检索结果-内蒙古大学图书馆

15th IEEE International Workshop on Advanced Motion Control (AMC)

作者： Huve, Gauvain Takahashi, Kazuhiko Hashimoto, Masafumi Doshisha Univ Kyoto Japan

ISBN: (纸本)9781538619469

Functional near-infrared spectroscopic (fNIRS) systems have recently attracted considerable attention for their potential in the domain of brain-computer interfaces (BCIs). This study presents a method for brain activity classification using signals obtained through fNIRS and suggests strategies to optimize the classifier for real-time control applications. A deep neural network (DNN) classifies differing brain activity signals from the pre-frontal cortex that were generated by pre-defined activities. Optimization of the DNN showed that varying the number of neurons per layer does not adversely affect the classification accuracy past a certain size and using a dropout method during training further improves the classification accuracy of the DNN. In the offline classification trials, the DNN achieved an accuracy of 82% for the two-class (activity vs rest) classification. A control system for a mobile robot is conceived to explore the practical application of BCIs. The components of the input vector to the DNN were altered and a post-processing step was added to the output of the DNN to use an fNIRS-based BCI for real-time data classification. Trials with online data classification depicted the plausibility of using DNNs for real-time control with fNIRS-based BCIs;however, the maximum classification accuracy of the system is 66%, which renders it impractical for real-time application.

关键词： brain-computer interface fNIRS Deep Neural Network Robot Control

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface Based on Magnetic Particle Imaging For Diagnostic and Neurological Rehabilitation in Multiple Sclerosis 8

Brain-Computer Interface Based on Magnetic Particle Imaging ...

引用

8th International Winter Conference on brain-computer interface (BCI)

作者： Miller, Tiffany C. USF Coll Engn Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9781728147079

Applications of brain-computer interface (BCI) based on data of magnetic particle image (MPI) scanning of biological effector bound in-vivo magnetic nanoparticles (MNs) can be a desirable method of providing noninvasive diagnostic and neurological rehabilitative solutions for patients having various neurological dysfunctions such as, Multiple Sclerosis (MS). MNs may be dispensed to a patient for noninvasive localized monitoring of neurodegenerative disease by measuring changes in action potential along a neuron or by measuring levels of inflammation by determining concentrations of cellular debris at a particular point of interest in the brain such as at impaired nerve fibers of a neuron. The MNs may be configured for bidirectional communication with an electronic device in electrical communication with a MPI machine. Further, these MNs may be configured for drug delivery to a predetermined location associated with brain inflammation or to actuate the pharmacokinetic expulsion of toxic cellular debris.

关键词： neurological dysfunction Magnetic Particle Image (MPI) nanoparticles axonal injury Multiple Sclerosis (MS) pharmacokinetic process brain-computer interface

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface: Feature Extraction and Classification of Motor Imagery-Based Cognitive Tasks

Brain-Computer Interface: Feature Extraction and Classificat...

引用

2022 International Conference on Automatic Control and Intelligent Systems

作者： Nisar, Humaira Boon, Kee Wee Ho, Yeap Kim Khang, Teoh Shen Univ Tunku Abdul Rahman Fac Engn & Green Technol Kampar 31900 Malaysia Univ Tunku Abdul Rahman Fac Informat & Commun Technol Kampar 31900 Malaysia

ISBN: (纸本)9781665495813

Decoding motor imagery (MI) signals accurately is important for brain-computer interface (BCI) systems for healthcare applications. Electroencephalography (EEG) decoding is a challenging task because of its complexity, and dynamic nature. By improving EEG signal classification, the performance of MI-based BCI can be enhanced. In this paper, five features (Band Power (BP), Approximate Entropy (ApEn), statistical features, wavelet-based features, and Common Spatial Pattern (CSP)) are extracted from EEG signals. For classification, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN) are used. These methods are tested on a publicly available Physionet motor imagery database. The EEG signals are recorded from 64 channels for 50 subjects, while the subject is performing four different MI tasks. The proposed method achieved an accuracy of 98.53% for left and right hands MI tasks with ApEn feature (overlapping ratio similar to 0.8) and SVM classifier. Hence the proposed method shows better results than several EEG MI classification methods proposed in the literature.

关键词： Electroencephalography Motor imagery brain-computer interface Entropy Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface Training of mu EEG Rhythms in Intellectually Impaired Children with Autism: A Feasibility Case Series

引用

APPLIED PSYCHOPHYSIOLOGY AND BIOFEEDBACK 2023年第2期48卷 229-245页

作者： LaMarca, Kristen Gevirtz, R. Lincoln, Alan J. Pineda, Jaime A. Alliant Univ Dept Clin Psychol Calif Sch Profess Psychol San Diego CA 92131 USA Univ Calif San Diego Dept Cognit Neurosci San Diego CA USA

Prior studies show that neurofeedback training (NFT) of mu rhythms improves behavior and EEG mu rhythm suppression during action observation in children with autism spectrum disorder (ASD). However, intellectually impaired persons were excluded because of their behavioral challenges. We aimed to determine if intellectually impaired children with ASD, who were behaviorally prepared to take part in a mu-NFT study using conditioned auditory reinforcers, would show improvements in symptoms and mu suppression following mu-NFT. Seven children with ASD (ages 6-8;mean IQ 70.6 +/- 7.5) successfully took part in mu-NFT. Four cases demonstrated positive learning trends (hit rates) during mu-NFT (learners), and three cases did not (non-learners). Artifact-creating behaviors were present during tests of mu suppression for all cases, but were more frequent in non-learners. Following NFT, learners showed behavioral improvements and were more likely to show evidence of a short-term increase in mu suppression relative to non-learners who showed little to no EEG or behavior improvements. Results support mu-NFT's application in some children who otherwise may not have been able to take part without enhanced behavioral preparations. Children who have more limitations in demonstrating learning during NFT, or in providing data with relatively low artifact during task-dependent EEG tests, may have less chance of benefiting from mu-NFT. Improving the identification of ideal mu-NFT candidates, mu-NFT learning rates, source analyses, EEG outcome task performance, population-specific artifact-rejection methods, and the theoretical bases of NFT protocols, could aid future BCI-based, neurorehabilitation efforts.

关键词： Autism Intellectual disability brain-computer interface Neurofeedback Mu rhythms Mirror neurons

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface Design Based on Slow Cortical Potentials using Matlab/Simulink

Brain-Computer Interface Design Based on Slow Cortical Poten...

引用

IEEE International Conference on Mechatronics and Automation

作者： Zhao, Hai-bin Wang, Hong Li, Chun-sheng Li, Yun-gong Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Prov Peoples R China

ISBN: (纸本)9781424426928

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other electronic devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. The feature selection and classification methods of EEC signals are two key points of the BCI system. In this paper, we designed a simulation model based on slow cortical potentials (SCPs) using Matlab/Simulink. The simulation model mainly had three parts: preprocessing, feature selection, and classification. We obtained significant improvement on classification accuracy for data set la of BCI competition 2003, which was a typical BCI data. The classification accuracy rates were 70.90% (190/268) and 89.76% (263/293) for train and test data sets respectively. The result also showed that training could improve the classification accuracy since the test data were taken later in the recording sessions than the training data.

关键词： brain-computer interface Slow Cortical Potentials Linear Discriminant Analysis Simulation Model

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface Through the Prism of Modern Age 16th

Brain-Computer Interface Through the Prism of Modern Age

引用

Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH)

作者： Radoncic, Amina Hadzic, Semina Lakovic, Jasmina Int Burch Univ Fac Engn & Nat Sci Dept Genet & Bioengn Sarajevo Bosnia & Herceg

ISBN: (纸本)9783031490675;9783031490682

brain-computer interface is on the rise within different areas of medical diagnostics and rehabilitation. BCI devices are currently in clinical use for neurodegenerative disorder treatments, mental health treatments, and cognitive and behavioral rehabilitation. Gaming and marketing universes have also grabbed their share of novel technologies to take both virtual reality and advertisement strategies to another level. The future of BCI technology will eventually yield to the needs in areas of medicine, gaming, and marketing, and find its way toward our homes. As engineers and scientists work hard to make these devices affordable for all, we can say that BCI could soon fulfill all the needs of modern rehabilitation and influence healthy individuals' lives. Interpretation of BCI devices may vary in certain ways, as the need for a modern approach in rehabilitation. Whether it is virtual reality or cognitive behavior therapy, BCI technology is surely making its way through the golden age of scientific innovations.

关键词： brain-computer interface Neurostimulation Signal acquisition Diagnostics Neurocrime

来源：评论

学校读者我要写书评

暂无评论

Bioelectronic nose for ultratrace odor detection via brain–computer interface with olfactory bulb electrode arrays

引用

Biosensors and Bioelectronics 2025年 285卷 117585页

作者： Qian Lu Ming Yi Junjie Jiang The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University No.28 West Xianning Road Xi'an 710049 Shaanxi PR China Neuroscience Research Institute and Department of Neurobiology School of Basic Medical Sciences Peking University NO.38 Xueyuan Road Beijing 100083 PR China

Rapid and accurate detection of hazardous volatile compounds is crucial for public health and environmental safety. While conventional methods, including electronic noses, typically exhibit detection thresholds in the parts-per-million (ppm) range, many harmful substances pose risks at parts-per-billion (ppb) concentrations or lower. To address this challenge, we leverage the exceptional sensitivity of the mammalian olfactory system, specifically that of Rattus norvegicus (lab rat), which has evolved to detect and discriminate a vast array of odors at extremely low concentrations. In this study, we developed a novel bio-hybrid system that integrates behavioral training with in vivo electrophysiological recordings from the olfactory bulb (OB). Rats were operantly conditioned to recognize target odors, namely TNT (2,4,6-trinitrotoluene), TNP (2,4,6-trinitrophenol), and chlorine gas ( Cl 2 ), at ppb levels. Concurrent with behavioral testing, we recorded neural activity from both the dorsal and ventral OB using a customdesigned, multi-channel electrode array optimized for the rat OB's cytoarchitecture. Electrophysiological data were decoded using a Support Vector Machine algorithm, achieving a mean accuracy of over 90 % in classifying odor identity at ppb concentrations based on OB activity patterns. These results demonstrate the feasibility of utilizing a brain-computer interface with the olfactory system to achieve ultratrace detection of hazardous substances. This bio-hybrid approach offers significantly enhanced sensitivity compared to existing electronic nose technologies, paving the way for highly effective environmental and biomedical sensing applications.

关键词： brain-computer interface Cl(2) Odor detection Olfactory bulb electrode arrays ppb

来源：评论

学校读者我要写书评

暂无评论

Remote Navigation of Turtle by Controlling Instinct Behavior via Human brain-computer interface

引用

Journal of Bionic Engineering 2016年第3期13卷 491-503页

作者： Cheol-Hu Kim Bongjae Choi Dae-Gun Kim Serin Lee Sungho Jo Phill-Seung Lee Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) 373-1 Guseong-dong Yuseong-gu Daejeon 34141 Republic of Korea School of Computing Science Korea Advanced Institute of Science and Technology (KAIST) 373-1 Guseong-dong Yuseong-gu Daejeon 34141 Republic of Korea Instituteforlnfocomm Research 1 Fusionopolis Way #21-01 Connexis (South Tower) 138632 Singapore

brain-computer interface （BCI） techniques have advanced to a level where it is now eliminating the need lor hand-based activation. This paper presents a novel attempt to remotely control an animal＇s behavior by human BCI using a hybrid of Event Related Desynchronization （ERD） and Steady-State Visually Evoked Potential （SSVEP） BCI protocols. The turtle was chosen as the target animal, and we developed a head-mounted display, wireless communication, and a specially designed stimulation device for the turtle. These devices could evoke the turtle＇s instinctive escape behavior to guide its moving path, and turtles were remotely controlled in both indoor and outdoor environments. The system architecture and design were presented. To demon- strate the feasibility of the system, experimental tests were performed under various conditions. Our system could act as a framework for future human-animal interaction systems.

关键词： brain-computer interface turtle (Trachemys scripta elegans) remote navigation instinct behaviour escape be- havior

来源：评论

学校读者我要写书评

暂无评论

Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain-computer interface

引用

JOURNAL OF NEURAL ENGINEERING 2021年第4期18卷

作者： Liu, Chang Jin, Jing Xu, Ren Li, Shurui Zuo, Cili Sun, Hao Wang, Xingyu Cichocki, Andrzej East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Guger Technol OG Herbersteinstr 60 A-8020 Graz Austria Skolkovo Inst Sci & Technol Skoltech Moscow 121205 Russia Nicolaus Copernicus Univ UMK Dept Appl Comp Sci PL-87100 Torun Poland

Objective. Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain-computer interfaces (BCIs) in this study. Approach. The first step of the DSSFLNN framework was to extract FBCSP features from raw EEG signals. Then two squeeze-and-excitation modules were used to re-calibrate CSP features along the band-wise axis and the class-wise axis, respectively. Next, we used a parallel convolutional neural network module to learn distinguishable spatial-spectral features. Finally, the distinguishable spatial-spectral features were fed to a fully connected layer for classification. To verify the effectiveness of the proposed framework, we compared it with the state-of-the-art methods on BCI competition IV datasets 2a and 2b. Main results. The results showed that the DSSFLNN framework can achieve a mean Cohen's kappa value of 0.7 on two datasets, which outperformed the state-of-the-art methods. Moreover, two additional experiments were conducted and they proved that the combination of band-wise feature learning and class-wise feature learning can achieve significantly better performance than only using either one of them. Significance. The proposed DSSFLNN can effectively improve the decoding performance of MI-based BCIs.

关键词： brain-computer interface motor imagery convolutional neural network squeeze-and-excitation common spatial pattern

来源：评论

学校读者我要写书评

暂无评论

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第5期63卷 1367-1381页

作者： Fu, Rongrong Niu, Shaoxiong Feng, Xiaolei Shi, Ye Jia, Chengcheng Zhao, Jing Wen, Guilin Yanshan Univ Dept Elect Engn Measurement Technol & Instrumentat Key Lab Hebei P Qinhuangdao Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao Peoples R China Yanshan Univ Key Lab Intelligent Rehabil & Neromodulat Hebei Pr Qinhuangdao Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Yanshan Univ Sch Mech Engn Qinhuangdao Peoples R China

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.

关键词： Steady-state visual evoked potential brain-computer interface Robotic arm Multivariate variational mode decomposition Multivariate synchronization index

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：