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Paari-Molnar E, Kardos K, Told R, Simon I, Sahai N, Szabo P, Bovari-Biri J, Steinerbrunner-Nagy A, Pongracz JE, Rendeki S, Maroti P. Comprehensive Study of Mechanical, Electrical and Biological Properties of Conductive Polymer Composites for Medical Applications through Additive Manufacturing. Polymers (Basel) 2024; 16:2625. [PMID: 39339089 PMCID: PMC11435950 DOI: 10.3390/polym16182625] [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: 08/12/2024] [Revised: 09/13/2024] [Accepted: 09/15/2024] [Indexed: 09/30/2024] Open
Abstract
Conductive polymer composites are commonly present in flexible electrodes for neural interfaces, implantable sensors, and aerospace applications. Fused filament fabrication (FFF) is a widely used additive manufacturing technology, where conductive filaments frequently contain carbon-based fillers. In this study, the static and dynamic mechanical properties and the electrical properties (resistance, signal transmission, resistance measurements during cyclic tensile, bending and temperature tests) were investigated for polylactic acid (PLA)-based, acrylonitrile butadiene styrene (ABS)-based, thermoplastic polyurethane (TPU)-based, and polyamide (PA)-based conductive filaments with carbon-based additives. Scanning electron microscopy (SEM) was implemented to evaluate the results. Cytotoxicity measurements were performed. The conductive ABS specimens have a high gauge factor between 0.2% and 1.0% strain. All tested materials, except the PA-based conductive composite, are suitable for low-voltage applications such as 3D-printed EEG and EMG sensors. ABS-based and TPU-based conductive composites are promising raw materials suitable for temperature measuring and medical applications.
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Affiliation(s)
- Emese Paari-Molnar
- 3D Printing and Visualization Centre, University of Pecs, Boszorkany Str. 2, H-7624 Pecs, Hungary
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
| | - Kinga Kardos
- 3D Printing and Visualization Centre, University of Pecs, Boszorkany Str. 2, H-7624 Pecs, Hungary
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
| | - Roland Told
- 3D Printing and Visualization Centre, University of Pecs, Boszorkany Str. 2, H-7624 Pecs, Hungary
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
| | - Imre Simon
- 3D Printing and Visualization Centre, University of Pecs, Boszorkany Str. 2, H-7624 Pecs, Hungary
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
| | - Nitin Sahai
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, Meghalaya, India
| | - Peter Szabo
- Institute of Geography and Earth Sciences, Faculty of Sciences, University of Pecs, Ifjusag Str. 6, H-7624 Pecs, Hungary
- Environmental Analytical and Geoanalytical Research Group, Szentágothai Research Centre, University of Pecs, H-7624 Pecs, Hungary
| | - Judit Bovari-Biri
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, University of Pecs, Rokus Str. 2, H-7624 Pecs, Hungary
| | - Alexandra Steinerbrunner-Nagy
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, University of Pecs, Rokus Str. 2, H-7624 Pecs, Hungary
| | - Judit E Pongracz
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, University of Pecs, Rokus Str. 2, H-7624 Pecs, Hungary
| | - Szilard Rendeki
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
| | - Peter Maroti
- 3D Printing and Visualization Centre, University of Pecs, Boszorkany Str. 2, H-7624 Pecs, Hungary
- Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, H-7624 Pecs, Hungary
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2
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Wang Z, Ding Y, Yuan W, Chen H, Chen W, Chen C. Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas. Bioengineering (Basel) 2024; 11:276. [PMID: 38534550 DOI: 10.3390/bioengineering11030276] [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: 02/07/2024] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infections, and signal performance deterioration that may occur over time due to the air drying of the conductive gel. Although the emergence of dry electrodes has overcome these shortcomings, their electrode-skin contact impedance is significantly high and unstable, especially in hair-covered areas. To address the above problems, an active claw-shaped dry electrode is designed, moving from electrode morphological design, slurry preparation, and coating to active electrode circuit design. The active claw-shaped dry electrode, which consists of a claw-shaped electrode and active electrode circuit, is dedicated to offering a flexible solution for elevating electrode fittings on the scalp in hair-covered areas, reducing electrode-skin contact impedance and thus improving the quality of the acquired EEG signal. The performance of the proposed electrodes was verified by impedance, active electrode circuit, eyes open-closed, steady-state visually evoked potential (SSVEP), and anti-interference tests, based on EEG signal acquisition. Experimental results show that the proposed claw-shaped electrodes (without active circuit) can offer a better fit between the scalp and electrodes, with a low electrode-skin contact impedance (18.62 KΩ@1 Hz in the hairless region and 122.15 KΩ@1 Hz in the hair-covered region). In addition, with the active circuit, the signal-to-noise ratio (SNR) of the acquiring EEG signal was improved and power frequency interference was restrained, therefore, the proposed electrodes can yield an EEG signal quality comparable to wet electrodes.
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Affiliation(s)
- Zaihao Wang
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Yuhao Ding
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Yuan
- Center for Intelligent Medical Equipment and Devices, Institute for Innovative Medical Devices, School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Hongyu Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Chen Chen
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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3
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Lin S, Jiang J, Huang K, Li L, He X, Du P, Wu Y, Liu J, Li X, Huang Z, Zhou Z, Yu Y, Gao J, Lei M, Wu H. Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces. ACS NANO 2023; 17:24487-24513. [PMID: 38064282 DOI: 10.1021/acsnano.3c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.
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Affiliation(s)
- Sen Lin
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jingjing Jiang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Kai Huang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
| | - Xian He
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Peng Du
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Yufeng Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Junchen Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xilin Li
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning 530004, China
| | - Zhibao Huang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Zenan Zhou
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Yuanhang Yu
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jiaxin Gao
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Ming Lei
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hui Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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Zhang A, Shyam AB, Cunningham AM, Williams C, Brissenden A, Bartley A, Amsden B, Docoslis A, Kontopoulou M, Ameri SK. Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp. Adv Healthc Mater 2023; 12:e2300142. [PMID: 37165724 PMCID: PMC11469214 DOI: 10.1002/adhm.202300142] [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: 01/12/2023] [Revised: 03/23/2023] [Indexed: 05/12/2023]
Abstract
Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and Internet of Things. Conventional electroencephalography relies on wet and dry electrodes. Despite favorable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes, and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Herein, a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite is reported. The CMSA sensor is fabricated using the innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes.
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Affiliation(s)
- Anan Zhang
- Department of Electrical and Computer EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | | | | | - Christopher Williams
- Department of Chemical EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Amanda Brissenden
- Department of Chemical EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Alex Bartley
- Department of Electrical and Computer EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Brian Amsden
- Department of Chemical EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Aristides Docoslis
- Department of Chemical EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Marianna Kontopoulou
- Department of Chemical EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
| | - Shideh Kabiri Ameri
- Department of Electrical and Computer EngineeringQueen's UniversityKingstonOntarioK7L 3N6Canada
- Centre for Neuroscience Studies (CNS)Queen's UniversityKingstonOntarioK7L 3N6Canada
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5
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Lee DH, Park T, Yoo H. Biodegradable Polymer Composites for Electrophysiological Signal Sensing. Polymers (Basel) 2022; 14:polym14142875. [PMID: 35890650 PMCID: PMC9323782 DOI: 10.3390/polym14142875] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/09/2022] [Accepted: 07/13/2022] [Indexed: 12/23/2022] Open
Abstract
Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited.
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Affiliation(s)
- Dong Hyun Lee
- Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Korea;
| | - Taehyun Park
- Department of Chemical and Biological Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Korea;
| | - Hocheon Yoo
- Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Korea;
- Correspondence:
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6
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Fiedler P, Fonseca C, Supriyanto E, Zanow F, Haueisen J. A high-density 256-channel cap for dry electroencephalography. Hum Brain Mapp 2021; 43:1295-1308. [PMID: 34796574 PMCID: PMC8837591 DOI: 10.1002/hbm.25721] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/29/2021] [Accepted: 11/08/2021] [Indexed: 11/09/2022] Open
Abstract
High‐density electroencephalography (HD‐EEG) is currently limited to laboratory environments since state‐of‐the‐art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256‐channel cap with dry multipin electrodes for HD‐EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD‐EEG cap to a conventional gel‐based cap for electrode‐skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 kΩ for 252 out of 256 dry electrodes enables recording with state‐of‐the‐art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel‐based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel‐based cap prior and after the EEG recordings, respectively (scale 1–10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256‐channel HD‐EEG dry electrode cap overcomes the principal limitations of HD‐EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD‐EEG.
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Affiliation(s)
- Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität IlmenauIlmenauGermany
| | - Carlos Fonseca
- Faculdade de Engenharia, Departamento de Engenharia Metalúrgica e de MateriaisUniversidade do PortoPortoPortugal
- LAETA/INEGI, Institute of Science and Innovation in Mechanical and Industrial EngineeringPortoPortugal
| | - Eko Supriyanto
- IJN‐UTM Cardiovascular Engineering Centre, Universiti Teknologi MalaysiaJohor BahruMalaysia
| | - Frank Zanow
- eemagine Medical Imaging Solutions GmbHBerlinGermany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität IlmenauIlmenauGermany
- Department of NeurologyBiomagnetic Center, University Hospital JenaJenaGermany
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7
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Vasconcelos B, Fiedler P, Machts R, Haueisen J, Fonseca C. The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort. Front Neurosci 2021; 15:748100. [PMID: 34733134 PMCID: PMC8558300 DOI: 10.3389/fnins.2021.748100] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/27/2021] [Indexed: 11/27/2022] Open
Abstract
Electroencephalography (EEG) is increasingly used for repetitive and prolonged applications like neurofeedback, brain computer interfacing, and long-term intermittent monitoring. Dry-contact electrodes enable rapid self-application. A common drawback of existing dry electrodes is the limited wearing comfort during prolonged application. We propose a novel dry Arch electrode. Five semi-circular arches are arranged parallelly on a common baseplate. The electrode substrate material is a flexible thermoplastic polyurethane (TPU) produced by additive manufacturing. A chemical coating of Silver/Silver-Chloride (Ag/AgCl) is applied by electroless plating using a novel surface functionalization method. Arch electrodes were manufactured and validated in terms of mechanical durability, electrochemical stability, in vivo applicability, and signal characteristics. We compare the results of the dry arch electrodes with dry pin-shaped and conventional gel-based electrodes. 21-channel EEG recordings were acquired on 10 male and 5 female volunteers. The tests included resting state EEG, alpha activity, and a visual evoked potential. Wearing comfort was rated by the subjects directly after application, as well as at 30 min and 60 min of wearing. Our results show that the novel plating technique provides a well-adhering electrically conductive and electrochemically stable coating, withstanding repetitive strain and bending tests. The signal quality of the Arch electrodes is comparable to pin-shaped dry electrodes. The average channel reliability of the Arch electrode setup was 91.9 ± 9.5%. No considerable differences in signal characteristics have been observed for the gel-based, dry pin-shaped, and arch-shaped electrodes after the identification and exclusion of bad channels. The comfort was improved in comparison to pin-shaped electrodes and enabled applications of over 60 min duration. Arch electrodes required individual adaptation of the electrodes to the orientation and hairstyle of the volunteers. This initial preparation time of the 21-channel cap increased from an average of 5 min for pin-like electrodes to 15 min for Arch electrodes and 22 min for gel-based electrodes. However, when re-applying the arch electrode cap on the same volunteer, preparation times of pin-shaped and arch-shaped electrodes were comparable. In summary, our results indicate the applicability of the novel Arch electrode and coating for EEG acquisition. The novel electrode enables increased comfort for prolonged dry-contact measurement.
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Affiliation(s)
- Beatriz Vasconcelos
- Departamento de Engenharia Metalúrgica e de Materiais, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.,CEMUC - Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - René Machts
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany.,Department of Neurology, Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Carlos Fonseca
- Departamento de Engenharia Metalúrgica e de Materiais, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.,LAETA/INEGI, Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
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吕 晓, 丁 鹏, 李 思, 龚 安, 赵 磊, 钱 谦, 苏 磊, 伏 云. [Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:210-223. [PMID: 33913280 PMCID: PMC9927690 DOI: 10.7507/1001-5515.202101093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/05/2021] [Indexed: 11/03/2022]
Abstract
Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.
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Affiliation(s)
- 晓彤 吕
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 鹏 丁
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 思语 李
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 安民 龚
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 磊 赵
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 谦 钱
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 武警工程大学 信息工程学院(西安 710000)College of Information Engineering, Engineering University of PAP, Xi’an 710000, P.R.China
| | - 磊 苏
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
| | - 云发 伏
- 昆明理工大学 信息工程与自动化学院(昆明 650500)School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 昆明理工大学 脑认知与脑机智能融合创新团队(昆明 650500)Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China
- 武警工程大学 信息工程学院(西安 710000)College of Information Engineering, Engineering University of PAP, Xi’an 710000, P.R.China
- 昆明理工大学 理学院(昆明 650500)Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P.R.China
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9
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Nakatani S, Araki N, Hoshino T, Fukayama O, Mabuchi K. Brain-controlled cycling system for rehabilitation following paraplegia with delay-time prediction. J Neural Eng 2020; 18. [PMID: 33291086 DOI: 10.1088/1741-2552/abd1bf] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 12/08/2020] [Indexed: 11/11/2022]
Abstract
Objective.Robotic rehabilitation systems have been investigated to assist with motor dysfunction recovery in patients with lower-extremity paralysis caused by central nervous system lesions. These systems are intended to provide appropriate sensory feedback associated with locomotion. Appropriate feedback is thought to cause synchronous neuron firing, resulting in the recovery of function.Approach.In this study, we designed and evaluated an ergometric cycling wheelchair, with a brain-machine interface (BMI), that can force the legs to move by including normal stepping speeds and quick responses. Experiments were conducted in five healthy subjects and one patient with spinal cord injury (SCI), who experienced the complete paralysis of the lower limbs. Event-related desynchronization (ERD) in the β band (18-28 Hz) was used to detect lower-limb motor images.Main results.An ergometer-based BMI system was able to safely and easily force patients to perform leg movements, at a rate of approximately 1.6 seconds/step (19 rpm), with an online accuracy rate of 73.1% for the SCI participant. Mean detection time from the cue to pedaling onset was 0.83±0.31 s.Significance.This system can easily and safely maintain a normal walking speed during the experiment and be designed to accommodate the expected delay between the intentional onset and physical movement, to achieve rehabilitation effects for each participant. Similar BMI systems, implemented with rehabilitation systems, may be applicable to a wide range of patients.
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Affiliation(s)
- Shintaro Nakatani
- School of engineering, Tottori University, 101, 4 cho-me, Koyama-cho Minami School of Engineering Tottori university, Tottori, Tottori, 680-8550, JAPAN
| | - Nozomu Araki
- Graduate school of engineering, University of Hyogo, 2167, Shosha, Himeji, Hyogo, 671-2280, JAPAN
| | - Takayuki Hoshino
- Department of Mechanical Science, Hirosaki University, 3, Bunkyo, Hirosaki, Aomori, 036-8561, JAPAN
| | - Osamu Fukayama
- National Institute of Information and Communications Technology Center for Information and Neural Networks, 1-4 Yamadaoka, Suita, Osaka, 565-0871, JAPAN
| | - Kunihiko Mabuchi
- The University of Tokyo Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, JAPAN
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