2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献_第7页

Dipole Cancellation as an Artifact Suppression Technique in Simultaneous Electrocorticography Stimulation and Recording 偶极子对消技术在皮质电图同时刺激和记录中的伪影抑制

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8716961

Jeffrey Lim, Po T. Wang, Haoran Pu, C. Liu, S. Kellis, R. Andersen, P. Heydari, An H. Do, Z. Nenadic

引用次数: 5

Comparing Two Different Cursor Control Methods which Use Single-Site Surface Electromyography* 比较两种使用单点表面肌电图的光标控制方法*

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8716903

Sarah M. O'Meara, Megan C. Shyr, Kenneth R. Lyons, S. Joshi

{"title":"Comparing Two Different Cursor Control Methods which Use Single-Site Surface Electromyography*","authors":"Sarah M. O'Meara, Megan C. Shyr, Kenneth R. Lyons, S. Joshi","doi":"10.1109/NER.2019.8716903","DOIUrl":"https://doi.org/10.1109/NER.2019.8716903","url":null,"abstract":"Electromyography (EMG) can be used as an input signal for Human-Computer Interfaces, which are used in the disabled community. Typically, both the user-condition and the end-goal application dictate the number and locations of EMG sensors. We developed two cursor control methods intended for high-level spinal cord injury patients, where available muscle sites are limited to the head and neck areas. For patient comfort, it is desirable to minimize the number of sensors and their intrusiveness while maximizing the functionality of the control method. Both our control methods use a single, noninvasive, surface, differential EMG sensor at the temporalis muscle that controls a cursor in two degrees of freedom (DOF). Fourteen inexperienced able-bodied subjects completed a Fitts’s law task-based cursor-to-target paradigm using both control methods (\"auto-rotate\" and \"manual rotate\"). Subjects evaluated and compared performance between the two control methods, which both enabled the cursor to move in 2-DOF. They differed in the level of user control, where one allowed direction manipulation in 1-DOF and 2-DOF in the other. Subjects also completed pre- and post-session surveys and the National Aeronautics and Space Administration Task Load Index for workload assessment. In general, subjects’ performance improved with subsequent sessions within each control method. Subjects achieved a higher throughput (better performance) in the auto-rotate method, had lower workload scores, and tended to prefer this control method. However, about half the subjects felt the manual rotate method allowed them more control over cursor behavior. Our results suggest that a viable cursor control method can be achieved with only a single muscle site.","PeriodicalId":356177,"journal":{"name":"2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126421682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Simulation Studies of Neuronal Modulation Using Magneto-electric Nanoparticles for Astrocyte Stimulation 磁电纳米粒子对星形胶质细胞刺激神经元调制的模拟研究

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8716935

K. Yue, Rebecca K. Lee, A. C. Parker

引用次数: 0

Dynamic Modeling and Classification of Epileptic EEG Data 癫痫脑电图数据的动态建模与分类

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8717126

Xiaomu Song, L. Aguilar, Angela Herb, Suk-Chung Yoon

{"title":"Dynamic Modeling and Classification of Epileptic EEG Data","authors":"Xiaomu Song, L. Aguilar, Angela Herb, Suk-Chung Yoon","doi":"10.1109/NER.2019.8717126","DOIUrl":"https://doi.org/10.1109/NER.2019.8717126","url":null,"abstract":"Brain functional connectivity has been used to investigate the interaction between brain regions. It provides important information related to brain diseases, injuries, and high level cognitive functions. Statistical methods have been widely used to model brain functional connectivity based upon which insights of brain function are expected to be revealed. Most statistical approaches were developed based upon an assumption that connectivity patterns are static during the recording. This is not true because the connectivity changes over time. A dynamical modeling of connectivity patterns allows to characterize these variations. In this work, a simplified dynamic Bayesian modeling approach, parallel Hidden Markov Model (PaHMM), was investigated by characterizing temporal variations of cortical functional connectivity patterns computed using epileptic electroencephalogram (EEG) data. The performance of the PaHMM was evaluated based on an experimental study of epilepsy detection and classification, where multisubject epileptic EEG data from Temple University Hospital EEG Data Corpus were used. Experimental results show that an accuracy of 93.5% was obtained for the epilepsy detection, and an overall accuracy above 81% was achieved for the seizure type classification. This indicates that the method can efficiently capture temporal variations of functional connectivity patterns, and is potentially applicable in clinical settings to detect epilepsy and differentiate seizure types.","PeriodicalId":356177,"journal":{"name":"2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130666502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation eConHand:一种用于中风康复的可穿戴脑机接口系统

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8716940

Z. Qin, Yao Xu, Xiaokang Shu, Lei Hua, X. Sheng, Xiangyang Zhu

引用次数: 8

NAV-VIR: an audio-tactile virtual environment to assist visually impaired people* NAV-VIR:一个听觉触觉虚拟环境，以帮助视障人士*

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8717086

Marc-Aurèle Rivière, S. L. Gay, Katerine Romeo, E. Pissaloux, M. Bujacz, P. Skulimowski, P. Strumiłło

引用次数: 1

Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages 使用深度卷积自编码器神经网络对脑电图进行睡眠阶段聚类的自动特征学习

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8716996

Kedar S. Prabhudesai, L. Collins, B. Mainsah

{"title":"Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages","authors":"Kedar S. Prabhudesai, L. Collins, B. Mainsah","doi":"10.1109/NER.2019.8716996","DOIUrl":"https://doi.org/10.1109/NER.2019.8716996","url":null,"abstract":"Deep neural networks have emerged as popular machine learning tools due to their ability to automatically learn feature representations from raw input data. An auto-encoder neural network is a special network that can be trained in an unsupervised manner for automated feature learning. Unsupervised analysis of EEG signals is highly desirable since supervised analysis requires manual labeling of EEG signals which can be labor intensive and time consuming given the large amount of EEG data collected. We present a deep convolutional auto-encoder neural network to automatically learn feature representations from raw EEG signals in an unsupervised manner. We use the features extracted from the auto-encoder neural network for clustering EEG signals into sleep stages. For clustering, we test two algorithms: K-means – which is a single-membership model, and the latent Dirichlet allocation (LDA) topic model – which is a mixed membership model. Results are presented demonstrating an improvement in clustering performance using auto-encoder features compared to standard manually extracted features.","PeriodicalId":356177,"journal":{"name":"2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Switching Delay Analysis for Two Neuronal Toggle Switch Designs: Direct and Staged Mutual Inhibition 两种神经元拨动开关设计的切换延迟分析:直接和阶段相互抑制

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8717123

Farimah Mapar, Ron Weiss

引用次数: 0

Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation 重度瘫痪慢性脑卒中患者上肢运动康复后肌肉疲劳的肌电图指标

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8717165

Andreas M. Ray, Aurélien Maillot, F. Helmhold, W. Mahmoud, E. López-Larraz, A. Ramos-Murguialday

引用次数: 1

High frequency shift in Carotid Sinus Nerve and Sympathetic Nerve activity in Type 2 Diabetic Rat Model* 2型糖尿病大鼠颈动脉窦神经和交感神经活动的高频移位

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2019-03-20 DOI: 10.1109/NER.2019.8717052

Marina Cracchiolo, J. Sacramento, A. Mazzoni, A. Panarese, J. Carpaneto, S. Conde, S. Micera

{"title":"High frequency shift in Carotid Sinus Nerve and Sympathetic Nerve activity in Type 2 Diabetic Rat Model*","authors":"Marina Cracchiolo, J. Sacramento, A. Mazzoni, A. Panarese, J. Carpaneto, S. Conde, S. Micera","doi":"10.1109/NER.2019.8717052","DOIUrl":"https://doi.org/10.1109/NER.2019.8717052","url":null,"abstract":"Overactivity of the sympathetic nervous system (SNS) is associated to several cardiovascular and metabolic dysfunctions, such as hypertension and insulin resistance. Indirect biochemical measurements and surgical manipulations have provided preliminary evidences about a crucial role of the Carotid Sinus Nerve (CSN) in generating the SNS overactivity. However, CSN and SNS neural activities and their interplay have not been yet characterized in healthy and pathological conditions. Understanding this relationship is key for the development of electroceutical approaches to deliver therapeutic neuromodulation to the autonomic nervous system and restore insulin sensitivity. Here we show that early type 2 diabetes rats present a high frequency shift in both CSN and SNS neural activities with respect to control animals. This feature could be an important neural signature characterizing type 2 diabetes. Moreover, we show that CSN resection in early type 2 diabetes rats abolishes SNS high frequency shift confirming that normal SNS activity and insulin sensitivity may be recovered by CSN activity suppression. These findings shed new light on the pathological neural changes within the autonomic nervous system in type 2 diabetes. Moreover, they pave the way for electrical monitoring of the metabolic state of diabetic patients, a key first step for the development of electroceutical therapies.","PeriodicalId":356177,"journal":{"name":"2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5