobin concentration changes, respectively. The response is detected using a photodetector with an integrated transimpedance amplifier. The system is designed with four channels for functional near-infrared spectroscopy (fNIRS) signals acquisition. Two experiments were conducted to demonstrate the ability of the system to detect the changes in hemodynamic responses of different tasks. In the first experiment, the hemodynamic changes during motor execution and imagery of right- and left-fist clenching tasks were acquired by the developed system and validated against a standard multichannel NIRS system. In another experiment, the fNIRS signals during rest and motor execution of right-fist clenching task were acquired using the system and classified. Results The results demonstrate the ability of the designed system to detect the brain hemodynamic changes during various tasks. Also, the activation patterns obtained by the developed system with a minimum number of channels are on par with those obtained by the commercial system. Conclusions The developed four-channel NIRS system is user-friendly and has been designed with inexpensive components, unlike the commercially available NIRS instruments that are cumbersome and expensive.
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Affiliation(s)
- Janani Arivudaiyanambi
- Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Sardar Patel Road, Chennai, Tamil Nadu, India
| | - Sasikala Mohan
- Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Sardar Patel Road, Chennai, Tamil Nadu, India
| | - Sunaina Mariam Cherian
- Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Sardar Patel Road, Chennai, Tamil Nadu, India
| | - Kumaravel Natesan
- Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Sardar Patel Road, Chennai, Tamil Nadu, India
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von Lühmann A, Ortega-Martinez A, Boas DA, Yücel MA. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Front Hum Neurosci 2020; 14:30. [PMID: 32132909 PMCID: PMC7040364 DOI: 10.3389/fnhum.2020.00030] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/22/2020] [Indexed: 11/28/2022] Open
Abstract
Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States.,Machine Learning Department, Berlin Institute of Technology, Berlin, Germany
| | | | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Meryem Ayşe Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
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15
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von Lühmann A, Li X, Müller KR, Boas DA, Yücel MA. Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis. Neuroimage 2019; 208:116472. [PMID: 31870944 PMCID: PMC7703677 DOI: 10.1016/j.neuroimage.2019.116472] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/04/2019] [Accepted: 12/17/2019] [Indexed: 01/28/2023] Open
Abstract
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. −55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA; Machine Learning Department, Berlin Institute of Technology, 10587, Berlin, Germany.
| | - Xinge Li
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Klaus-Robert Müller
- Machine Learning Department, Berlin Institute of Technology, 10587, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, South Korea; Max Planck Institute for Informatics, Saarbrücken, 66123, Germany
| | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Meryem A Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
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16
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Lohani M, Payne BR, Strayer DL. A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving. Front Hum Neurosci 2019; 13:57. [PMID: 30941023 PMCID: PMC6434408 DOI: 10.3389/fnhum.2019.00057] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 02/01/2019] [Indexed: 11/13/2022] Open
Abstract
As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.
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Affiliation(s)
- Monika Lohani
- Department of Educational Psychology, University of Utah, Salt Lake City, UT, United States
| | - Brennan R. Payne
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - David L. Strayer
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
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17
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Chiesi M, Guermandi M, Placati S, Scarselli EF, Guerrieri R. Creamino: A Cost-Effective, Open-Source EEG-Based BCI System. IEEE Trans Biomed Eng 2018; 66:900-909. [PMID: 30080140 DOI: 10.1109/tbme.2018.2863198] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the-shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux, and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos, and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications.
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18
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Petrantonakis PC, Kompatsiaris I. Detection of Mental Task Related Activity in NIRS-BCI systems Using Dirichlet Energy over Graphs. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:85-88. [PMID: 30440347 DOI: 10.1109/embc.2018.8512180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Near Infrared Spectroscopy (NIRS)-based Brain Computer Interfaces (NIRS-BCI) rely mainly on the mean concentration changes and slope of the hemodynamic responses in separate recording channels to detect the mental-task related brain activity. Nevertheless, spatial patterns across the measurement channels are also present and should be taken into account for reliable evaluation of the aforementioned detection. In this work the Dirichlet Energy of NIRS signals over a graph is considered for the definition of a measure that would take into account the spatial NIRS features and would integrate the activity of multiple NIRS channels for robust mental task related activity detection. The application of the proposed measure on a real NIRS dataset demonstrates the efficiency of the proposed measure.
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19
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Dadgostar M, Setarehdan SK, Shahzadi S, Akin A. CLASSIFICATION OF SCHIZOPHRENIA USING SVM VIA fNIRS. BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2018. [DOI: 10.4015/s1016237218500084] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In the present study, a classification of functional near-infrared spectroscopy (fNIRS) based on support vector machine (SVM) is presented. It is a non-invasive method monitoring human brain function by evaluating the concentration variation of oxy-hemoglobin and deoxy-hemoglobin. fNIRS is a functional optical imaging technology that measures the neural activities and hemodynamic responses in brain. The data were gathered from 11 healthy volunteers and 16 schizophrenia of the same average age by a 16-channel fNIRS (NIROXCOPE 301 system developed at the Neuro-Optical Imaging Laboratory, continuous-wave dual wavelength). Schizophrenia is a mental disorder that is characterized by mental processing collapse and weak emotional responses. This mental disorder is usually accompanied by a serious disturbance in social and occupational activities. The signals were initially preprocessed by DWT to remove any systemic physiological impediment. A preliminary examination by the genetic algorithm (GA) suggested that some channels of the recreated fNIRS signals required further investigation. The energy of these recreated channel signals was computed and utilized for signal arrangement. We used SVM-based classifier to determine the cases of schizophrenia. The result of six channels is higher than 16 channels. The results demonstrated a classification precision of about 87% in the discovery of schizophrenia in the healthy subjects.
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Affiliation(s)
- Mehrdad Dadgostar
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Sohrab Shahzadi
- Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ata Akin
- Department of Medical Engineering, Acibadem University, Istanbul, Turkey
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20
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Abstract
There are many kinds of neural prostheses available or being researched today. In most cases they are intended to cure or improve the condition of patients affected by some cerebral deficiency. In other cases, their goal is to provide new means to maintain or improve an individual's normal performance. In all these circumstances, one of the possible risks is that of violating the privacy of brain contents (which partly coincide with mental contents) or of depriving individuals of full control over their thoughts (mental states), as the latter are at least partly detectable by new prosthetic technologies. Given the (ethical) premise that the absolute privacy and integrity of the most relevant part of one's brain data is (one of) the most valuable and inviolable human right(s), I argue that a (technical) principle should guide the design and regulation of new neural prostheses. The premise is justified by the fact that whatever the coercion, the threat or the violence undergone, the person can generally preserve a "private repository" of thought in which to defend her convictions and identity, her dignity, and autonomy. Without it, the person may end up in a state of complete subjection to other individuals. The following functional principle is that neural prostheses should be technically designed and built so as to prevent such outcomes. They should: (a) incorporate systems that can find and signal the unauthorized detection, alteration, and diffusion of brain data and brain functioning; (b) be able to stop any unauthorized detection, alteration, and diffusion of brain data. This should not only regard individual devices, but act as a general (technical) operating principle shared by all interconnected systems that deal with decoding brain activity and brain functioning.
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Affiliation(s)
- Andrea Lavazza
- Neuroethics, Centro Universitario Internazionale, Arezzo, Italy
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21
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Zhao H, Cooper RJ. Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system. NEUROPHOTONICS 2018; 5:011012. [PMID: 28983490 PMCID: PMC5613216 DOI: 10.1117/1.nph.5.1.011012] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 08/28/2017] [Indexed: 05/23/2023]
Abstract
The development of a whole-scalp, high sampling-density diffuse optical tomography (DOT) system is a critical next step in the evolution of the field of diffuse optics. To achieve this with optical fiber bundles is extremely challenging, simply because of the sheer number of bundles required, and the associated challenges of weight and ergonomics. Dispensing with optical fiber bundles and moving to head-mounted optoelectronics can potentially facilitate the advent of a new generation of wearable, whole-scalp technologies that will open up a range of new experimental and clinical applications for diffuse optical measurements. Here, we present a concise review of the significant progress that has been made toward achieving a wearable, fiberless, high-density, whole-scalp DOT system. We identify the key limitations of current technologies and discuss the possible opportunities for future development.
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Affiliation(s)
- Hubin Zhao
- University College London, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Robert J. Cooper
- University College London, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
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22
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Funane T, Numata T, Sato H, Hiraizumi S, Hasegawa Y, Kuwabara H, Hasegawa K, Kiguchi M. Rearrangeable and exchangeable optical module with system-on-chip for wearable functional near-infrared spectroscopy system. NEUROPHOTONICS 2018; 5:011007. [PMID: 28924567 PMCID: PMC5591581 DOI: 10.1117/1.nph.5.1.011007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 08/14/2017] [Indexed: 05/04/2023]
Abstract
We developed a system-on-chip (SoC)-incorporated light-emitting diode (LED) and avalanche photodiode (APD) modules to improve the usability and flexibility of a fiberless wearable functional near-infrared spectroscopy (fNIRS) system. The SoC has a microprocessing unit and programmable circuits. The time division method and the lock-in method were used for separately detecting signals from different positions and signals of different wavelengths, respectively. Each module autonomously works for this time-divided-lock-in measurement with a high sensitivity for haired regions. By supplying [Formula: see text] of power and base and data clocks, the LED module emits both 730- and 855-nm wavelengths of light, amplitudes of which are modulated in each lock-in frequency generated from the base clock, and the APD module provides the lock-in detected signals synchronizing with the data clock. The SoC provided many functions, including automatic-power-control of the LED, automatic judgment of detected power level, and automatic-gain-control of the programmable gain amplifier. The number and the arrangement of modules can be adaptively changed by connecting this exchangeable modules in a daisy chain and setting the parameters dependent on the probing position. Therefore, users can configure a variety of arrangements (single- or multidistance combinations) of them with this module-based system.
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Affiliation(s)
- Tsukasa Funane
- Hitachi, Ltd., Research & Development Group, Center for Exploratory Research, Hatoyama, Saitama, Japan
- Address all correspondence to: Tsukasa Funane, E-mail:
| | - Takashi Numata
- Hitachi, Ltd., Research & Development Group, Center for Exploratory Research, Hatoyama, Saitama, Japan
| | - Hiroki Sato
- Hitachi, Ltd., Research & Development Group, Center for Exploratory Research, Hatoyama, Saitama, Japan
| | | | | | | | | | - Masashi Kiguchi
- Hitachi, Ltd., Research & Development Group, Center for Exploratory Research, Hatoyama, Saitama, Japan
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Kassab A, Le Lan J, Tremblay J, Vannasing P, Dehbozorgi M, Pouliot P, Gallagher A, Lesage F, Sawan M, Nguyen DK. Multichannel wearable fNIRS-EEG system for long-term clinical monitoring. Hum Brain Mapp 2017; 39:7-23. [PMID: 29058341 DOI: 10.1002/hbm.23849] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 10/02/2017] [Accepted: 10/08/2017] [Indexed: 01/27/2023] Open
Abstract
Continuous brain imaging techniques can be beneficial for the monitoring of neurological pathologies (such as epilepsy or stroke) and neuroimaging protocols involving movement. Among existing ones, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have the advantage of being noninvasive, nonobstructive, inexpensive, yield portable solutions, and offer complementary monitoring of electrical and local hemodynamic activities. This article presents a novel system with 128 fNIRS channels and 32 EEG channels with the potential to cover a larger fraction of the adult superficial cortex than earlier works, is integrated with 32 EEG channels, is light and battery-powered to improve portability, and can transmit data wirelessly to an interface for real-time display of electrical and hemodynamic activities. A novel fNIRS-EEG stretchable cap, two analog channels for auxiliary data (e.g., electrocardiogram), eight digital triggers for event-related protocols and an internal accelerometer for movement artifacts removal contribute to improve data acquisition quality. The system can run continuously for 24 h. Following instrumentation validation and reliability on a solid phantom, performance was evaluated on (1) 12 healthy participants during either a visual (checkerboard) task at rest or while pedalling on a stationary bicycle or a cognitive (language) task and (2) 4 patients admitted either to the epilepsy (n = 3) or stroke (n = 1) units. Data analysis confirmed expected hemodynamic variations during validation recordings and useful clinical information during in-hospital testing. To the best of our knowledge, this is the first demonstration of a wearable wireless multichannel fNIRS-EEG monitoring system in patients with neurological conditions. Hum Brain Mapp 39:7-23, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Ali Kassab
- Research Center, Centre Hospitalier Universitaire de Montréal, Université de Montréal, Montréal, Québec, H2X 0A9, Canada
| | - Jérôme Le Lan
- Department of Electrical Engineering, École Polytechnique de Montréal, Montréal, Québec, H3T 1J4, Canada
| | - Julie Tremblay
- Research Center, Hôpital Sainte-Justine, Université de Montréal, Montréal, Québec, H3T 1C4, Canada
| | - Phetsamone Vannasing
- Research Center, Hôpital Sainte-Justine, Université de Montréal, Montréal, Québec, H3T 1C4, Canada
| | - Mahya Dehbozorgi
- Department of Electrical Engineering, École Polytechnique de Montréal, Montréal, Québec, H3T 1J4, Canada
| | - Philippe Pouliot
- Department of Electrical Engineering, École Polytechnique de Montréal, Montréal, Québec, H3T 1J4, Canada.,Research Center, Montreal Heart Institute, Montréal, Québec, H1T 1C8, Canada
| | - Anne Gallagher
- Research Center, Hôpital Sainte-Justine, Université de Montréal, Montréal, Québec, H3T 1C4, Canada
| | - Frédéric Lesage
- Department of Electrical Engineering, École Polytechnique de Montréal, Montréal, Québec, H3T 1J4, Canada
| | - Mohamad Sawan
- Department of Electrical Engineering, École Polytechnique de Montréal, Montréal, Québec, H3T 1J4, Canada
| | - Dang Khoa Nguyen
- Research Center, Centre Hospitalier Universitaire de Montréal, Université de Montréal, Montréal, Québec, H2X 0A9, Canada.,Department of Neurology, Hôpital Notre-Dame (Centre Hospitalier de l'Université de Montréal), Montréal, Québec, H2L 4M1, Canada
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Ahn S, Jun SC. Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces - Current Limitations and Future Directions. Front Hum Neurosci 2017; 11:503. [PMID: 29093673 PMCID: PMC5651279 DOI: 10.3389/fnhum.2017.00503] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Accepted: 10/05/2017] [Indexed: 11/13/2022] Open
Abstract
Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality's drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features. In addition, mismatch of recording locations may hinder further improvement. In this literature review, we surveyed studies of the integration of EEG/fNIRS in BCI thoroughly and discussed its current limitations. We also suggested future directions for efficient and successful multi-modal integration of EEG/fNIRS in BCI systems.
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Affiliation(s)
- Sangtae Ahn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung C Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
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25
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Wyser D, Lambercy O, Scholkmann F, Wolf M, Gassert R. Wearable and modular functional near-infrared spectroscopy instrument with multidistance measurements at four wavelengths. NEUROPHOTONICS 2017; 4:041413. [PMID: 28840164 PMCID: PMC5562388 DOI: 10.1117/1.nph.4.4.041413] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/12/2017] [Indexed: 05/22/2023]
Abstract
With the aim of transitioning functional near-infrared spectroscopy (fNIRS) technology from the laboratory environment to everyday applications, the field has seen a recent push toward the development of wearable/miniaturized, multiwavelength, multidistance, and modular instruments. However, it is challenging to unite all these requirements in a precision instrument with low noise, low drift, and fast sampling characteristics. We present the concept and development of a wearable fNIRS instrument that combines all these key features with the goal of reliably and accurately capturing brain hemodynamics. The proposed instrument consists of a modular network of miniaturized optode modules that include a four-wavelength light source and a highly sensitive silicon photomultiplier detector. Simultaneous measurements with short-separation (7.5 mm; containing predominantly extracerebral signals) and long-separation (20 mm or more; containing both extracerebral and cerebral information) channels are used with short-channel regression filtering methods to increase robustness of fNIRS measurements. Performance of the instrument was characterized with phantom measurements and further validated in human in vivo measurements, demonstrating the good raw signal quality (signal-to-noise ratio of 64 dB for short channels; robust measurements up to 50 mm; dynamic optical range larger than 160 dB), the valid estimation of concentration changes (oxy- and deoxyhemoglobin, and cytochrome-c-oxidase) in muscle and brain, and the detection of task-evoked brain activity. The results of our preliminary tests suggest that the presented fNIRS instrument outperforms existing instruments in many aspects and bears high potential for real-time single-trial fNIRS applications as required for wearable brain-computer interfaces.
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Affiliation(s)
- Dominik Wyser
- ETH Zurich, Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Zurich, Switzerland
- University Hospital of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland
- Address all correspondence to: Dominik Wyser, E-mail:
| | - Olivier Lambercy
- ETH Zurich, Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Zurich, Switzerland
| | - Felix Scholkmann
- University Hospital of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland
| | - Martin Wolf
- University Hospital of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland
| | - Roger Gassert
- ETH Zurich, Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Zurich, Switzerland
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Li R, Potter T, Huang W, Zhang Y. Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features. Front Hum Neurosci 2017; 11:462. [PMID: 28966581 PMCID: PMC5605645 DOI: 10.3389/fnhum.2017.00462] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 09/04/2017] [Indexed: 11/29/2022] Open
Abstract
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0–1 s) along with initial hemodynamic dip information from fNIRS (0–2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.
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Affiliation(s)
- Rihui Li
- Department of Biomedical Engineering, University of HoustonHouston, TX, United States
| | - Thomas Potter
- Department of Biomedical Engineering, University of HoustonHouston, TX, United States
| | - Weitian Huang
- Guangdong Provincial Work-Injury Rehabilitation HospitalGuangzhou, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of HoustonHouston, TX, United States.,Guangdong Provincial Work-Injury Rehabilitation HospitalGuangzhou, China
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27
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Wijayasiri P, Hartley DE, Wiggins IM. Brain activity underlying the recovery of meaning from degraded speech: A functional near-infrared spectroscopy (fNIRS) study. Hear Res 2017; 351:55-67. [DOI: 10.1016/j.heares.2017.05.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 05/11/2017] [Accepted: 05/23/2017] [Indexed: 11/30/2022]
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28
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Kim HY, Seo K, Jeon HJ, Lee U, Lee H. Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models. Mol Cells 2017; 40:523-532. [PMID: 28835022 PMCID: PMC5582298 DOI: 10.14348/molcells.2017.0153] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 08/03/2017] [Indexed: 01/26/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.
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Affiliation(s)
- Hak Yeong Kim
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988,
Korea
| | - Kain Seo
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988,
Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul 06351,
Korea
| | - Unjoo Lee
- Department of Electronic Engineering, Hallym University, Kangwon 24252,
Korea
| | - Hyosang Lee
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988,
Korea
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29
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Hori S, Mori K, Mashimo T, Seiyama A. Effects of Light and Sound on the Prefrontal Cortex Activation and Emotional Function: A Functional Near-Infrared Spectroscopy Study. Front Neurosci 2017. [PMID: 28649190 PMCID: PMC5465298 DOI: 10.3389/fnins.2017.00321] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
We constructed a near infrared spectroscopy-based real-time feedback system to estimate the subjects' emotional states using the changes in oxygenated hemoglobin concentration [Δ(oxy-Hb)] in the prefrontal cortex (PFC). Using this system, we investigated the influences of continual mild and equivocal stimuli consisting of lights and a reconstructed waterfall sound on Δ[oxy-Hb] in the PFC. The visual (light) and auditory (sound) stimuli changed randomly and independently, depending on the emotional states of the individual subjects. The emotional states induced by the stimuli were examined via a questionnaire rated on an 11-point scale, from +5 (pleasant) to −5 (unpleasant), through 0 (neutral), after the 5-min experiments. Results from 757 subjects revealed that Δ[oxy-Hb] in the PFC exhibited a weak, but significant, correlation with emotional change, with the given continual and mild stimuli similar to that experienced in response to the intense pleasant/unpleasant stimuli. Based on the results we discuss the generation of pleasant/unpleasant weak emotional change induced by mild and weak stimuli such as light and sound.
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Affiliation(s)
- Shota Hori
- Human Health Sciences, Graduate School of Medicine, Kyoto UniversityKyoto, Japan.,Japan Society for the Promotion of ScienceTokyo, Japan
| | - Koichi Mori
- Department of Information and Media, Doshisha Women's College of Liberal ArtsKyoto, Japan
| | - Takehisa Mashimo
- Media Design Department, Seian University of Art and DesignOtsu, Japan
| | - Akitoshi Seiyama
- Human Health Sciences, Graduate School of Medicine, Kyoto UniversityKyoto, Japan
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30
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von Luhmann A, Wabnitz H, Sander T, Muller KR. M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring. IEEE Trans Biomed Eng 2017; 64:1199-1210. [DOI: 10.1109/tbme.2016.2594127] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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31
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Khan MJ, Hong KS. Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control. Front Neurorobot 2017; 11:6. [PMID: 28261084 PMCID: PMC5314821 DOI: 10.3389/fnbot.2017.00006] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 01/24/2017] [Indexed: 01/27/2023] Open
Abstract
In this paper, a hybrid electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain–computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG–fNIRS interface.
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Affiliation(s)
- Muhammad Jawad Khan
- 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
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32
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Gramigna V, Pellegrino G, Cerasa A, Cutini S, Vasta R, Olivadese G, Martino I, Quattrone A. Near-Infrared Spectroscopy in Gait Disorders: Is It Time to Begin? Neurorehabil Neural Repair 2017; 31:402-412. [PMID: 28196453 DOI: 10.1177/1545968317693304] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Walking is a complex motor behavior with a special relevance in clinical neurology. Many neurological diseases, such as Parkinson's disease and stroke, are characterized by gait disorders whose neurofunctional correlates are poorly investigated. Indeed, the analysis of real walking with the standard neuroimaging techniques poses strong challenges, and only a few studies on motor imagery or walking observation have been performed so far. Functional near-infrared spectroscopy (fNIRS) is becoming an important research tool to assess functional activity in neurological populations or for special tasks, such as walking, because it allows investigating brain hemodynamic activity in an ecological setting, without strong immobility constraints. A systematic review following PRISMA guidelines was conducted on the fNIRS-based examination of gait disorders. Twelve of the initial yield of 489 articles have been included in this review. The lesson learnt from these studies suggest that oxy-hemoglobin levels within the prefrontal and premotor cortices are more sensitive to compensation strategies reflecting postural control and restoration of gait disorders. Although this field of study is in its relative infancy, the evidence provided encourages the translation of fNIRS in clinical practice, as it offers a unique opportunity to explore in depth the activity of the cortical motor system during real walking in neurological patients. We also discuss to what extent fNIRS may be applied for assessing the effectiveness of rehabilitation programs.
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Affiliation(s)
| | | | - Antonio Cerasa
- 1 University Magna Graecia, Catanzaro, Italy.,3 Istituto di Bioimmagini e Fisiologia Molecolare, National Research Council, Catanzaro, Italy
| | - Simone Cutini
- 4 Department of Developmental Psychology, University of Padova, Padova, Italy
| | | | - Giuseppe Olivadese
- 3 Istituto di Bioimmagini e Fisiologia Molecolare, National Research Council, Catanzaro, Italy
| | | | - Aldo Quattrone
- 1 University Magna Graecia, Catanzaro, Italy.,3 Istituto di Bioimmagini e Fisiologia Molecolare, National Research Council, Catanzaro, Italy
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33
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Blankertz B, Acqualagna L, Dähne S, Haufe S, Schultze-Kraft M, Sturm I, Ušćumlic M, Wenzel MA, Curio G, Müller KR. The Berlin Brain-Computer Interface: Progress Beyond Communication and Control. Front Neurosci 2016; 10:530. [PMID: 27917107 PMCID: PMC5116473 DOI: 10.3389/fnins.2016.00530] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 10/31/2016] [Indexed: 12/11/2022] Open
Abstract
The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.
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Affiliation(s)
- Benjamin Blankertz
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
- Bernstein Focus: NeurotechnologyBerlin, Germany
| | - Laura Acqualagna
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
| | - Sven Dähne
- Machine Learning Group, Technische Universität BerlinBerlin, Germany
| | - Stefan Haufe
- Bernstein Focus: NeurotechnologyBerlin, Germany
- Machine Learning Group, Technische Universität BerlinBerlin, Germany
| | - Matthias Schultze-Kraft
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
- Bernstein Focus: NeurotechnologyBerlin, Germany
| | - Irene Sturm
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
| | - Marija Ušćumlic
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
| | - Markus A. Wenzel
- Neurotechnology Group, Technische Universität BerlinBerlin, Germany
| | - Gabriel Curio
- Bernstein Focus: NeurotechnologyBerlin, Germany
- Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité - University Medicine BerlinBerlin, Germany
| | - Klaus-Robert Müller
- Bernstein Focus: NeurotechnologyBerlin, Germany
- Machine Learning Group, Technische Universität BerlinBerlin, Germany
- Department of Brain and Cognitive Engineering, Korea UniversitySeoul, South Korea
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34
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Chitnis D, Airantzis D, Highton D, Williams R, Phan P, Giagka V, Powell S, Cooper RJ, Tachtsidis I, Smith M, Elwell CE, Hebden JC, Everdell N. Towards a wearable near infrared spectroscopic probe for monitoring concentrations of multiple chromophores in biological tissue in vivo. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2016; 87:065112. [PMID: 27370501 PMCID: PMC4957669 DOI: 10.1063/1.4954722] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The first wearable multi-wavelength technology for functional near-infrared spectroscopy has been developed, based on a custom-built 8-wavelength light emitting diode (LED) source. A lightweight fibreless probe is designed to monitor changes in the concentrations of multiple absorbers (chromophores) in biological tissue, the most dominant of which at near-infrared wavelengths are oxyhemoglobin and deoxyhemoglobin. The use of multiple wavelengths enables signals due to the less dominant chromophores to be more easily distinguished from those due to hemoglobin and thus provides more complete and accurate information about tissue oxygenation, hemodynamics, and metabolism. The spectroscopic probe employs four photodiode detectors coupled to a four-channel charge-to-digital converter which includes a charge integration amplifier and an analogue-to-digital converter (ADC). Use of two parallel charge integrators per detector enables one to accumulate charge while the other is being read out by the ADC, thus facilitating continuous operation without dead time. The detector system has a dynamic range of about 80 dB. The customized source consists of eight LED dies attached to a 2 mm × 2 mm substrate and encapsulated in UV-cured epoxy resin. Switching between dies is performed every 20 ms, synchronized to the detector integration period to within 100 ns. The spectroscopic probe has been designed to be fully compatible with simultaneous electroencephalography measurements. Results are presented from measurements on a phantom and a functional brain activation study on an adult volunteer, and the performance of the spectroscopic probe is shown to be very similar to that of a benchtop broadband spectroscopy system. The multi-wavelength capabilities and portability of this spectroscopic probe will create significant opportunities for in vivo studies in a range of clinical and life science applications.
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Affiliation(s)
- Danial Chitnis
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Dimitrios Airantzis
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - David Highton
- Neurocritical Care Unit, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, United Kingdom
| | - Rhys Williams
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Phong Phan
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Vasiliki Giagka
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Samuel Powell
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Robert J Cooper
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Ilias Tachtsidis
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Martin Smith
- Neurocritical Care Unit, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, United Kingdom
| | - Clare E Elwell
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Jeremy C Hebden
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
| | - Nicholas Everdell
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom
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35
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Carrieri M, Petracca A, Lancia S, Basso Moro S, Brigadoi S, Spezialetti M, Ferrari M, Placidi G, Quaresima V. Prefrontal Cortex Activation Upon a Demanding Virtual Hand-Controlled Task: A New Frontier for Neuroergonomics. Front Hum Neurosci 2016; 10:53. [PMID: 26909033 PMCID: PMC4754420 DOI: 10.3389/fnhum.2016.00053] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 02/01/2016] [Indexed: 11/15/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.
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Affiliation(s)
- Marika Carrieri
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Andrea Petracca
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Stefania Lancia
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Sara Basso Moro
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Sabrina Brigadoi
- Department of Developmental Psychology, University of Padova Padova, Italy
| | - Matteo Spezialetti
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Marco Ferrari
- Department of Physical and Chemical Sciences, University of L'Aquila L'Aquila, Italy
| | - Giuseppe Placidi
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
| | - Valentina Quaresima
- Department of Life, Health and Environmental Sciences, University of L'Aquila L'Aquila, Italy
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