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Wang G, You C, Feng C, Yao W, Zhao Z, Xue N, Yao L. Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance. BIOSENSORS 2024; 14:343. [PMID: 39056619 PMCID: PMC11275126 DOI: 10.3390/bios14070343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.
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Affiliation(s)
- Gang Wang
- School of Microelectronics, Shanghai University, Shanghai 200444, China;
- Zhangjiang Laboratory, Shanghai 200031, China
| | - Changhua You
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100190, China;
| | - Chengcong Feng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; (C.F.); (Z.Z.)
| | - Wenliang Yao
- Shanghai Mtrix Technology Co., Ltd., Shanghai 200031, China;
| | - Zhengtuo Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; (C.F.); (Z.Z.)
| | - Ning Xue
- Lingang Laboratory, Shanghai 200031, China;
| | - Lei Yao
- Lingang Laboratory, Shanghai 200031, China;
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Liu H, Wei P, Wang H, Lv X, Duan W, Li M, Zhao Y, Wang Q, Chen X, Shi G, Han B, Hao J. An EEG motor imagery dataset for brain computer interface in acute stroke patients. Sci Data 2024; 11:131. [PMID: 38272904 PMCID: PMC10811218 DOI: 10.1038/s41597-023-02787-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 11/24/2023] [Indexed: 01/27/2024] Open
Abstract
The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.
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Affiliation(s)
- Haijie Liu
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
- National Center for Neurological Disorders, Beijing, 100053, China
| | - Penghu Wei
- National Center for Neurological Disorders, Beijing, 100053, China
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Haochong Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shanxi, 710049, China
| | - Xiaodong Lv
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
- National Center for Neurological Disorders, Beijing, 100053, China
| | - Wei Duan
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100000, China
| | - Meijie Li
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
- National Center for Neurological Disorders, Beijing, 100053, China
| | - Yan Zhao
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
- National Center for Neurological Disorders, Beijing, 100053, China
| | - Qingmei Wang
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Xinyuan Chen
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gaige Shi
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shanxi, 710049, China
| | - Bo Han
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
- National Center for Neurological Disorders, Beijing, 100053, China
| | - Junwei Hao
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
- National Center for Neurological Disorders, Beijing, 100053, China.
- Chinese Institute for Brain Research, Beijing, 100053, China.
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Okatan M, Kocatürk M. Decoding the Spike-Band Subthreshold Motor Cortical Activity. J Mot Behav 2023; 56:161-183. [PMID: 37964432 DOI: 10.1080/00222895.2023.2280263] [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: 01/23/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.
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Affiliation(s)
- Murat Okatan
- Informatics Institute, Istanbul Technical University, Istanbul, Türkiye
- Artificial Intelligence and Data Engineering Department, Istanbul Technical University, Istanbul, Türkiye
| | - Mehmet Kocatürk
- Biomedical Engineering Department, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
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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: 5] [Impact Index Per Article: 5.0] [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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Pinheiro DJLL, Faber J, Micera S, Shokur S. Human-machine interface for two-dimensional steering control with the auricular muscles. Front Neurorobot 2023; 17:1154427. [PMID: 37342389 PMCID: PMC10277645 DOI: 10.3389/fnbot.2023.1154427] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/16/2023] [Indexed: 06/22/2023] Open
Abstract
Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20-30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.
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Affiliation(s)
- Daniel J. L. L. Pinheiro
- Division of Neuroscience, Department of Neurology and Neurosurgery, Neuroengineering and Neurocognition Laboratory, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- Translational Neural Engineering Lab, Institute Neuro X, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jean Faber
- Division of Neuroscience, Department of Neurology and Neurosurgery, Neuroengineering and Neurocognition Laboratory, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
- Neuroengineering Laboratory, Division of Biomedical Engineering, Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, Brazil
| | - Silvestro Micera
- Translational Neural Engineering Lab, Institute Neuro X, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Excellence in Robotics and AI, Institute of BioRobotics Interdisciplinary Health Center, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Solaiman Shokur
- Translational Neural Engineering Lab, Institute Neuro X, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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Wan Z, Liu T, Ran X, Liu P, Chen W, Zhang S. The influence of non-stationarity of spike signals on decoding performance in intracortical brain-computer interface: a simulation study. Front Comput Neurosci 2023; 17:1135783. [PMID: 37251598 PMCID: PMC10213332 DOI: 10.3389/fncom.2023.1135783] [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: 01/01/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Intracortical Brain-Computer Interfaces (iBCI) establish a new pathway to restore motor functions in individuals with paralysis by interfacing directly with the brain to translate movement intention into action. However, the development of iBCI applications is hindered by the non-stationarity of neural signals induced by the recording degradation and neuronal property variance. Many iBCI decoders were developed to overcome this non-stationarity, but its effect on decoding performance remains largely unknown, posing a critical challenge for the practical application of iBCI. Methods To improve our understanding on the effect of non-stationarity, we conducted a 2D-cursor simulation study to examine the influence of various types of non-stationarities. Concentrating on spike signal changes in chronic intracortical recording, we used the following three metrics to simulate the non-stationarity: mean firing rate (MFR), number of isolated units (NIU), and neural preferred directions (PDs). MFR and NIU were decreased to simulate the recording degradation while PDs were changed to simulate the neuronal property variance. Performance evaluation based on simulation data was then conducted on three decoders and two different training schemes. Optimal Linear Estimation (OLE), Kalman Filter (KF), and Recurrent Neural Network (RNN) were implemented as decoders and trained using static and retrained schemes. Results In our evaluation, RNN decoder and retrained scheme showed consistent better performance under small recording degradation. However, the serious signal degradation would cause significant performance to drop eventually. On the other hand, RNN performs significantly better than the other two decoders in decoding simulated non-stationary spike signals, and the retrained scheme maintains the decoders' high performance when changes are limited to PDs. Discussion Our simulation work demonstrates the effects of neural signal non-stationarity on decoding performance and serves as a reference for selecting decoders and training schemes in chronic iBCI. Our result suggests that comparing to KF and OLE, RNN has better or equivalent performance using both training schemes. Performance of decoders under static scheme is influenced by recording degradation and neuronal property variation while decoders under retrained scheme are only influenced by the former one.
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Affiliation(s)
- Zijun Wan
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Tengjun Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Xingchen Ran
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Pengfu Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Weidong Chen
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
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Albahri AS, Al-qaysi ZT, Alzubaidi L, Alnoor A, Albahri OS, Alamoodi AH, Bakar AA. A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology. Int J Telemed Appl 2023; 2023:7741735. [PMID: 37168809 PMCID: PMC10164869 DOI: 10.1155/2023/7741735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/01/2023] [Accepted: 03/16/2023] [Indexed: 05/13/2023] Open
Abstract
The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
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Affiliation(s)
- A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Z. T. Al-qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | | | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
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