Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics最新文献_第2页

Single-Trial Classification of Disfluent Brain States in Adults Who Stutter. 成人口吃患者不流利脑状态的单试验分类。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2018-10-01 Epub Date: 2019-01-17 DOI: 10.1109/smc.2018.00019

John C Myers, Farzan Irani, Edward J Golob, Jeffrey R Mock, Kay A Robbins

{"title":"Single-Trial Classification of Disfluent Brain States in Adults Who Stutter.","authors":"John C Myers, Farzan Irani, Edward J Golob, Jeffrey R Mock, Kay A Robbins","doi":"10.1109/smc.2018.00019","DOIUrl":"https://doi.org/10.1109/smc.2018.00019","url":null,"abstract":"Normal human speech requires precise coordination between motor planning and sensory processing. Speech disfluencies are common when children learn to talk, but usually abate with time. About 5% of children experience stuttering. For most, this resolves within a year. However, for approximately 1% of the world population, stuttering continues into adulthood, which is termed 'persistent developmental stuttering'. Most stuttering events occur at the beginning of an utterance. So, in principle, brain activity before speaking should differ between fluent and stuttered speech. Here we present a method for classifying brain network states associated with fluent vs. stuttered speech on a single trial basis. Brain activity was recorded with EEG before people who stutter read aloud pseudo-word pairs. Offline independent component analysis (ICA) was used to identify the independent neural sources that underlie speech preparation. A time window selection algorithm extracted spectral power and coherence data from salient windows specific to each neural source. A stepwise linear discriminant analysis (sLDA) algorithm predicted fluent vs. stuttered speech for 81% of trials in two subjects. These results support the feasibility of developing a brain-computer interface (BCI) system to detect stuttering before it occurs, with potential for therapeutic application.","PeriodicalId":72691,"journal":{"name":"Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/smc.2018.00019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39578785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images. 大脑活动条件下的生成对抗网络重建已见图像。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2018-10-01 DOI: 10.1109/SMC.2018.00187

Ghislain St-Yves, Thomas Naselaris

{"title":"Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images.","authors":"Ghislain St-Yves, Thomas Naselaris","doi":"10.1109/SMC.2018.00187","DOIUrl":"https://doi.org/10.1109/SMC.2018.00187","url":null,"abstract":"We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus, reconstructions of complex images from brain activity require a strong prior. Here we propose to train generative adversarial networks (GANs) to learn a generative model of images that is conditioned on measurements of brain activity. We consider two challenges of this approach: First, given that GANs require far more data to train than is typically collected in an fMRI experiment, how do we obtain enough samples to train a GAN that is conditioned on brain activity? Secondly, how do we ensure that our generated samples are robust against noise present in fMRI data? Our strategy to surmount both of these problems centers around the creation of surrogate brain activity samples that are generated by an encoding model. We find that the generative model thus trained generalizes to real fRMI data measured during perception of images and is able to reconstruct the basic outline of the stimuli.","PeriodicalId":72691,"journal":{"name":"Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics","volume":"2018 ","pages":"1054-1061"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/SMC.2018.00187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9751044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving malignancy prediction through feature selection informed by nodule size ranges in NLST. 通过NLST中结节大小范围的特征选择改进恶性肿瘤预测。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2016-10-01 Epub Date: 2017-02-09 DOI: 10.1109/SMC.2016.7844523

Dmitry Cherezov, Samuel Hawkins, Dmitry Goldgof, Lawrence Hall, Yoganand Balagurunathan, Robert J Gillies, Matthew B Schabath

{"title":"Improving malignancy prediction through feature selection informed by nodule size ranges in NLST.","authors":"Dmitry Cherezov, Samuel Hawkins, Dmitry Goldgof, Lawrence Hall, Yoganand Balagurunathan, Robert J Gillies, Matthew B Schabath","doi":"10.1109/SMC.2016.7844523","DOIUrl":"10.1109/SMC.2016.7844523","url":null,"abstract":"Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected by feature selectors, for making a decision. In this paper, we investigate automated selection of different image features informed by different nodule size ranges to increase the overall accuracy of the classification. The NLST dataset is one of the largest available datasets on CT screening for NSCLC. We used 261 cases as a training dataset and 237 cases as a test dataset. The nodule size, which may indicate biological variability, can vary substantially. For example, in the training set, there are nodules with a diameter of a couple millimeters up to a couple dozen millimeters. The premise is that benign and malignant nodules have different radiomic quantitative descriptors related to size. After splitting training and testing datasets into three subsets based on the longest nodule diameter (LD) parameter accuracy was improved from 74.68% to 81.01% and the AUC improved from 0.69 to 0.79. We show that if AUC is the main factor in choosing parameters then accuracy improved from 72.57% to 77.5% and AUC improved from 0.78 to 0.82. Additionally, we show the impact of an oversampling technique for the minority cancer class. In some particular cases from 0.82 to 0.87.","PeriodicalId":72691,"journal":{"name":"Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics","volume":"2016 ","pages":"001939-1944"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251413/pdf/nihms-994650.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36704081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems. 识别用于评估和量化脑机接口(BMI)系统性能的工程、临床和患者指标。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2014-10-05 DOI: 10.1109/SMC.2014.6974126

Jose L Contreras-Vidal

引用次数: 7

Automatic Steering of Manually Inserted Needles. 手动插入针的自动转向。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2013-01-01 DOI: 10.1109/SMC.2013.257

Guofan Wu, Xiao Li, Craig A Lehocky, Cameron N Riviere

引用次数: 3

Comparison of Baseline Tremor Under Various Microsurgical Conditions. 不同显微外科条件下基线震颤的比较。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2013-01-01 DOI: 10.1109/SMC.2013.256

Trent S Wells, Sungwook Yang, Robert A Maclachlan, James T Handa, Peter Gehlbach, Cameron Riviere

引用次数: 14

The Development of a Web-based Resident Sign-out Training Program. 基于网络的住院医师登记培训计划的开发。

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 2009-12-04 DOI: 10.1109/ICSMC.2009.5346345

Justin M Devoge, Ellen J Bass, Mangwi Atia, McKinsey Bond, Linda A Waggoner-Fountain, Stephen M Borowitz

引用次数: 10

Multi-branched diagnostic trees 多分支诊断树

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 1989-11-14 DOI: 10.1109/ICSMC.1989.71258

D. Tong, C. H. Jolly, Kevin C. Zalondek

引用次数: 7

Managing system complexity with a motivated interface: examining the effects parameter variation has on system stability 用激励接口管理系统复杂性:检验参数变化对系统稳定性的影响

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 1989-11-14 DOI: 10.1109/ICSMC.1989.71454

Theresa M. Vitolo

引用次数: 0

Product differentiation in the electric power industry 电力行业的产品差异化

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 1989-11-14 DOI: 10.1109/ICSMC.1989.71486

H. Chao

引用次数: 1