2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献

Common Spatial Pattern with Polarity Check for reducing delay latency in detection of MRCP based BCI system 带极性检查的公共空间模式在基于MRCP的BCI系统检测中减少延迟

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-15 DOI: 10.1109/NER.2017.8008409

Lin Yao, Mei Lin Chen, X. Sheng, N. Mrachacz‐Kersting, Xiangyang Zhu, D. Farina, N. Jiang

引用次数: 3

Effect of attention division on movement detection and execution in dual-task conditions 双任务条件下注意划分对动作检测和执行的影响

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-15 DOI: 10.1109/NER.2017.8008411

Susan Aliakbaryhosseinabadi, E. Kamavuako, D. Farina, N. Mrachacz‐Kersting

{"title":"Effect of attention division on movement detection and execution in dual-task conditions","authors":"Susan Aliakbaryhosseinabadi, E. Kamavuako, D. Farina, N. Mrachacz‐Kersting","doi":"10.1109/NER.2017.8008411","DOIUrl":"https://doi.org/10.1109/NER.2017.8008411","url":null,"abstract":"Dual tasking refers to the simultaneous execution of two tasks with different demands. In this study, we aimed to investigate the effect of a second task on a main task of motor execution and on the ability to detect the cortical potential related to the main task from non-invasive electroencephalographic (EEG). Participants were asked to perform a series of cue-based ankle dorsiflexions as the primary task (single task level). In some experimental runs, in addition to the primary task they concurrently attended an auditory oddball paradigm consisting of three tones while they were asked to count the number of sequences of special tones (dual task level). EEG signals were recorded from nine channels centered on Cz. Analysis of event-related potential (ERP) signals from Cz confirmed that the oddball task decreased the attention to the ankle dorsiflexion significantly. Furthermore, movement-related cortical potential (MRCP) analysis revealed that the amplitude of the MRCP and pre-movement slopes were changed significantly. These variations were significantly greater for the EEG channels corresponding to the motor cortex and the frontal-central cortex.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125517666","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

Use of regularized discriminant analysis improves myoelectric hand movement classification 使用正则化判别分析改进了手肌电运动分类

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-15 DOI: 10.1109/NER.2017.8008373

Agamemnon Krasoulis, K. Nazarpour, S. Vijayakumar

{"title":"Use of regularized discriminant analysis improves myoelectric hand movement classification","authors":"Agamemnon Krasoulis, K. Nazarpour, S. Vijayakumar","doi":"10.1109/NER.2017.8008373","DOIUrl":"https://doi.org/10.1109/NER.2017.8008373","url":null,"abstract":"Linear discriminant analysis (LDA) is the most commonly used classification method for movement intention decoding from myoelectric signals. In this work, we review the performance of various discriminant analysis variants on the task of hand motion classification. We demonstrate that optimal classification performance is achieved with regularized discriminant analysis (RDA), a method which generalizes various class-conditional Gaussian classifiers, including LDA, quadratic discriminant analysis (QDA), and Gaussian naive Bayes (GNB). The RDA method offers a continuum between these models via tuning two hyper-parameters which control the amount of regularization applied to the estimated covariance matrices. In this study, we performed a systematic classification performance comparison on four datasets. Hand motion was decoded from myoelectric and inertial data recorded from 60 able-bodied and 12 amputee subjects whilst they performed a range of 40 movements. We found that when the regularization parameters of the RDA classifier were carefully tuned via cross-validation, classification accuracy was statistically higher by a large margin as compared to any other discriminant analysis method (average improvement of 13.7% over LDA). Importantly, our findings were consistent across the able-bodied and amputee populations. This observation provides supporting evidence that our proposed methodology could improve the performance of pattern recognition-based myoelectric prostheses.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"195 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049355","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: 10

Dynamic synchronization state identification 动态同步状态识别

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-10 DOI: 10.1109/NER.2017.8008405

Huichun Luo, Xueying Du, Yongzhi Huang, A. Green, T. Aziz, Shouyan Wang

{"title":"Dynamic synchronization state identification","authors":"Huichun Luo, Xueying Du, Yongzhi Huang, A. Green, T. Aziz, Shouyan Wang","doi":"10.1109/NER.2017.8008405","DOIUrl":"https://doi.org/10.1109/NER.2017.8008405","url":null,"abstract":"In the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) nucleus, the synchronization level of multiple frequency band oscillations of local field potentials (LFPs) have been shown to be associated with chronic pain perception and modulation. In this study, a state identification approach was generated to dynamically identify the synchronization state of neural oscillation. In this approach, a pattern extraction model was created to characterize the patterning of the neural oscillations based on wavelet packet transform. The value of wavelet packet coefficients represents the synchronization level of pattern. And then a state discrimination model was designed to distinguish the synchronization state and de-synchronization state of pattern based on calculating a suitable threshold and discrimination strategies. By using the sensory thalamus and PVAG LFPs of neuropathic pain and simulation signals, the parameters of the approach were optimized for theta pattern (6–9Hz) and alpha pattern (9–12hz) identification respectively. Finally, the mean best performance of identifying the theta pattern states from 300s simulation signals achieved 91% sensitivity and 86% specificity, and achieved 80% sensitivity and 88% specificity for alpha pattern state identification. Then this approach was applied to the sensory thalamus and PVAG LFPs, and was able to identify the synchronization state of theta and alpha pattern. This study provides a reliable approach to dynamically identify the synchronization level of pattern of neuropathic pain disease through optimizing the parameters. Based on this approach, a real-time monitoring of the pain state and an adaptive treatment regimen can be achieved.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043609","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

Inter-ictal Seizure Onset Zone localization using unsupervised clustering and Bayesian Filtering 基于无监督聚类和贝叶斯滤波的癫痫发作区域定位

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-10 DOI: 10.1109/NER.2017.8008407

Y. Varatharajah, Brent M. Berry, Z. Kalbarczyk, B. Brinkmann, G. Worrell, R. Iyer

{"title":"Inter-ictal Seizure Onset Zone localization using unsupervised clustering and Bayesian Filtering","authors":"Y. Varatharajah, Brent M. Berry, Z. Kalbarczyk, B. Brinkmann, G. Worrell, R. Iyer","doi":"10.1109/NER.2017.8008407","DOIUrl":"https://doi.org/10.1109/NER.2017.8008407","url":null,"abstract":"Surgical removal of seizure-generating brain tissue can cure epilepsy in patients who do not respond to medications. However, identifying seizure-generating regions is difficult and fails in many cases. In this paper, we report a fully unsupervised and automated approach to seizure focus localization using a Bayesian filter. This method uses a spectral domain feature, Power in Bands (PIB). PIB is extracted from inter-ictal (non-seizure) intracranial EEG recordings of patients with focal epilepsy to differentiate normal and abnormal brain regions. This study was carried out using data collected from 34 patients with focal epilepsy at the Mayo Clinic. Experiments show that using a Bayesian filter for capturing temporal properties of the iEEGs recorded from epileptic brains remarkably improves localization accuracy (AUC: 0.63 → 0.72). Our study also reaffirms that high-frequency oscillations and inter-ictal spikes are useful inter-ictal biomarkers of the epileptic brain, and PIB, which could be implemented with relatively low computational burden, performs as well as the standard bio-markers when used in this setting.We conclude that the technique of extracting spectral features from inter-ictal iEEGs and capturing their temporal properties via a Bayesian filter markedly improves our ability to localize seizure onset zones.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128834263","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

Decoding acute pain with combined EEG and physiological data 结合脑电图和生理数据解码急性疼痛

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-08-10 DOI: 10.17863/CAM.12803

J. Lancaster, H. Mano, D. Callan, M. Kawato, B. Seymour

{"title":"Decoding acute pain with combined EEG and physiological data","authors":"J. Lancaster, H. Mano, D. Callan, M. Kawato, B. Seymour","doi":"10.17863/CAM.12803","DOIUrl":"https://doi.org/10.17863/CAM.12803","url":null,"abstract":"Across neuroscience research, clinical diagnostics, and engineering applications in pain evaluation and treatment, there is a need for an objective measure of pain experience and detection when it occurs. This detector should be reliable in real-world settings using easily accessible, non-invasive data sources. We present a simple yet robust paradigm for decoding pain using neural and physiological data including electroencephalography (EEG), pulse, and skin conductance (GSR) measurements. The present study uses multivariate classification to distinguish painful events from non-painful multimodal sensory stimuli. To classify the pain response and detect relevant data attributes, we employed a sparse logistic regression (SLR) machine learning protocol with automatic feature selection. EEG input consisted of time-frequency changes under trial conditions, and physiological data included fluctuations and spikes in pulse and skin conductance. Classification averaged 70% accuracy and selected between 5 and 15 features. In our experiment, pain was induced by cold stimulation which became noxious with prolonged exposure. Due to the long, ramp-and-hold nature of the stimulus, along with individual variability in sensitivity to pain, we did not observe specific rapid evoked responses or time-locked events common across participants. However, this format more closely resembles the experience of pain conditions requiring intervention which could be facilitated by a decoding system. The results illustrate the feasibility of developing a wireless pain detection system and give insight to important temporal, spectral, and spatial EEG events and physiological indicators of pain states. Success of the classifier protocol using these parameters could lead to the creation of a closed-loop system for decoding and intervention which can be applied in engineering and medical contexts.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132036856","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: 10

Single laser to multiple optical fiber device for optogenetics-based epidural spinal cord stimulation 单激光对多光纤光遗传学硬膜外脊髓刺激装置

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-05-25 DOI: 10.1109/NER.2017.8008327

Shih-Yin Chang, K. Naganuma, Hoshinori Kanazawa, Kenta Takashima, K. Konishi, T. Someya, M. Sekino, Y. Kuniyoshi, H. Onodera

{"title":"Single laser to multiple optical fiber device for optogenetics-based epidural spinal cord stimulation","authors":"Shih-Yin Chang, K. Naganuma, Hoshinori Kanazawa, Kenta Takashima, K. Konishi, T. Someya, M. Sekino, Y. Kuniyoshi, H. Onodera","doi":"10.1109/NER.2017.8008327","DOIUrl":"https://doi.org/10.1109/NER.2017.8008327","url":null,"abstract":"This paper reports on the design and optical characterization of a single laser to multiple optical fiber (SLMOF) device for optogenetics research. Output end of SLMOF is an optical fiber bundle made by 210 optical fibers arranged into a 7×30 matrix. Single laser beam produced by laser diode (LD) is delicately steered on optical table to be coupled into target optical fiber. By controlling the power of the LD and quickly switching among fibers, this device allows for high flexibility when designing spatiotemporal light patterns. We tested the optical characterization of the device both in vitro and in vivo. Light was emitted from the end of the fiber at a power of 275.4 mW/mm2 when a drive current of 80 mA was used. The transmission fraction was 18.6% after penetrating 0.2 mm of spinal cord slice, and 1.3% after 1 mm. Coupling with 50 ms of repetitive stimulation at 10 Hz produced an only 0.348 K temperature increase at the spinal cord surface. The capacity of the system for optogenetics research was also demonstrated by epidural spinal cord stimulation induced hindlimb motion in paralyzed rats expressing channelrhodopsin-2 (ChR2).","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122855743","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

Combined eye-head vs. head-only scanning in a blind patient implanted with the Argus II retinal prosthesis 植入Argus II视网膜假体的盲人患者的眼-头联合扫描与仅头扫描

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-05-25 DOI: 10.1109/NER.2017.8008284

A. Caspi, P. Rosendall, Jason W. Harper, Michael P. Barry, Kapil D. Katyal, G. Dagnelie, A. Roy

引用次数: 1

Improving sensitivity of cluster-based permutation test for EEG/MEG data 提高基于聚类的脑电图/脑磁图排列测试灵敏度

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-05-25 DOI: 10.1109/NER.2017.8008279

Gan Huang, Zhiguo Zhang

{"title":"Improving sensitivity of cluster-based permutation test for EEG/MEG data","authors":"Gan Huang, Zhiguo Zhang","doi":"10.1109/NER.2017.8008279","DOIUrl":"https://doi.org/10.1109/NER.2017.8008279","url":null,"abstract":"To solve multiple comparisons problems in EEG/MEG analyses, cluster-based permutation test is possibly the most powerful approach, while it also inherits the advantage of well-controlled family-wise error rate from point-level permutation test. Because the cluster-level statistics used accumulate statistical power of all points in a cluster, cluster-based permutation test has a much higher sensitivity for widespread clusters. In this study, we demonstrate that, when the threshold for cluster inclusion is inappropriately set, the existence of larger clusters lowers the sensitivity for detecting the presence of smaller clusters, because the influence of large clusters on permutation distribution is overlooked in previous studies. Further, we demonstrated that increasing the threshold for cluster inclusion can efficiently solve this problem and then proposed a new guideline for threshold selection in the cluster-based permutation test. Results on simulated data and real data show the proposed guideline can greatly improve the sensitivity of cluster-based permutation test for detecting small clusters while retaining the same family-wise error rate.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132909066","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

Profiling BCI users based on contralateral activity to improve kinesthetic motor imagery detection 基于对侧活动对脑机接口用户进行分析，以改善动觉运动图像检测

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2017-05-25 DOI: 10.1109/NER.2017.8008383

Sébastien Rimbert, C. Lindig-León, L. Bougrain

{"title":"Profiling BCI users based on contralateral activity to improve kinesthetic motor imagery detection","authors":"Sébastien Rimbert, C. Lindig-León, L. Bougrain","doi":"10.1109/NER.2017.8008383","DOIUrl":"https://doi.org/10.1109/NER.2017.8008383","url":null,"abstract":"Kinesthetic motor imagery (KMI) tasks induce brain oscillations over specific regions of the primary motor cortex within the contralateral hemisphere of the body part involved in the process. This activity can be measured through the analysis of electroencephalographic (EEG) recordings and is particularly interesting for Brain-Computer Interface (BCI) applications. The most common approach for classification consists of analyzing the signal during the course of the motor task within a frequency range including the alpha band, which attempts to detect the Event-Related Desynchronization (ERD) characteristics of the physiological phenomenon. However, to discriminate right-hand KMI and left-hand KMI, this scheme can lead to poor results on subjects for which the lateralization is not significant enough. To solve this problem, we propose that the signal be analyzed at the end of the motor imagery within a higher frequency range, which contains the Event-Related Synchronization (ERS). This study found that 6 out of 15 subjects have a higher classification rate after the KMI than during the KMI, due to a higher lateralization during this period. Thus, for this population we can obtain a significant improvement of 13% in classification taking into account the users lateralization profile.","PeriodicalId":142883,"journal":{"name":"2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"334 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455513","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: 6