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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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2
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Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [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: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
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Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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3
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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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Al-Omairi HR, AL-Zubaidi A, Fudickar S, Hein A, Rieger JW. Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique. SENSORS (BASEL, SWITZERLAND) 2024; 24:3173. [PMID: 38794026 PMCID: PMC11125330 DOI: 10.3390/s24103173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.
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Affiliation(s)
- Hayder R. Al-Omairi
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Department of Biomedical Engineering, University of Technology—Iraq, Baghdad 10066, Iraq
| | - Arkan AL-Zubaidi
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Sebastian Fudickar
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany; (S.F.); (A.H.)
- Institute for Medical Informatics, University of Lübeck, 23562 Lübeck, Germany
| | - Andreas Hein
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany; (S.F.); (A.H.)
| | - Jochem W. Rieger
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
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5
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Ercan R, Xia Y, Zhao Y, Loureiro R, Yang S, Zhao H. An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2024; 32:763-773. [PMID: 38765316 PMCID: PMC11100859 DOI: 10.1109/tvlsi.2024.3356161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/02/2023] [Accepted: 01/13/2024] [Indexed: 05/22/2024]
Abstract
Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.
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Affiliation(s)
- Renas Ercan
- UCLUCLWC1E 6BTLondonU.K.
- Department of PhysicsUniversity of CambridgeCB2 1TNCambridgeU.K.
| | - Yunjia Xia
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
| | - Yunyi Zhao
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
| | - Rui Loureiro
- IOMS, Division of Surgery and Interventional Science, UCLWC1E 6BTLondonU.K.
| | - Shufan Yang
- UCLUCLWC1E 6BTLondonU.K.
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of LeedsLS2 9JTLeedsU.K.
| | - Hubin Zhao
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
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Tao Q, Liu S, Zhang J, Jiang J, Jin Z, Huang Y, Liu X, Lin S, Zeng X, Li X, Tao G, Chen H. Clinical applications of smart wearable sensors. iScience 2023; 26:107485. [PMID: 37636055 PMCID: PMC10448028 DOI: 10.1016/j.isci.2023.107485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023] Open
Abstract
Smart wearable sensors are electronic devices worn on the body that collect, process, and transmit various physiological data. Compared to traditional devices, their advantages in terms of portability and comfort have made them increasingly important in the medical field. This review takes a unique clinical physician's standpoint, diverging from conventional sensor-type-based classifications, and provides a comprehensive overview of the diverse clinical applications of wearable sensors in recent years. In this review, we categorize these applications according to different diseases, encompassing skin diseases and injuries, cardiovascular diseases, abnormal human motion, as well as endocrine and metabolic disorders. Additionally, we discuss the challenges and perspectives hindering the development of sensors for clinical use, emphasizing the critical need for interdisciplinary collaboration between medical and engineering professionals. Overall, this review would serve as an important reference for the future direction of sensor devices in clinical use.
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Affiliation(s)
- Qingxiao Tao
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Suwen Liu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Jingyu Zhang
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
- Shenzhen University Medical School, Shenzhen 518060, China
| | - Jian Jiang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Zilin Jin
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Yuqiong Huang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xin Liu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shiying Lin
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xin Zeng
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
| | - Xuemei Li
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
| | - Guangming Tao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hongxiang Chen
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
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7
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Huang R, Hong KS, Yang D, Huang G. Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review. Front Neurosci 2022; 16:878750. [PMID: 36263362 PMCID: PMC9576156 DOI: 10.3389/fnins.2022.878750] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/04/2022] Open
Abstract
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
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Affiliation(s)
- Ruisen Huang
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
- *Correspondence: Keum-Shik Hong,
| | - Dalin Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao, China
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Carretero A, Araujo A. Analysis of Simple Algorithms for Motion Detection in Wearable Devices. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2410-2415. [PMID: 36086250 DOI: 10.1109/embc48229.2022.9871070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 DOI: 10.1109/access.2022.3159155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/27/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh
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10
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 PMCID: PMC9102309 DOI: 10.3390/s22093169] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/14/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar;
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh;
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Beach C, Li M, Balaban E, Casson AJ. Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering. Healthc Technol Lett 2021; 8:128-138. [PMID: 34584747 PMCID: PMC8450177 DOI: 10.1049/htl2.12016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/14/2021] [Accepted: 05/28/2021] [Indexed: 11/24/2022] Open
Abstract
This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.
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Affiliation(s)
- Christopher Beach
- Department of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
| | | | - Ertan Balaban
- Department of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
| | - Alexander J. Casson
- Department of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
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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.
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13
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Neonatal NIRS monitoring: recommendations for data capture and review of analytics. J Perinatol 2021; 41:675-688. [PMID: 33589724 PMCID: PMC7883881 DOI: 10.1038/s41372-021-00946-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/20/2020] [Accepted: 01/19/2021] [Indexed: 01/29/2023]
Abstract
Brain injury is one of the most consequential problems facing neonates, with many preterm and term infants at risk for cerebral hypoxia and ischemia. To develop effective neuroprotective strategies, the mechanistic basis for brain injury must be understood. The fragile state of neonates presents unique research challenges; invasive measures of cerebral blood flow and oxygenation assessment exceed tolerable risk profiles. Near-infrared spectroscopy (NIRS) can safely and non-invasively estimate cerebral oxygenation, a correlate of cerebral perfusion, offering insight into brain injury-related mechanisms. Unfortunately, lack of standardization in device application, recording methods, and error/artifact correction have left the field fractured. In this article, we provide a framework for neonatal NIRS research. Our goal is to provide a rational basis for NIRS data capture and processing that may result in better comparability between studies. It is also intended to serve as a primer for new NIRS researchers and assist with investigation initiation.
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Lyu B, Pham T, Blaney G, Haga Z, Sassaroli A, Fantini S, Aeron S. Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200209SSRR. [PMID: 33415849 PMCID: PMC7790507 DOI: 10.1117/1.jbo.26.2.022908] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 11/25/2020] [Indexed: 06/12/2023]
Abstract
SIGNIFICANCE We demonstrated the potential of using domain adaptation on functional near-infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory. AIM Domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects. To address this problem, two domain adaptation approaches-Gromov-Wasserstein (G-W) and fused Gromov-Wasserstein (FG-W) were used. APPROACH Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels. We compared these approaches with three supervised methods: multiclass support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN). RESULTS In a sample of six subjects, G-W resulted in an alignment accuracy of 68 % ± 4 % (weighted mean ± standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 % ± 2 % for subject-by-subject alignment. In each of these cases, 25% accuracy represents chance. Alignment accuracy results from both G-W and FG-W are significantly greater than those from SVM, CNN, and RNN. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. CONCLUSIONS Domain adaptation has potential for session-by-session and subject-by-subject alignment of mental workload by using fNIRS data.
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Affiliation(s)
- Boyang Lyu
- Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts, United States
| | - Thao Pham
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Zachary Haga
- Tufts University, Department of Computer Science, Medford, Massachusetts, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Shuchin Aeron
- Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts, United States
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Siddiquee MR, Atri R, Marquez JS, Hasan SMS, Ramon R, Bai O. Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5082. [PMID: 32906737 PMCID: PMC7570614 DOI: 10.3390/s20185082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/31/2020] [Accepted: 08/31/2020] [Indexed: 11/28/2022]
Abstract
Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators' cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.
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Affiliation(s)
- Masudur R. Siddiquee
- Human Cyber-Physical Systems Laboratory, Florida International University, Miami, FL 33174, USA; (R.A.); (J.S.M.); (S.M.S.H.); (R.R.); (O.B.)
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Shafiul Hasan SM, Siddiquee MR, Atri R, Ramon R, Marquez JS, Bai O. Prediction of gait intention from pre-movement EEG signals: a feasibility study. J Neuroeng Rehabil 2020; 17:50. [PMID: 32299460 PMCID: PMC7164221 DOI: 10.1186/s12984-020-00675-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 04/01/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. METHODS An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. RESULTS Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. CONCLUSION Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.
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Affiliation(s)
- S. M. Shafiul Hasan
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Masudur R. Siddiquee
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Roozbeh Atri
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Rodrigo Ramon
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - J. Sebastian Marquez
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Ou Bai
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
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