1
|
Acuña K, Sapahia R, Jiménez IN, Antonietti M, Anzola I, Cruz M, García MT, Krishnan V, Leveille LA, Resch MD, Galor A, Habash R, DeBuc DC. Functional Near-Infrared Spectrometry as a Useful Diagnostic Tool for Understanding the Visual System: A Review. J Clin Med 2024; 13:282. [PMID: 38202288 PMCID: PMC10779649 DOI: 10.3390/jcm13010282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
This comprehensive review explores the role of Functional Near-Infrared Spectroscopy (fNIRS) in advancing our understanding of the visual system. Beginning with an introduction to fNIRS, we delve into its historical development, highlighting how this technology has evolved over time. The core of the review critically examines the advantages and disadvantages of fNIRS, offering a balanced view of its capabilities and limitations in research and clinical settings. We extend our discussion to the diverse applications of fNIRS beyond its traditional use, emphasizing its versatility across various fields. In the context of the visual system, this review provides an in-depth analysis of how fNIRS contributes to our understanding of eye function, including eye diseases. We discuss the intricacies of the visual cortex, how it responds to visual stimuli and the implications of these findings in both health and disease. A unique aspect of this review is the exploration of the intersection between fNIRS, virtual reality (VR), augmented reality (AR) and artificial intelligence (AI). We discuss how these cutting-edge technologies are synergizing with fNIRS to open new frontiers in visual system research. The review concludes with a forward-looking perspective, envisioning the future of fNIRS in a rapidly evolving technological landscape and its potential to revolutionize our approach to studying and understanding the visual system.
Collapse
Affiliation(s)
- Kelly Acuña
- School of Medicine, Georgetown University, Washington, DC 20007, USA;
| | - Rishav Sapahia
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Irene Newman Jiménez
- Department of Cognitive Science, Faculty of Arts & Science, McGill University, Montreal, QC H4A 3J1, Canada;
| | - Michael Antonietti
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ignacio Anzola
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Marvin Cruz
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Michael T. García
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Varun Krishnan
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Lynn A. Leveille
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Miklós D. Resch
- Department of Ophthalmology, Semmelweis University, 1085 Budapest, Hungary;
| | - Anat Galor
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ranya Habash
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Delia Cabrera DeBuc
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| |
Collapse
|
2
|
Perpetuini D, Russo EF, Cardone D, Palmieri R, De Giacomo A, Intiso D, Pellicano F, Pellegrino R, Merla A, Calabrò RS, Filoni S. Assessing the Impact of Electrosuit Therapy on Cerebral Palsy: A Study on the Users' Satisfaction and Potential Efficacy. Brain Sci 2023; 13:1491. [PMID: 37891858 PMCID: PMC10605024 DOI: 10.3390/brainsci13101491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/13/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
The aim of this study is to evaluate the effectiveness of electrosuit therapy in the clinical treatment of children with Cerebral Palsy, focusing on the effect of the therapy on spasticity and trunk control. Moreover, the compliance of caregivers with respect to the use of the tool was investigated. During the period ranging from 2019 to 2022, a total of 26 children (18 M and 8 F), clinically stable and affected by CP and attending the Neurorehabilitation Unit of the "Padre Pio Foundation and Rehabilitation Centers", were enrolled in this study. A subset of 12 patients bought or rented the device; thus, they received the administration of the EMS-based therapy for one month, whereas the others received only one-hour training to evaluate the feasibility (by the caregivers) and short-term effects. The Gross Motor Function Classification System was utilized to evaluate gross motor functions and to classify the study sample, while the MAS and the LSS were employed to assess the outcomes of the EMS-based therapy. Moreover, between 80% and 90% of the study sample were satisfied with the safety, ease of use, comfort, adjustment, and after-sales service. Following a single session of electrical stimulation with EMS, patients exhibited a statistically significant enhancement in trunk control. For those who continued this study, the subscale of the QUEST with the best score was adaptability (0.74 ± 0.85), followed by competence (0.67 ± 0.70) and self-esteem (0.59 ± 0.60). This study investigates the impact of the employment of the EMS on CP children's ability to maintain trunk control. Specifically, after undergoing a single EMS session, LSS showed a discernible improvement in children's trunk control. In addition, the QUEST and the PIADS questionnaires demonstrated a good acceptability and satisfaction of the garment by the patients and the caregivers.
Collapse
Affiliation(s)
- David Perpetuini
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.); (A.M.)
| | - Emanuele Francesco Russo
- Padre Pio Foundation and Rehabilitation Centers, 71013 San Giovanni Rotondo, Italy; (E.F.R.); (F.P.)
| | - Daniela Cardone
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.); (A.M.)
| | - Roberta Palmieri
- Translational Biomedicine and Neuroscience Department (DiBraiN), University of Bari “Aldo Moro”, 70124 Bari, Italy; (R.P.); (A.D.G.)
| | - Andrea De Giacomo
- Translational Biomedicine and Neuroscience Department (DiBraiN), University of Bari “Aldo Moro”, 70124 Bari, Italy; (R.P.); (A.D.G.)
| | - Domenico Intiso
- Unit of Neuro-Rehabilitation, IRCCS “Casa Sollievo della Sofferenza”, 71013 San Giovanni Rotondo, Italy; (D.I.); (S.F.)
| | - Federica Pellicano
- Padre Pio Foundation and Rehabilitation Centers, 71013 San Giovanni Rotondo, Italy; (E.F.R.); (F.P.)
| | - Raffaello Pellegrino
- Department of Scientific Research, Campus Ludes, Off-Campus Semmelweis University, 6912 Lugano, Switzerland;
| | - Arcangelo Merla
- Department of Engineering and Geology, University G. D’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; (D.P.); (D.C.); (A.M.)
| | | | - Serena Filoni
- Unit of Neuro-Rehabilitation, IRCCS “Casa Sollievo della Sofferenza”, 71013 San Giovanni Rotondo, Italy; (D.I.); (S.F.)
| |
Collapse
|
3
|
Anaya D, Batra G, Bracewell P, Catoen R, Chakraborty D, Chevillet M, Damodara P, Dominguez A, Emms L, Jiang Z, Kim E, Klumb K, Lau F, Le R, Li J, Mateo B, Matloff L, Mehta A, Mugler EM, Murthy A, Nakagome S, Orendorff R, Saung EF, Schwarz R, Sethi R, Sevile R, Srivastava A, Sundberg J, Yang Y, Yin A. Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight). JOURNAL OF BIOMEDICAL OPTICS 2023; 28:065003. [PMID: 37325190 PMCID: PMC10261976 DOI: 10.1117/1.jbo.28.6.065003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/26/2023] [Accepted: 05/01/2023] [Indexed: 06/17/2023]
Abstract
Significance We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions The advances presented here should serve to make fNIRS more accessible for BCI applications.
Collapse
Affiliation(s)
- Daniel Anaya
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Gautam Batra
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Ryan Catoen
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Mark Chevillet
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | | | - Laurence Emms
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Zifan Jiang
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ealgoo Kim
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Keith Klumb
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Frances Lau
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Rosemary Le
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Jamie Li
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Brett Mateo
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Laura Matloff
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Asha Mehta
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - Akansh Murthy
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Sho Nakagome
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ryan Orendorff
- Meta Platforms, Inc., Menlo Park, California, United States
| | - E-Fann Saung
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Roland Schwarz
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ruben Sethi
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Rudy Sevile
- Meta Platforms, Inc., Menlo Park, California, United States
| | | | - John Sundberg
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Ying Yang
- Meta Platforms, Inc., Menlo Park, California, United States
| | - Allen Yin
- Meta Platforms, Inc., Menlo Park, California, United States
| |
Collapse
|
4
|
Perpetuini D, Günal M, Chiou N, Koyejo S, Mathewson K, Low KA, Fabiani M, Gratton G, Chiarelli AM. Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface. Bioengineering (Basel) 2023; 10:553. [PMID: 37237623 PMCID: PMC10215195 DOI: 10.3390/bioengineering10050553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
A brain-computer interface (BCI) allows users to control external devices through brain activity. Portable neuroimaging techniques, such as near-infrared (NIR) imaging, are suitable for this goal. NIR imaging has been used to measure rapid changes in brain optical properties associated with neuronal activation, namely fast optical signals (FOS) with good spatiotemporal resolution. However, FOS have a low signal-to-noise ratio, limiting their BCI application. Here FOS were acquired with a frequency-domain optical system from the visual cortex during visual stimulation consisting of a rotating checkerboard wedge, flickering at 5 Hz. We used measures of photon count (Direct Current, DC light intensity) and time of flight (phase) at two NIR wavelengths (690 nm and 830 nm) combined with a machine learning approach for fast estimation of visual-field quadrant stimulation. The input features of a cross-validated support vector machine classifier were computed as the average modulus of the wavelet coherence between each channel and the average response among all channels in 512 ms time windows. An above chance performance was obtained when differentiating visual stimulation quadrants (left vs. right or top vs. bottom) with the best classification accuracy of ~63% (information transfer rate of ~6 bits/min) when classifying the superior and inferior stimulation quadrants using DC at 830 nm. The method is the first attempt to provide generalizable retinotopy classification relying on FOS, paving the way for the use of FOS in real-time BCI.
Collapse
Affiliation(s)
- David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, G. D’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. D’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
| | - Mehmet Günal
- Beckman Institute, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
| | - Nicole Chiou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Sanmi Koyejo
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Kyle Mathewson
- Department of Psychology, Faculty of Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Kathy A. Low
- Beckman Institute, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
| | - Monica Fabiani
- Beckman Institute, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
- Psychology Department, University of Illinois at Urbana Champaign, Champaign, IL 61820, USA
| | - Gabriele Gratton
- Beckman Institute, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
- Psychology Department, University of Illinois at Urbana Champaign, Champaign, IL 61820, USA
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, G. D’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. D’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
| |
Collapse
|
5
|
Perpetuini D, Formenti D, Cardone D, Trecroci A, Rossi A, Di Credico A, Merati G, Alberti G, Di Baldassarre A, Merla A. Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue? SENSORS (BASEL, SWITZERLAND) 2023; 23:832. [PMID: 36679631 PMCID: PMC9863897 DOI: 10.3390/s23020832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
Surface electromyography (sEMG) is the acquisition, from the skin, of the electrical signal produced by muscle activation. Usually, sEMG is measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless electrodes have been developed to overcome this limitation. However, contactless EMG devices are still sensitive to motion artifacts and often not comfortable for long monitoring. In this study, a non-invasive contactless method to estimate parameters indicative of muscular activity and fatigue, as they are assessed by EMG, through infrared thermal imaging (IRI) and cross-validated machine learning (ML) approaches is described. Particularly, 10 healthy participants underwent five series of bodyweight squats until exhaustion interspersed by 1 min of rest. During exercising, the vastus medialis activity and its temperature were measured through sEMG and IRI, respectively. The EMG average rectified value (ARV) and the median frequency of the power spectral density (MDF) of each series were estimated through several ML approaches applied to IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 for ARV, and r = 0.661, p < 0.001 for MDF). Although EMG and IRI measure physiological processes of a different nature and are not interchangeable, these results suggest a potential link between skin temperature and muscle activity and fatigue, fostering the employment of contactless methods to deliver metrics of muscular activity in a non-invasive and comfortable manner in sports and clinical applications.
Collapse
Affiliation(s)
- David Perpetuini
- Department of Neurosciences, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Damiano Formenti
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy
| | - Daniela Cardone
- Department of Engineering and Geology, University “G. d’Annunzio” of Chieti-Pescara, 65127 Pescara, Italy
| | - Athos Trecroci
- Department of Biomedical Sciences for Health, University of Milan, 20129 Milan, Italy
| | - Alessio Rossi
- Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Andrea Di Credico
- Department of Medicine and Aging Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Giampiero Merati
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milano, Italy
| | | | - Angela Di Baldassarre
- Department of Medicine and Aging Sciences, University “G. d’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University “G. d’Annunzio” of Chieti-Pescara, 65127 Pescara, Italy
| |
Collapse
|
6
|
Liu R, Qi S, Hao S, Lian G, Luo Y. Using electroencephalography to analyse drivers' different cognitive workload characteristics based on on-road experiment. Front Psychol 2023; 14:1107176. [PMID: 37168425 PMCID: PMC10164949 DOI: 10.3389/fpsyg.2023.1107176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 03/31/2023] [Indexed: 05/13/2023] Open
Abstract
Driver's cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time-frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver.
Collapse
Affiliation(s)
- Ruiwei Liu
- Department of Naval Architecture and Marine Engineering, Guangzhou Maritime University, Guangzhou, China
| | - Shouming Qi
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, China
- Shenzhen Urban Public Safety and Technology Institute, Shenzhen, Guangdong, China
- *Correspondence: Shouming Qi,
| | - Siqi Hao
- Department of Ports and Shipping Management, Guangzhou Maritime University, Guangzhou, China
| | - Guan Lian
- School of Transportation and Architecture Engineering, Guilin University of Electronic Technology, Guilin, China
| | - Yeying Luo
- Department of Ports and Shipping Management, Guangzhou Maritime University, Guangzhou, China
| |
Collapse
|
7
|
Khalil K, Asgher U, Ayaz Y. Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface. Sci Rep 2022; 12:3198. [PMID: 35210460 PMCID: PMC8873341 DOI: 10.1038/s41598-022-06805-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 01/04/2022] [Indexed: 01/23/2023] Open
Abstract
The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.
Collapse
Affiliation(s)
- Khurram Khalil
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
| | - Umer Asgher
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.,Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
| | - Yasar Ayaz
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
| |
Collapse
|
8
|
Mohamed M, Jo E, Mohamed N, Kim M, Yun JD, Kim JG. Development of an Integrated EEG/fNIRS Brain Function Monitoring System. SENSORS 2021; 21:s21227703. [PMID: 34833775 PMCID: PMC8625300 DOI: 10.3390/s21227703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 11/16/2022]
Abstract
In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of -115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.
Collapse
Affiliation(s)
- Manal Mohamed
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (M.M.); (E.J.); (N.M.); (M.K.)
| | - Eunjung Jo
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (M.M.); (E.J.); (N.M.); (M.K.)
| | - Nourelhuda Mohamed
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (M.M.); (E.J.); (N.M.); (M.K.)
| | - Minhee Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (M.M.); (E.J.); (N.M.); (M.K.)
| | | | - Jae Gwan Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (M.M.); (E.J.); (N.M.); (M.K.)
- Correspondence: ; Tel.: +82-62-715-2220
| |
Collapse
|
9
|
Wearable, Integrated EEG-fNIRS Technologies: A Review. SENSORS 2021; 21:s21186106. [PMID: 34577313 PMCID: PMC8469799 DOI: 10.3390/s21186106] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 02/04/2023]
Abstract
There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.
Collapse
|
10
|
A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking. SENSORS 2021; 21:s21155117. [PMID: 34372353 PMCID: PMC8346954 DOI: 10.3390/s21155117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/13/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022]
Abstract
Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes' movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes' movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes' movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.
Collapse
|
11
|
Kim B, Kim H, Kim S, Hwang YR. A brief review of non-invasive brain imaging technologies and the near-infrared optical bioimaging. Appl Microsc 2021; 51:9. [PMID: 34170436 PMCID: PMC8227874 DOI: 10.1186/s42649-021-00058-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/07/2021] [Indexed: 12/12/2022] Open
Abstract
Brain disorders seriously affect life quality. Therefore, non-invasive neuroimaging has received attention to monitoring and early diagnosing neural disorders to prevent their progress to a severe level. This short review briefly describes the current MRI and PET/CT techniques developed for non-invasive neuroimaging and the future direction of optical imaging techniques to achieve higher resolution and specificity using the second near-infrared (NIR-II) region of wavelength with organic molecules.
Collapse
Affiliation(s)
- Beomsue Kim
- Neural Circuit Research Group, Korea Brain Research Institute (KBRI), 61, Cheomdan-ro, Dong-gu, Daegu, 41068, South Korea.
| | - Hongmin Kim
- Neural Circuit Research Group, Korea Brain Research Institute (KBRI), 61, Cheomdan-ro, Dong-gu, Daegu, 41068, South Korea
| | - Songhui Kim
- Neural Circuit Research Group, Korea Brain Research Institute (KBRI), 61, Cheomdan-ro, Dong-gu, Daegu, 41068, South Korea
| | - Young-Ran Hwang
- Neural Circuit Research Group, Korea Brain Research Institute (KBRI), 61, Cheomdan-ro, Dong-gu, Daegu, 41068, South Korea
| |
Collapse
|
12
|
AL-Quraishi MS, Elamvazuthi I, Tang TB, Al-Qurishi M, Adil SH, Ebrahim M. Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements. Brain Sci 2021; 11:brainsci11060713. [PMID: 34071982 PMCID: PMC8227788 DOI: 10.3390/brainsci11060713] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/19/2021] [Accepted: 05/24/2021] [Indexed: 01/24/2023] Open
Abstract
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.
Collapse
Affiliation(s)
- Maged S. AL-Quraishi
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia; (M.S.A.-Q.); (I.E.)
- Faculty of Engineering, Thamar University, Dhamar 87246, Yemen
| | - Irraivan Elamvazuthi
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia; (M.S.A.-Q.); (I.E.)
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia
- Correspondence: ; Tel.: +60-5-368-7801
| | - Muhammad Al-Qurishi
- Faculty of information and Computer Science, Thamar University, Dhamar 87246, Yemen;
| | - Syed Hasan Adil
- Faculty of Engineering, Sciences and Technology, Iqra University, Karachi 75500, Pakistan; (S.H.A.); (M.E.)
| | - Mansoor Ebrahim
- Faculty of Engineering, Sciences and Technology, Iqra University, Karachi 75500, Pakistan; (S.H.A.); (M.E.)
| |
Collapse
|
13
|
Perpetuini D, Chiarelli AM, Filippini C, Cardone D, Croce P, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1380. [PMID: 33279924 PMCID: PMC7762102 DOI: 10.3390/e22121380] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.
Collapse
Affiliation(s)
- David Perpetuini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Antonio Maria Chiarelli
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Chiara Filippini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Daniela Cardone
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Ludovica Rotunno
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Nelson Anzoletti
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Michele Zito
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Filippo Zappasodi
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| |
Collapse
|