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Marino M, Mantini D. Human brain imaging with high-density electroencephalography: Techniques and applications. J Physiol 2024. [PMID: 39173191 DOI: 10.1113/jp286639] [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: 05/04/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.
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Affiliation(s)
- Marco Marino
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
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Mishra AR, Kumar R, Gupta V, Prabhu S, Upadhyay R, Chhipa PC, Rakesh S, Mokayed H, Das Chakladar D, De K, Liwicki M, Simistira Liwicki F, Saini R. SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems. Sci Data 2024; 11:718. [PMID: 38956046 PMCID: PMC11220021 DOI: 10.1038/s41597-024-03546-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.
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Affiliation(s)
- Ashish Ranjan Mishra
- Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India.
| | - Rakesh Kumar
- Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India
| | - Vibha Gupta
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Sameer Prabhu
- Operation, Maintenance and Acoustics, Luleå University of Technology, Luleå, Sweden
| | - Richa Upadhyay
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Prakash Chandra Chhipa
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Sumit Rakesh
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Hamam Mokayed
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Debashis Das Chakladar
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Kanjar De
- Department of Video Communication and Applications, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany
| | - Marcus Liwicki
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Foteini Simistira Liwicki
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
| | - Rajkumar Saini
- Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems LAB, Luleå, Sweden
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Zayed A, Belhadj N, Ben Khalifa K, Bedoui MH, Valderrama C. Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes. SENSORS (BASEL, SWITZERLAND) 2024; 24:4256. [PMID: 39001037 PMCID: PMC11244425 DOI: 10.3390/s24134256] [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: 05/13/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.
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Affiliation(s)
- Aymen Zayed
- Technology and Medical Imaging Laboratory, Faculty of Medicine Monastir, University of Monastir, Monastir 5019, Tunisia
- National Engineering School of Sousse, University of Sousse, BP 264 Erriyadh, Sousse 4023, Tunisia
- Department of Electronics and Microelectronics (SEMi), University of Mons, 7000 Mons, Belgium
| | - Nidhameddine Belhadj
- Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, Monsatir 5019, Tunisia
| | - Khaled Ben Khalifa
- Technology and Medical Imaging Laboratory, Faculty of Medicine Monastir, University of Monastir, Monastir 5019, Tunisia
- Higher Institute of Applied Science and Technology of Sousse, University of Sousse, Sousse 4003, Tunisia
| | - Mohamed Hedi Bedoui
- Technology and Medical Imaging Laboratory, Faculty of Medicine Monastir, University of Monastir, Monastir 5019, Tunisia
| | - Carlos Valderrama
- Department of Electronics and Microelectronics (SEMi), University of Mons, 7000 Mons, Belgium
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Fang H, Berman SA, Wang Y, Yang Y. Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics. J Neural Eng 2024; 21:036043. [PMID: 38834058 DOI: 10.1088/1741-2552/ad5406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/04/2024] [Indexed: 06/06/2024]
Abstract
Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.
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Affiliation(s)
- Hao Fang
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
| | - Stephen A Berman
- College of Medicine, University of Central Florida, Orlando, FL 32816, United States of America
| | - Yueming Wang
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- Qiushi Academy for Advanced Studies, Hangzhou 310058, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
- State Key Laboratory of Brain-machine Intelligence, Hangzhou 310058, People's Republic of China
| | - Yuxiao Yang
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
- State Key Laboratory of Brain-machine Intelligence, Hangzhou 310058, People's Republic of China
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Hangzhou 310058, People's Republic of China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, People's Republic of China
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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Hossain MS, Mahmud S, Khandakar A, Al-Emadi N, Chowdhury FA, Mahbub ZB, Reaz MBI, Chowdhury MEH. MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals. Bioengineering (Basel) 2023; 10:bioengineering10050579. [PMID: 37237649 DOI: 10.3390/bioengineering10050579] [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: 04/05/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Nasser Al-Emadi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Farhana Ahmed Chowdhury
- Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
| | - Zaid Bin Mahbub
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Department of Electrical and Electronic Engineering, Independent University, Bashundhara, Dhaka 1229, Bangladesh
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Porr B, Daryanavard S, Bohollo LM, Cowan H, Dahiya R. Real-time noise cancellation with deep learning. PLoS One 2022; 17:e0277974. [PMID: 36409690 PMCID: PMC9678292 DOI: 10.1371/journal.pone.0277974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 11/08/2022] [Indexed: 11/22/2022] Open
Abstract
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.
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Affiliation(s)
- Bernd Porr
- Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Sama Daryanavard
- Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Lucía Muñoz Bohollo
- Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Henry Cowan
- Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
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Dillen A, Lathouwers E, Miladinović A, Marusic U, Ghaffari F, Romain O, Meeusen R, De Pauw K. A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics. Front Hum Neurosci 2022; 16:949224. [PMID: 35966996 PMCID: PMC9364873 DOI: 10.3389/fnhum.2022.949224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
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Affiliation(s)
- Arnau Dillen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Elke Lathouwers
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Aleksandar Miladinović
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Institute for Maternal and Child Health - IRCCS Burlo Garofolo, Trieste, Italy
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Fakhreddine Ghaffari
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Olivier Romain
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Romain Meeusen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Kevin De Pauw
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- *Correspondence: Kevin De Pauw
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