2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献

Localization of Epileptogenic Zone Based on Radiomics Features of 18F-FDG PET in Patients with Temporal Lobe Epilepsy**Research supported by the National Natural Science Foundation of China (No. 82071550). 基于颞叶癫痫患者18F-FDG PET放射组学特征的癫痫区定位**国家自然科学基金项目(82071550)

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441427

Jiaxin Hao, Yuhai Xie, Qiangqiang Liu, Jiwen Xu, Puming Zhang

{"title":"Localization of Epileptogenic Zone Based on Radiomics Features of 18F-FDG PET in Patients with Temporal Lobe Epilepsy**Research supported by the National Natural Science Foundation of China (No. 82071550).","authors":"Jiaxin Hao, Yuhai Xie, Qiangqiang Liu, Jiwen Xu, Puming Zhang","doi":"10.1109/NER49283.2021.9441427","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441427","url":null,"abstract":"Interictal 18F-fluorodeoxyglucose-PET (18F-FDG PET) is widely used in presurgical evaluation for epileptogenic zone (EZ) identification in patients with temporal lobe epilepsy. However, the area of PET hypometabolism is usually more extensive than the epileptogenic foci, providing approximate localization rather than precise. In this study, we evaluated the performance of 18F-FDG PET for classifying the EZs and the non-EZs using the radiomics features in addition to the hypometabolic features. The PET images of twelve patients with temporal lobe epilepsy were segmented into small cubes, which were labelled as EZs defined by the surgical resection areas or non-EZs. After extracting the radiomics features as well as the hypometabolic features of each cube, a support vector machine classifier was employed to classify each cube into EZ or non-EZ. The classifier performed well with the area under the curve (AUC) of $0.893 pm 0.051$, and using the combined features together got better performance than using the hypometabolic features only. The findings of this study merit further explorations to encourage the use of radiomics features in clinical decision for the EZ localization based on 18F-FDG PET.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115605533","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: 0

Identifying the Onset of Increased Cognitive Load using Event-Related Potentials in Electroencephalography 使用脑电图中的事件相关电位识别认知负荷增加的开始

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441434

Margaret M. Swerdloff, L. Hargrove

引用次数: 0

Serial Decoding of Macaque Intracortical Activity for Feedforward Control of Coherent Sequential Reach 猕猴皮层内活动的序列解码及其前馈控制

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441209

Chenyang Li, Tianwei Wang, Yiheng Zhang, Xinxu Xu, Qifan Wang, Ruichen Zheng, He Cui

引用次数: 0

Investigating Variational Phase-Amplitude Coupling in EEG-based Emotion Recognition**This study is supported by the National Natural Science Foundation of China (Grant No. 62001026), and the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. 3050012222022). 基于脑电图的情绪识别的变分相幅耦合研究**本研究由国家自然科学基金项目(批准号:62001026)和北京理工大学青年科研基金项目(批准号:3050012222022)资助。

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441426

Chuting Zhang, Wenbin Shi, C. Yeh

引用次数: 0

Dimensionality Reduction via the Laplace-Beltrami Operator: Application to EEG-based BCI 拉普拉斯-贝尔特拉米算子的降维:在基于脑电图的脑机接口中的应用

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441404

Xiaoqi Xu, Nicolas Drougard, R. Roy

{"title":"Dimensionality Reduction via the Laplace-Beltrami Operator: Application to EEG-based BCI","authors":"Xiaoqi Xu, Nicolas Drougard, R. Roy","doi":"10.1109/NER49283.2021.9441404","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441404","url":null,"abstract":"Neural oscillations captured by electroencephalography (EEG) can be used by Brain-Computer Interfaces (BCIs) to reveal the underlying mental processes and enable explicitly or implicitly interacting with one's environment. Most feature extraction techniques are based on spatial filters and power analyses in multiple frequency bands. The global geometry feature is seldom investigated. In this paper, the spatial aspects of EEG signals are studied using the Laplace-Beltrami operator. The eigenvectors of the Laplace-Beltrami operator form an orthonormal basis for square-integrable functions over the scalp and capture the geometry of electrodes' position in a hierarchical way. The signals are decomposed into different spatial frequency components by the projection into the eigenspaces of the Laplace-Beltrami operator. Dimensionality reduction could be done by using only the low frequency components. This method is compared with Principal Component Analysis (PCA) filtering on publicly available motor imagery BCI data and achieved comparable results while being unsupervised, data-independent and requiring 33.7% less computation time.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129550390","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: 0

Investigating Cognitive Global Coordination in normal and autistic children using virtual reality environments – An EEG Study 利用虚拟现实环境研究正常儿童和自闭症儿童的认知全局协调能力——一项脑电图研究

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441259

S. Chrisilla, Tharun R Ragav, S. Vidhusha, A. Kavitha

引用次数: 1

Mechanosensitive Ion Channels Contribute to Micromotion Induced Membrane Potential Changes in Cells at the Neural Interface In Vivo 机械敏感离子通道参与微运动诱导的细胞内神经界面膜电位变化

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441094

Arati Sridharan, Jonathan Duncan, J. Muthuswamy

{"title":"Mechanosensitive Ion Channels Contribute to Micromotion Induced Membrane Potential Changes in Cells at the Neural Interface In Vivo","authors":"Arati Sridharan, Jonathan Duncan, J. Muthuswamy","doi":"10.1109/NER49283.2021.9441094","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441094","url":null,"abstract":"The functional impact of tissue micromotion and associated mechanical stresses on neurons and other cells in a neural interface remains unknown. Past in vivo experimental studies involving intracellular neural recordings have routinely noted membrane potential changes due to respiration and vascular pulsations. Often dismissed as an artifact, however, recent data using sharp electrodes in rodent models suggest that these changes are physiological. Previous work using gadolinium chloride, which directly and indirectly blocks mechano-sensitive ion channels and other ion channels, showed reduced micromotion-related membrane potential changes in intracellular neural recordings in vivo. This study aims to elucidate the role of mechanosensitive ion channels in micromotion related membrane potential changes. Using a double-barreled pipette, we delivered GsMTx-4 peptide (a known inhibitor of a class of mechano-sensitive ion channels) near cells experiencing micromotion related membrane potential changes. In all, we measured intracellular membrane potentials from 15 cells in one rat. Nine of 15 cells had significantly diminished respiratory-related membrane potential changes in the presence of GsMTx-4. These results indicate that mechano-sensitive ion channels may play a crucial role in modulating subthreshold electrophysiological signals at neural interfaces in response to mechanical micromotion.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365935","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: 1

Decoding Resting-state EEG to Predict Visual Field Defect with Convolutional Neural Network in Stroke 用卷积神经网络解码静息状态脑电图预测脑卒中患者视野缺损

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441458

Jiahua Xu, Zheng Wu, A. Nürnberger, B. Sabel

引用次数: 0

Home-based Detection of Epileptic Seizures Using a Bracelet with Motor Sensors* 使用带有运动传感器的手环在家检测癫痫发作*

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441171

Chunjiao Dong, Lei Chen, Tianchun Ye, X. Long, Ronald M. Aarts, J. V. van Dijk, Chunheng Shang, Xiwen Liao, Yunfeng Wang

{"title":"Home-based Detection of Epileptic Seizures Using a Bracelet with Motor Sensors*","authors":"Chunjiao Dong, Lei Chen, Tianchun Ye, X. Long, Ronald M. Aarts, J. V. van Dijk, Chunheng Shang, Xiwen Liao, Yunfeng Wang","doi":"10.1109/NER49283.2021.9441171","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441171","url":null,"abstract":"Epilepsy is a long-term neurogenic disease that requires caregivers to accompany the patient days and nights. Caregivers have to help the patients immediately when they are having a seizure, which could cause vital injuries or even death. To address this issue, we designed a bracelet containing a three-dimensional accelerometer and a three-dimensional gyroscope to record the movements of the patient and built a Random Forest model to automatically detect seizures in at most 10 seconds upfront. We designed a home-based data-collecting method that allows patients to stay at home or perform their daily activities outside the hospital. Data collected in this method would be similar to the situation in which the patients would actually use wearable monitoring devices at home. The performance was evaluated based on an experimental study of epilepsy detection and classification, where epileptic motor data was collected in the West China Hospital of Sichuan University. Due to the experimental results, our daytime seizure detection model achieved 75.91% sensitivity and 88.90% precision, while our nighttime seizure detection model achieved 88.01% sensitivity and 88.33% precision. These preliminary results indicate that this home-based data collection method can capture seizures efficiently.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"11 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114009669","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: 3

Application of Digital Image Analysis Methods for Quantifying Spatiotemporal Neural Dynamics from Planar Microelectrode Arrays 数字图像分析方法在平面微电极阵列时空神经动力学量化中的应用

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441122

Afareen Jaleel, P. Kudela, W. Anderson

引用次数: 1