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Wang X, Chen S, Wang X, Song Z, Wang Z, Niu X, Chen X, Chen X. Application of artificial hibernation technology in acute brain injury. Neural Regen Res 2024; 19:1940-1946. [PMID: 38227519 DOI: 10.4103/1673-5374.390968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/20/2023] [Indexed: 01/17/2024] Open
Abstract
Controlling intracranial pressure, nerve cell regeneration, and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury. There is currently a lack of effective treatment methods. Hibernation has the characteristics of low temperature, low metabolism, and hibernation rhythm, as well as protective effects on the nervous, cardiovascular, and motor systems. Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body's metabolism, lowering the body's core temperature, and allowing the body to enter a state similar to hibernation. This review introduces artificial hibernation technology, including mild hypothermia treatment technology, central nervous system regulation technology, and artificial hibernation-inducer technology. Upon summarizing the relevant research on artificial hibernation technology in acute brain injury, the research results show that artificial hibernation technology has neuroprotective, anti-inflammatory, and oxidative stress-resistance effects, indicating that it has therapeutic significance in acute brain injury. Furthermore, artificial hibernation technology can alleviate the damage of ischemic stroke, traumatic brain injury, cerebral hemorrhage, cerebral infarction, and other diseases, providing new strategies for treating acute brain injury. However, artificial hibernation technology is currently in its infancy and has some complications, such as electrolyte imbalance and coagulation disorders, which limit its use. Further research is needed for its clinical application.
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Affiliation(s)
- Xiaoni Wang
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shulian Chen
- Characteristic Medical Center of People's Armed Police Forces, Tianjin, China
| | - Xiaoyu Wang
- Characteristic Medical Center of People's Armed Police Forces, Tianjin, China
| | - Zhen Song
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ziqi Wang
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaofei Niu
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaochu Chen
- Characteristic Medical Center of People's Armed Police Forces, Tianjin, China
| | - Xuyi Chen
- Characteristic Medical Center of People's Armed Police Forces, Tianjin, China
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Barmpas K, Panagakis Y, Zoumpourlis G, Adamos DA, Laskaris N, Zafeiriou S. A causal perspective on brainwave modeling for brain-computer interfaces. J Neural Eng 2024; 21:036001. [PMID: 38621380 DOI: 10.1088/1741-2552/ad3eb5] [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: 09/06/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.
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Affiliation(s)
- Konstantinos Barmpas
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Yannis Panagakis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens 15784, Greece
- Archimedes Research Unit, Research Center Athena, Athens 15125, Greece
- Cogitat Ltd, London, United Kingdom
| | | | - Dimitrios A Adamos
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Nikolaos Laskaris
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Stefanos Zafeiriou
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
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3
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Han Y, Ke Y, Wang R, Wang T, Ming D. Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1407-1415. [PMID: 38517720 DOI: 10.1109/tnsre.2024.3380635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.
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Sieghartsleitner S, Sebastián-Romagosa M, Cho W, Grünwald J, Ortner R, Scharinger J, Kamada K, Guger C. Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system. Front Neurosci 2024; 18:1346607. [PMID: 38500488 PMCID: PMC10944934 DOI: 10.3389/fnins.2024.1346607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/08/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions. Methods Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively. Results Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed. Discussion The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.
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Affiliation(s)
- Sebastian Sieghartsleitner
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Johannes Grünwald
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Rupert Ortner
- g.tec Medical Engineering Spain S.L., Barcelona, Spain
| | - Josef Scharinger
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Christoph Guger
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
- g.tec Medical Engineering Spain S.L., Barcelona, Spain
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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Hooks K, El-Said R, Fu Q. Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb. Front Hum Neurosci 2023; 17:1302647. [PMID: 38021246 PMCID: PMC10663285 DOI: 10.3389/fnhum.2023.1302647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Fundamental to human movement is the ability to interact with objects in our environment. How one reaches an object depends on the object's shape and intended interaction afforded by the object, e.g., grasp and transport. Extensive research has revealed that the motor intention of reach-to-grasp can be decoded from cortical activities using EEG signals. The goal of the present study is to determine the extent to which information encoded in the EEG signals is shared between two limbs to enable cross-hand decoding. We performed an experiment in which human subjects (n = 10) were tasked to interact with a novel object with multiple affordances using either right or left hands. The object had two vertical handles attached to a horizontal base. A visual cue instructs what action (lift or touch) and whether the left or right handle should be used for each trial. EEG was recorded and processed from bilateral frontal-central-parietal regions (30 channels). We trained LDA classifiers using data from trials performed by one limb and tested the classification accuracy using data from trials performed by the contralateral limb. We found that the type of hand-object interaction can be decoded with approximately 59 and 69% peak accuracy in the planning and execution stages, respectively. Interestingly, the decoding accuracy of the reaching directions was dependent on how EEG channels in the testing dataset were spatially mirrored, and whether directions were labeled in the extrinsic (object-centered) or intrinsic (body-centered) coordinates.
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Affiliation(s)
- Kevin Hooks
- Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States
| | - Refaat El-Said
- College of Medicine, University of Central Florida, Orlando, FL, United States
| | - Qiushi Fu
- Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States
- Biionix Cluster, University of Central Florida, Orlando, FL, United States
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Liu M, Li T, Zhang X, Yang Y, Zhou Z, Fu T. IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification. Comput Methods Biomech Biomed Engin 2023:1-14. [PMID: 37936533 DOI: 10.1080/10255842.2023.2275244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/17/2023] [Indexed: 11/09/2023]
Abstract
As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.
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Affiliation(s)
- Menghao Liu
- Mechanical College, Shanghai Dianji University, Shanghai, China
| | - Tingting Li
- Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xu Zhang
- Mechanical College, Shanghai Dianji University, Shanghai, China
| | - Yang Yang
- Shanghai Lanhui Medical Technology Co., Ltd, Shanghai, China
| | - Zhiyong Zhou
- Mechanical College, Shanghai Dianji University, Shanghai, China
| | - Tianhao Fu
- Mechanical College, Shanghai Dianji University, Shanghai, China
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Karikari E, Koshechkin KA. Review on brain-computer interface technologies in healthcare. Biophys Rev 2023; 15:1351-1358. [PMID: 37974976 PMCID: PMC10643750 DOI: 10.1007/s12551-023-01138-6] [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: 08/21/2023] [Accepted: 08/31/2023] [Indexed: 11/19/2023] Open
Abstract
Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.
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Affiliation(s)
- Evelyn Karikari
- Department of Public Health and Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Konstantin A Koshechkin
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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Orban M, Guo K, Yang H, Hu X, Hassaan M, Elsamanty M. Soft pneumatic muscles for post-stroke lower limb ankle rehabilitation: leveraging the potential of soft robotics to optimize functional outcomes. Front Bioeng Biotechnol 2023; 11:1251879. [PMID: 37781541 PMCID: PMC10539589 DOI: 10.3389/fbioe.2023.1251879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction: A soft pneumatic muscle was developed to replicate intricate ankle motions essential for rehabilitation, with a specific focus on rotational movement along the x-axis, crucial for walking. The design incorporated precise geometrical parameters and air pressure regulation to enable controlled expansion and motion. Methods: The muscle's response was evaluated under pressure conditions ranging from 100-145 kPa. To optimize the muscle design, finite element simulation was employed to analyze its performance in terms of motion range, force generation, and energy efficiency. An experimental platform was created to assess the muscle's deformation, utilizing advanced techniques such as high-resolution imaging and deep-learning position estimation models for accurate measurements. The fabrication process involved silicone-based materials and 3D-printed molds, enabling precise control and customization of muscle expansion and contraction. Results: The experimental results demonstrated that, under a pressure of 145 kPa, the y-axis deformation (y-def) reached 165 mm, while the x-axis and z-axis deformations were significantly smaller at 0.056 mm and 0.0376 mm, respectively, highlighting the predominant elongation in the y-axis resulting from pressure actuation. The soft muscle model featured a single chamber constructed from silicone rubber, and the visually illustrated and detailed geometrical parameters played a critical role in its functionality, allowing systematic manipulation to meet specific application requirements. Discussion: The simulation and experimental results provided compelling evidence of the soft muscle design's adaptability, controllability, and effectiveness, thus establishing a solid foundation for further advancements in ankle rehabilitation and soft robotics. Incorporating this soft muscle into rehabilitation protocols holds significant promise for enhancing ankle mobility and overall ambulatory function, offering new opportunities to tailor rehabilitation interventions and improve motor function restoration.
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Affiliation(s)
- Mostafa Orban
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
| | - Kai Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Hongbo Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xuhui Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Mohamed Hassaan
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
| | - Mahmoud Elsamanty
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
- Mechatronics and Robotics Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt
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Chaddad A, Wu Y, Kateb R, Bouridane A. Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:6434. [PMID: 37514728 PMCID: PMC10385593 DOI: 10.3390/s23146434] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
- The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
| | - Yihang Wu
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
| | - Reem Kateb
- College of Computer Science and Engineering, Taibah University, Madinah 41477, Saudi Arabia
| | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates
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11
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Peksa J, Mamchur D. State-of-the-Art on Brain-Computer Interface Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:6001. [PMID: 37447849 DOI: 10.3390/s23136001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.
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Affiliation(s)
- Janis Peksa
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Institute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
| | - Dmytro Mamchur
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Computer Engineering and Electronics Department, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600 Kremenchuk, Ukraine
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Guo K, Orban M, Lu J, Al-Quraishi MS, Yang H, Elsamanty M. Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System. Bioengineering (Basel) 2023; 10:bioengineering10050557. [PMID: 37237627 DOI: 10.3390/bioengineering10050557] [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: 04/01/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient's physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution-a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4-5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove's effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm (the 1D-CNN algorithm and the InceptionTime algorithm). The InceptionTime algorithm classified ten hand gestures' sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke's physical, financial, and social impact.
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Affiliation(s)
- Kai Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Mostafa Orban
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Jingxin Lu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130001, China
| | | | - Hongbo Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130001, China
| | - Mahmoud Elsamanty
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
- Mechatronics and Robotics Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
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