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2018 6th International Conference on Brain-Computer Interface (BCI)最新文献

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Classification of mental arithmetic and resting-state based on Ear-EEG 基于耳脑电图的心算分类与静息状态
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311525
Soo-In Choi, G. Choi, Hyung-Tak Lee, Han-Jeong Hwang, Jaeyoung Shin
Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 %, central: 76.7 %, occipital: 82.6 % and ear: 75.6 %). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.
脑电图(EEG)主要用于开发脑机接口(BCI)系统。近年来,为了提高传统的基于脑电图的脑机接口(BCI)系统的实用性,人们提出了使用耳朵周围测量的脑电图(Ear-EEG)。基于耳-脑电图的脑机接口系统大多采用外源性脑机接口模式。在这项研究中,我们研究了使用Ear-EEG开发内源性脑机接口系统的可行性,该系统使用自调制脑信号。在受试者执行心算和基线任务时测量脑电数据。将整个脑区划分为4个感兴趣的脑区(额区、中枢区、枕区和耳区),对EEG数据进行分析,比较其EEG特征和分类性能。4个roi之间的事件相关(de)同步(ERD/ERS)模式相似,除枕区(额区:72.6%,中央区:76.7%,枕区:82.6%,耳区:75.6%)外,其余4个roi之间的分类表现不显著。从结果来看,我们可以证实利用Ear-EEG开发内源性脑机接口系统的可能性。
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引用次数: 12
Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI Meta脑机接口:海马体-纹状体网络启发的灵活脑机接口架构
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311488
Minryung R. Song, Sang Wan Lee
Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called “Meta BCI”.
神经信号分类是脑机接口(BCI)的关键步骤。尽管深度神经网络(DNN)已被证明具有惊人的分类能力,但DNN存在训练时间长和灾难性遗忘的问题。灾难性遗忘是指深度神经网络在学习新任务时倾向于忘记之前学习过的任务的现象。在这里,我们认为解决这个问题的方法可以在人脑中找到,具体来说,通过结合纹状体和海马体这两个区域的功能,这两个区域分别是与当前环境相关的强化学习和记忆回忆的关键。这些脑区的作用机制为解决dnn的灾难性遗忘和长时间训练提供了新的思路。参考海马体-纹状体网络,我们讨论了结合不同类型的dnn构建新的脑机接口架构的设计原则,称为“Meta脑机接口”。
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引用次数: 2
Context-dependent meta-control for reinforcement learning using a Dirichlet process Gaussian mixture model 使用狄利克雷过程高斯混合模型的强化学习的上下文相关元控制
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311512
Dongjae Kim, Sang Wan Lee
Arbitration between model-based (MB) and model-free (MF) reinforcement learning (RL) is key feature of human reinforcement learning. The computational model of arbitration control has been demonstrated to outperform conventional reinforcement learning algorithm, in terms of not only behavioral data but also neural signals. However, this arbitration process does not take full account of contextual changes in environment during learning. By incorporating a Dirichlet process Gaussian mixture model into the arbitration process, we propose a meta-controller for RL that quickly adapts to contextual changes of environment. The proposed model performs better than a conventional model-free RL, model-based RL, and arbitration model.
基于模型(MB)和无模型(MF)的强化学习(RL)之间的仲裁是人类强化学习的关键特征。仲裁控制的计算模型不仅在行为数据方面,而且在神经信号方面都优于传统的强化学习算法。然而,这种仲裁过程并没有充分考虑到学习过程中环境的上下文变化。通过将Dirichlet过程高斯混合模型纳入仲裁过程,我们提出了一种快速适应环境上下文变化的强化学习元控制器。该模型的性能优于传统的无模型RL、基于模型的RL和仲裁模型。
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引用次数: 2
EEG-based brain-computer interface for real-time communication of patients in completely locked-in state 基于脑电图的脑机接口,实现患者完全闭锁状态下的实时交流
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311509
Changhee Han, C. Im
In this study, we developed a practical EEG-based BCI paradigm for online binary communication of patients in completely locked-in state (CLIS). The performance of our BCI paradigm was evaluated with a female patient in CLIS, who had never communicated even with her family for more than a year. An average online classification accuracy of 87.5 % was achieved using EEG data recorded just for 5 seconds. This is the first report of successful application of EEG-based BCI to the online yes/no communication of patients in CLIS.
在这项研究中,我们开发了一种实用的基于脑电图的脑机接口模式,用于完全锁定状态(CLIS)患者的在线二进制通信。我们的脑机接口模式的性能评估与女性患者的CLIS,谁从未与她的家人沟通超过一年。使用记录5秒的脑电图数据,平均在线分类准确率达到87.5%。这是首个基于脑电图的脑机接口成功应用于CLIS患者在线是/否沟通的报道。
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引用次数: 10
Decoding movement information from cortical activity for invasive BMIs 从侵入性bmi的皮质活动中解码运动信息
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311504
Min-Ki Kim, Sung-Phil Kim
Most invasive brain-machine interfaces (BMIs) have relied on the movement-related information in the firing activities of a number of cortical neurons. Recently, many efforts have been made to represent high-dimensional firing activities of a neuronal ensemble in a low-dimensional features space, visualizing the trajectory of temporal evolution of neural activities. The resulting neural trajectory often provides a sound means to visualize encoding of movement information in neuronal ensembles as well as to improve decoding performance by eliminating noise from irrelevant neurons. The present study aims to build the neural trajectory from motor cortical neurons in a primate performing a center-out task. The neural trajectory built by the standard principal component analysis method well represented hand speed profiles and provided proper feature vectors to a subsequent decoding algorithm. The results suggest an effective way of single-trial speed decoding for invasive BMIs.
大多数侵入性脑机接口(BMIs)依赖于许多皮质神经元放电活动中的运动相关信息。近年来,研究人员在低维特征空间中对神经元集合的高维发射活动进行了表征,以可视化神经活动的时间演化轨迹。由此产生的神经轨迹通常提供了一种声音手段来可视化神经元集合中运动信息的编码,以及通过消除无关神经元的噪声来提高解码性能。本研究旨在建立灵长类动物运动皮层神经元在执行“中心向外”任务时的神经轨迹。采用标准主成分分析法构建的神经轨迹能够很好地表征手速度特征,为后续的解码算法提供了合适的特征向量。结果提示了一种有效的侵入性bmi单次快速解码方法。
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引用次数: 1
A dynamic Bayesian network analysis of functional connectivity during a language listening comprehension task 语言听力过程中功能连通性的动态贝叶斯网络分析
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311516
K. Shiba, T. Kaburagi, Y. Kurihara
We aim to characterize functional connectivity during a listening comprehension task in terms of fit to common network topology models. The functional connectivity is expressed as a network structure which is reconstructed from cerebral blood volume measurements. The cerebral blood volume in the frontal lobe is measured using functional near-infrared spectroscopy (NIRS). Based on the reconstructed functional network structure, we discuss whether the functional connectivity has a scale-free or random graph structure. The feasibility of the reconstructed network is evaluated based on the distribution of the number of edges at nodes. In order to validate our proposed model, two language listening comprehension tasks were presented to subjects and the feasibility of the model structure is discussed. The experimental results suggest that the reconstructed functional connectivity network is more likely to be a scale-free network with an “ultra-small” world than a random network.
我们的目标是描述听力理解任务中的功能连通性,以适应常见的网络拓扑模型。功能连通性表示为一个网络结构,由脑血容量测量重建。使用功能近红外光谱(NIRS)测量额叶的脑血容量。在重构功能网络结构的基础上,讨论了功能连通性是否具有无标度结构或随机图结构。基于节点边数的分布,对重构网络的可行性进行了评价。为了验证我们提出的模型,我们提出了两个语言听力理解任务,并讨论了模型结构的可行性。实验结果表明,重构的功能连接网络比随机网络更有可能是一个具有“超小”世界的无标度网络。
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引用次数: 1
An approach for assessing stroke motor function ability using the similarity between electroencephalographic power spectral densities on both motor cortices 一种利用脑电功率谱密度在两个运动皮层之间的相似性来评估中风运动功能能力的方法
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311510
Jihyeon Ha, Da-hye Kim, Laehyun Kim
The electroencephalography (EEG) based brain-computer interface (BCI) presented a new paradigm of rehabilitation. Especially, rehabilitation incorporating EEG based BCI for stroke with motor impairment makes rehabilitation more effective than previously; for example, it provides neurofeedback to improve engagement of the brain. In this study, we measured EEG data of nine patients with chronic stroke accompanied with a unilateral motor problem while all patients performed upper limb rehabilitation (performing a grasping task with the affected hand). As a result, we found that the EEG feature showed similar EEG power spectral densities between the ipsilesional area and contralesional area. Additionally, this feature was significantly correlated (Spearman correlation coefficient p = −0.7280, p < 0.05) with the Fugl-Meyer Assessment score of the affected hand, indicating a degree of motor function. These results showed that brain activity of patients who had low motor function bilaterally appeared in ipsilesional and contralesional areas, whereas brain activity of patients who had high motor function specifically appeared in the ipsilesional area only.
基于脑电图(EEG)的脑机接口(BCI)为康复治疗提供了一种新的范式。特别是,结合脑电脑机接口的康复治疗卒中合并运动障碍,使康复比以前更有效;例如,它提供神经反馈来提高大脑的参与度。在这项研究中,我们测量了9例伴有单侧运动问题的慢性中风患者的脑电图数据,同时所有患者都进行了上肢康复(用患手执行抓握任务)。结果表明,同侧和对侧的脑电特征具有相似的功率谱密度。此外,该特征与患手的Fugl-Meyer评估评分显著相关(Spearman相关系数p = - 0.7280, p < 0.05),表明运动功能的程度。这些结果表明,双侧低运动功能患者的脑活动出现在同侧和对侧区域,而高运动功能患者的脑活动只出现在同侧区域。
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引用次数: 2
Reliable predictors of SMR BCI performance — Do they exist? SMR脑机接口性能的可靠预测因素——它们存在吗?
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311490
L. Botrel, A. Kübler
Reliable predictors of BCI performance would be desirable for basic research and application of BCI in a clinical context alike. In basic research, predictors help to elucidate how the brain instantiates BCI control. With respect to BCI controlled applications to be used by patient end-users with disease, predictors could support the choice of the optimal brain signal. Training of the predicting variable may support later BCI control. Among others, physiologic and psychologic variables have been suggested as such predictors. For example, the resting state μ-rhythm peak, the activation of dorsolateral prefrontal cortex during motor imagery, and the ability to coordinate visual and motor information were related to performance in different motor imagery BCI paradigms. The predictive power was low to medium, few even high, where the physiologic predictor was most powerful. To identify predictors, those and the related criterion variable have to be unambiguously defined. Likewise, reliability and validity have to be specified in the realm of BCI.
脑机接口性能的可靠预测对于脑机接口的基础研究和临床应用都是可取的。在基础研究中,预测因子有助于阐明大脑如何实例化BCI控制。对于患者最终用户使用的脑机接口控制应用程序,预测器可以支持最佳脑信号的选择。对预测变量的训练可以支持以后的脑机接口控制。其中,生理和心理变量被认为是这样的预测因素。例如,静息状态的μ-节律峰、运动想象时背外侧前额叶皮层的激活以及视觉和运动信息的协调能力与不同运动想象脑机接口模式的表现有关。预测能力从低到中等,少数甚至高,其中生理预测能力最强。为了识别预测因子,那些和相关的标准变量必须被明确地定义。同样,在BCI领域中必须指定可靠性和有效性。
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引用次数: 5
Deep recurrent spatio-temporal neural network for motor imagery based BCI 基于运动意象的脑机接口深度递归时空神经网络
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311535
Wonjun Ko, Jee Seok Yoon, Eunsong Kang, E. Jun, Jun-Sik Choi, Heung-Il Suk
In this paper, we propose a novel architecture of a deep neural network for EEG-based motor imagery classification. Unlike the existing deep neural networks in the literature, the proposed network allows us to analyze the learned network weights from a neurophysiological perspective, thus providing an insight into the underlying patterns inherent in motor imagery induced EEG signals. In order to validate the effectiveness of the proposed method, we conducted experiments on the BCI Competition IV-IIa dataset by comparing with the competing methods in terms of the Cohen's k value. For qualitative analysis, we also performed visual inspection of the activation patterns estimated from the learned network weights.
在本文中,我们提出了一种新的基于脑电图的运动图像分类的深度神经网络架构。与文献中现有的深度神经网络不同,本文提出的网络允许我们从神经生理学的角度分析学习到的网络权重,从而深入了解运动图像诱发的脑电图信号的内在模式。为了验证所提出方法的有效性,我们在BCI Competition IV-IIa数据集上进行了实验,并在Cohen’s k值方面与竞争方法进行了比较。为了进行定性分析,我们还对从学习到的网络权重估计的激活模式进行了视觉检查。
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引用次数: 24
Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces 基于子空间对齐的自适应CSP在运动图像脑机接口中的转移
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311494
Yi-Ming Jin, Mahta Mousavi, V. D. Sa
In brain-computer interfaces, adapting a classifier from one user to another is challenging but essential to reduce training time for new users. Common Spatial Patterns (CSP) is a widely used method for learning spatial filters for user specific feature extraction but the performance is degraded when applied to a different user. This paper proposes a novel Adaptive Selective Common Spatial Pattern (ASCSP) method to update the covariance matrix using selected candidates. Subspace alignment is then applied to the extracted features before classification. The proposed method outperforms the standard CSP and adaptive CSP algorithms previously proposed. Visualization of extracted features is provided to demonstrate how subspace alignment contributes to reduce the domain variance between source and target domains.
在脑机接口中,将分类器从一个用户调整到另一个用户是具有挑战性的,但对于减少新用户的训练时间至关重要。公共空间模式(Common Spatial Patterns, CSP)是一种广泛应用于特定用户特征提取的空间过滤器学习方法,但当应用于不同的用户时,其性能会下降。本文提出了一种新的自适应选择公共空间模式(ASCSP)方法,利用选择的候选者更新协方差矩阵。然后在分类之前对提取的特征进行子空间对齐。该方法优于已有的标准CSP算法和自适应CSP算法。对提取的特征进行可视化,以演示子空间对齐如何有助于减少源域和目标域之间的域方差。
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引用次数: 12
期刊
2018 6th International Conference on Brain-Computer Interface (BCI)
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