检索结果-内蒙古大学图书馆

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Quattrociocchi, Ilaria Caracci, Valentina Riccio, Angela Galiotta, Valentina D'Ippolito, Mariagrazia Cincotti, Febo Toppi, Jlenia Astolfi, Laura Sapienza University of Rome Department of Computer Control and Management Engineering Italy Fondazione Santa Lucia Irccs Neuroelectrical Imaging and Brain Computer Interface Laboratory Rome Italy

ISBN: (纸本)9798350371499

Event-Related Potentials (ERPs) are modifications of the brain activity in response to external sensory stimulation. P300 is a positive ERP component that occurs 300 ms after the presentation of a rare stimulus and indicates conscious perception of an unexpected change in sensory stimulation. Since the amplitude of ERPs is comparable to that of background electroencephalography, the most widespread approach to render ERPs correctly detectable is time-domain averaging. ERPs are physiologically affected by variations in latency, referred to as latency jitter. The most promising techniques to address latency jitter are cross-correlation template matching algorithms, which estimate single-trial latencies and resynchronize them according to a template that best describes the ERP component. Adaptive Wavelet Filtering (AWF) is an algorithm applied in the wavelet domain to provide the optimal compromise of time-frequency resolution in detecting ERPs. The choice of an appropriate decomposition frequency is crucial in the application of AWF, as it is essential for identifying the correct template and estimating single-trial latencies. In this study, we investigated the influence of frequency on the performance of AWF to optimize it. The algorithm was tested on simulated data with varying latency shifts and signal-to-noise ratios as well as for data recorded from 11 healthy subjects during an auditory oddball paradigm. Results on both simulated and real EEG data showed how frequency decomposition of 3 Hz is the most appropriate for P300 waveform detection. © 2024 IEEE.

关键词： Template matching

来源：评论

学校读者我要写书评

暂无评论

Decomposition frequency optimization in wavelet-based template matching algorithms to manage P300 latency jitter

Decomposition frequency optimization in wavelet-based templa...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ilaria Quattrociocchi Valentina Caracci Angela Riccio Valentina Galiotta Mariagrazia D’Ippolito Febo Cincotti Jlenia Toppi Laura Astolfi Department of Computer Control and Management Engineering Sapienza University of Rome Italy Neuroelectrical Imaging and Brain Computer Interface Laboratory Fondazione Santa Lucia IRCCS Rome Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

关键词： Time-frequency analysis Wavelet domain Jitter Electroencephalography Physiology Frequency estimation Time-domain analysis Signal resolution Optimization Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

A discussion of statistical criteria for assessing awareness...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Amaunam, Idorenyin Schneider, Christoph Da Silva, Marina Lopes Jöhr, Jane Diserens, Karin Perdikis, Serafeim Brain-Computer Interface and Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Department of Radiology BostonMA United States Acute Neurorehabilitation Unit Division of Neurology Centre Hospitalier Universitaire Vaudois Department of Clinical Neurosciences Lausanne Switzerland

ISBN: (纸本)9798350337020

This work discusses the implications of selecting particular statistical metrics and thresholds as criteria to diagnose awareness through brain-computer interface (BCI) technology in patients with Disorders of Consciousness (DOC). We report a first analysis of a novel dataset collected to investigate whether a motor attempt electroencephalography (EEG) paradigm coupled with Functional Electrical Stimulation (FES) can detect command following and, therefore, signs of conscious awareness in DOC. We assessed 22 DOC patients admitted to the acute rehabilitation unit after a brain lesion over one or more sessions. We extracted EEG sensorimotor rhythms and performed a standard open-loop BCI pipeline evaluation, classifying motor attempt against resting-state trials. We validate this approach by correlating classification accuracy with the established clinical scale Coma Recovery Scale Revised. We employ a machine learning (ML)-inspired diagnostic criterion based on confidence intervals over chance-level classification accuracy and show that it yields more conservative and, arguably, reliable inference of Cognitive Motor Dissociation (CMD) by means of command-following, neuroimaging-based tools, compared to diagnoses based on clinical assessments or criteria examining the statistical significance of brain features across different mental states. © 2023 IEEE.

关键词： awareness brain-computer interface chance-level disorders of consciousness motor attempt

来源：评论

学校读者我要写书评

暂无评论

A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

A discussion of statistical criteria for assessing awareness...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Idorenyin Amaunam Christoph Schneider Marina Lopes da Silva Jane Jöhr Karin Diserens Serafeim Perdikis Brain-Computer Interface and Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Department of Radiology Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Clinical Neurosciences Acute Neurorehabilitation Unit Division of Neurology Centre Hospitalier Universitaire Vaudois Lausanne Switzerland

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction: On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study

引用

Journal of neuroengineering and rehabilitation 2025年第1期22卷 46页

作者： Francesca Patarini Federica Tamburella Floriana Pichiorri Shiva Mohebban Alessandra Bigioni Andrea Ranieri Francesco Di Tommaso Nevio Luigi Tagliamonte Giada Serratore Matteo Lorusso Angela Ciaramidaro Febo Cincotti Giorgio Scivoletto Donatella Mattia Jlenia Toppi Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Rome Italy. Neuroelectrical Imaging and Brain Computer Interface Lab IRCCS Fondazione Santa Lucia Rome Italy. Department of Life Sciences Health and Health Professions Link Campus University Rome Rome Italy. Laboratory of Robotic Neurorehabilitation IRCCS Fondazione Santa Lucia Rome Italy. Università Campus Bio-Medico di Roma Rome Italy. Department of Biomedical Metabolic and Neural Sciences University of Modena and Reggio Emilia Reggio Emilia Italy. Center of Neuroscience and Neurotechnology University of Modena and Reggio Emilia Modena Italy. Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Rome Italy. jlenia.toppi@uniroma1.it. Neuroelectrical Imaging and Brain Computer Interface Lab IRCCS Fondazione Santa Lucia Rome Italy. jlenia.toppi@uniroma1.it.

来源：评论

学校读者我要写书评

暂无评论

Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study

引用

Translational psychiatry 2025年第1期15卷 188页

作者： Jianyue Pang Jiaying Xu Long Chen Huimin Teng Chenxiao Su Zhiyuan Zhang Lingyun Gao Rui Zhang Gang Liu Yuan Chen Jin He Yajing Pang Hengfen Li Department of Psychiatry The First Affiliated Hospital of Zhengzhou University Zhengzhou China. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou China. Department of Neurosurgery Zhengzhou Central Hospital Affiliated to Zhengzhou University Zhengzhou China. Department of Magnetic Resonance Imaging The First Affiliated Hospital of Zhengzhou University Zhengzhou China. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou China. yajingpang@***. Department of Psychiatry The First Affiliated Hospital of Zhengzhou University Zhengzhou China. fcclihf2@***.

A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke

引用

Nature communications 2025年第1期16卷 5132页

作者： Xiao He Jiadong Chen Yan Zhong Peili Cen Li Shen Fei Huang Jing Wang Chentao Jin Rui Zhou Xiaohui Zhang Anxin Wang Jing Fan Shuang Wu Mengjiao Tu Xiyi Qin Xiaoyun Luo Yu Zhou Jieqiao Peng Youyou Zhou A Cahid Civelek Mei Tian Hong Zhang Department of Nuclear Medicine Zhejiang Cancer Hospital Hangzhou Institute of Medicine Chinese Academy of Sciences Hangzhou 310022 China. Department of Radiology Affiliated Hangzhou First People's Hospital Westlake University School of Medicine Hangzhou 310006 China. Department of Nuclear Medicine and PET Center The Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou 310009 Zhejiang China. Key Laboratory of Medical Molecular Imaging of Zhejiang Province Hangzhou 310009 China. Department of Neurobiology Departments of Neurosurgery and Neurology of the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310058 China. Nanhu Brain-computer Interface Institute Hangzhou 311100 China. NHC and CAMS Key Laboratory of Medical Neurobiology MOE Frontier Science Center for Brain Research and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310058 China. The MOE Key Laboratory of Biosystems Homeostasis & Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology Life Sciences Institute Zhejiang University Hangzhou Zhejiang 310009 China. Hopstem Bioengineering Co. Ltd. Hangzhou 310009 China. Department of Radiology and Radiological Science Johns Hopkins Medicine Baltimore MD 21287 USA. National Institutes of Health Clinical Center Radiology and Imaging Sciences Bethesda Maryland USA. Department of Nuclear Medicine and PET Center The Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou 310009 Zhejiang China. tianmei@***. Key Laboratory of Medical Molecular Imaging of Zhejiang Province Hangzhou 310009 China. tianmei@***. Department of Nuclear Medicine and PET Center Huashan Hospital and Human Phenome Institute Fudan University Shanghai 200235 China. tianmei@***. Department of Nuclear Medicine Zhejiang Cancer Hospital Hangzhou Institute of Medicine Chinese Academy of Sciences Hangzhou 310022 China. hzhang21@zju.edu

Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.

关键词：

来源：评论

学校读者我要写书评

暂无评论

What does clean EEG look like?

What does clean EEG look like?

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ian Daly Floriana Pichiorri Josef Faller Vera Kaiser Alex Kreilinger Reinhold Scherer Gernot Müller-Putz Institute for Knowledge Discovery Laboratory of Brain-Computer Interfaces Institute for Knowledge Discovery Graz University of Technology Krenngasse 37/IV 8010 Graz Austria Clinical Neurophysiology Neuroelectrical Imaging and BCI Lab Fondazione Santa Lucia IRCCS Via Ardeatina 306 00179 Rome Italy

ISBN: (纸本)9781424441198

Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).

关键词： analytical metric identifying artifact healthy subjects

来源：评论

学校读者我要写书评

暂无评论

Vibrotactile feedback for brain-computer interface operation

引用

Computational Intelligence and Neuroscience 2007年第unknown期2007卷 1-12页

作者： Cincotti, Febo Kauhanen, Laura Aloise, Fabio Palomäki, Tapio Caporusso, Nicholas Jylänki, Pasi Mattia, Donatella Babiloni, Fabio Vanacker, Gerolf Nuttin, Marnix Marciani, Maria Grazia Millán, José Del R. Laboratory of Neuroelectrical Imaging and Brain Computer Interface Fondazione Santa Lucia IRCCS Roma 00179 Via Ardeatina 306 Italy Laboratory of Computational Engineering Helsinki University of Technology TKK 02015 P.O. Box 9203 Finland Department of Computer Science University of Bari Bari 70125 Via Orabona 4 Italy Department of Mechanical Engineering Katholieke Universiteit Leuven Leuven 3000 Celestijnenlaan 300 B Belgium Dipartimento di Fisiologia Umana e Farmacologia Universitá Degli Studi di Roma la Sapienza Roma 00185 Piazzale Aldo Moro 5 Italy Dipartimento di Neuroscienze Universitá Degli Studi di Roma Tor Vergata Roma 00173 Via Montpellier 1 Italy IDIAP Research Institute Martigny 1920 Switzerland Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms using tactile feedback;(II) the compatibility of this form of feedback in presence of a visual distracter;(III) the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The stimulation protocol we developed supports a general usage of the tactors;preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.

关键词： .visual feedback spinal cord visual channel brain-computer interfaces

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：