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

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Dominant and subdominant hand exhibit different cortical activation patterns during tactile stimulation: An fNIRS study 在触觉刺激过程中,优势手和次优势手表现出不同的皮层激活模式:一项近红外光谱研究
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311502
Seung Tae Yang, S. Jin, Gihyoun Lee, Seon Yun Jeong, J. An
Recently, only little is known about the cortical activity of tactile sensation for the dominant and subdominant hand. The objective of this study was to investigate the hemodynamic response of human's cortical brain to tactile sensation to compare the dominant and subdominant hand. Ten healthy adults, 25–35 ages, were enrolled. A 45 channel near-infrared spectroscopy system was used to measure brain responses and a solenoid resonance actuator was utilized to stimulate tactile sensation. The results showed that for the hemodynamic response to both hands on tactile stimulation, the corresponding primary sensory cortex and supplementary motor area were commonly activated, but the tactile stimuli of the subdominant hand induced broader areas of cortical activation than that of the dominant hand. Thus, broad brain areas, including the primary motor cortex and sensory association cortex, were activated by tactile stimulation in subdominant hand. These results suggest that there are differences in brain responses to tactile stimulation of the dominant and subdominant hand, which may reflect the importance of neural adaptability and efficiency for tactile sensation of the hand dominance.
目前,人们对优势手和次优势手的触觉皮层活动知之甚少。本研究的目的是研究人类皮层对触觉的血流动力学反应,以比较优势手和次优势手。10名25-35岁的健康成年人被纳入研究。使用45通道近红外光谱系统测量大脑反应,使用电磁共振执行器刺激触觉。结果表明,对于双手触觉刺激的血流动力学反应,相应的初级感觉皮层和辅助运动区普遍被激活,但亚优势手的触觉刺激诱导的皮层激活区域比优势手更广泛。因此,在亚优势手的触觉刺激下,包括初级运动皮层和感觉关联皮层在内的广泛脑区被激活。这些结果表明,大脑对优势手和次优势手触觉刺激的反应存在差异,这可能反映了优势手触觉的神经适应性和效率的重要性。
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引用次数: 8
A neural recording microimplants with wireless data and energy transfer link 带有无线数据和能量传输链路的神经记录微植入物
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311506
Yoon-Kyu Song, Jihun Lee, Jungwoo Jang, C. Lee, Ah-Hyoung Lee
We have developed a wireless neural recording microimplant for a minimally invasive brain-machine interface. The proposed device utilizes a novel dual-band midfleld antenna to establish an efficient power and data link between an antenna and a coil receiver. It also uses a bipolar junction transistor to convert neural signals into third-order backscattering signals with high detection sensitivity levels. The overall performance of this system is evaluated with a head phantom which closely simulates an in-vivo recording condition. Our antenna achieves high transmission efficiency at 2.5/5 GHz when a miniaturized coil is placed at a target separation distance of about 20mm. This powering scheme allows the neural recording sensor to have a small footprint of a comparable passive neural implant. Thus, we have demonstrated an RFID-like system based on midfield wireless energy/data transfer to extract neural signals from the brain while minimizing potential trauma and physiological interference from the implant.
我们开发了一种无线神经记录微植入物用于微创脑机接口。该装置利用一种新型双频中场天线在天线和线圈接收器之间建立有效的功率和数据链路。它还使用双极结晶体管将神经信号转换为具有高检测灵敏度的三阶后向散射信号。该系统的整体性能是通过模拟体内记录条件的头部幻影来评估的。当小型化线圈放置在约20mm的目标分离距离时,我们的天线在2.5/5 GHz时获得了很高的传输效率。这种供电方案使得神经记录传感器的占地面积比被动神经植入物小。因此,我们展示了一种基于中场无线能量/数据传输的类似rfid的系统,可以从大脑中提取神经信号,同时最大限度地减少植入物的潜在创伤和生理干扰。
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引用次数: 0
Study of wavelet-based performance enhancement for motor imagery brain-computer interface 基于小波的运动图像脑机接口性能增强研究
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311520
Mukhtar M. Alansari, Mahmoud Kamel, B. Hakim, Y. Kadah
To enhance the reliability of motor imagery based brain-computer interface, we present a study that considers subject-based optimization of feature extraction and classification. In particular, wavelet-based feature extraction performed on different bands was optimized over available selections of wavelet family, length and number of decomposition levels. Likewise, the classification step considers three general families of classifiers whose parameters are optimized in a similar manner. Such optimization was performed for each subject whereby processing parameters are selected based on the best performance obtained in the training session. We report the results obtained from applying this approach to the BCI competition 2008 dataset 2b (Graz) and demonstrate that such optimization provides results that outperform previous methods.
为了提高基于运动图像的脑机接口的可靠性,我们提出了一项考虑基于主体的特征提取和分类优化的研究。特别是,在不同波段进行基于小波的特征提取,优化了可用的小波族、长度和分解层数的选择。同样,分类步骤考虑三大类分类器,它们的参数以类似的方式进行优化。这种优化是对每个主题进行的,其中处理参数是根据在训练中获得的最佳性能来选择的。我们报告了将这种方法应用于2008年BCI竞赛数据集2b (Graz)获得的结果,并证明这种优化提供的结果优于以前的方法。
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引用次数: 8
Towards robust machine learning methods for the analysis of brain data 迈向分析大脑数据的强大机器学习方法
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311495
K. Müller
In this short abstract I will discuss recent directions that machine learning and BCI efforts of the BBCI team and coworkers have taken. It is the nature of this short text that many pointers to research are given all of which show a high overlap to prior own contributions; this is not only unavoidable but intentional. When analysing Brain Data, it is challenging to combine data streams stemming from various modalities (see e.g. Biessmann et al., 2011, Sui et al., 2012, Fazli et al., 2015, Dähne et al., 2015). Hybrid BCIs are a successful example in this direction (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2012, 2015). These techniques are firmly rooted in modern machine learning and signal processing that are now readily in use for analysing EEG, for decoding cognitive states etc. (Nikulin et al. 2007, and see Dornhege et al. 2004, Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, 2016, Lemm et al., 2011, for recent reviews and contributions to Machine Learning for BCI). Note that fusing information has also been a very common practice in the sciences and engineering (W altz and Llinas, 1990). The talk will discuss challenges for BCIs that are to be applied outside controlled lab spaces. Such complex and highly artifactual scenarios demand robust signal processing methods; see e.g. Samek et al. 2014, 2017b for recent reviews on robust methods for BCI. In addition I may expand on technical advances on the explanation framework for deep neural networks (Baehrens et al. 2010, Bach et al. 2015, Lapuschkin et al. 2016a and 2016b, Samek et al. 2017a, Montavon et al. 2017, 2018) to BCI data is given (Sturm et al. 2016). Furthermore, time permitting, I will revisit co-adaptive BCI systems (Vidaurre et al. 2011, Müller et al. 2017) and report on an upcoming study connecting fMRI and EEG data for co-adaptive training (Nierhaus et al. 2017). This abstract is based on joint work with Wojciech Samek, Benjamin Blankertz, Gabriel Curio, Michael Tangermann, Siamac Fazli, Vadim Nikulin, Gregoire Montavon, Sebastian Bach/Lapuschkin, Irene Sturm, Arno Villringer, Carmen Vidaurre, Till Nierhaus and many other members of the Berlin Brain Computer Interface team, the machine learning groups and many more esteemed collaborators. We greatly acknowledge funding by BMBF, EU, DFG and NRF.
在这篇简短的摘要中,我将讨论机器学习和BCI团队及其同事的BCI工作的最新方向。这篇短文的本质是给出了许多研究的指针,所有这些都与之前自己的贡献有很大的重叠;这不仅是不可避免的,而且是有意为之。在分析大脑数据时,将来自不同模式的数据流结合起来是一项挑战(参见Biessmann等人,2011年,Sui等人,2012年,Fazli等人,2015年,Dähne等人,2015年)。混合型脑机接口是这一方向的成功范例(Pfurtscheller et al., 2010, meller - putz et al. 2015, Dähne et al. 2015, Fazli et al. 2012, 2015)。这些技术深深扎根于现代机器学习和信号处理,现在很容易用于分析脑电图,解码认知状态等(Nikulin等人,2007年,见Dornhege等人2004年,m ller等人2008年,b nau等人2009年,Tomioka和m ller, 2010年,Blankertz等人,2008年,2011年,2016年,Lemm等人,2011年,最近的评论和对BCI机器学习的贡献)。请注意,融合信息在科学和工程中也是一种非常常见的做法(W altz和Llinas, 1990)。本次讲座将讨论在受控实验室空间之外应用脑机接口所面临的挑战。这种复杂和高度人工的场景需要鲁棒的信号处理方法;参见Samek等人2014、2017b对脑接口鲁棒方法的最新综述。此外,我可以扩展深度神经网络解释框架的技术进步(Baehrens等人,2010年,Bach等人,2015年,Lapuschkin等人,2016a和2016b, Samek等人,2017a, Montavon等人,2017,2018),并给出BCI数据(Sturm等人,2016)。此外,如果时间允许,我将重新审视共适应脑机接口系统(Vidaurre等人,2011年,m勒等人,2017年),并报告即将进行的一项将功能磁共振成像和脑电图数据连接起来进行共适应训练的研究(Nierhaus等人,2017年)。这篇摘要是基于与Wojciech Samek、Benjamin Blankertz、Gabriel Curio、Michael Tangermann、Siamac Fazli、Vadim Nikulin、Gregoire Montavon、Sebastian Bach/Lapuschkin、Irene Sturm、Arno Villringer、Carmen Vidaurre、Till Nierhaus以及柏林脑机接口团队的许多其他成员、机器学习小组和许多更受尊敬的合作者的共同工作。我们非常感谢BMBF, EU, DFG和NRF的资助。
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引用次数: 0
Approaches to large scale neural recording by chronic implants for mobile BCIs 移动脑机接口慢性植入物大规模神经记录的方法
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311503
A. Nurmikko
The development of techniques for reading information from the brain to translate e.g. movement intentions to control of robotic hands and operating simple tablet-base communication devices by tetraplegic devices is an example of a contemporary BCI operating at a system level of neuro-technology innovation. At the same time, the BCI field could be viewed still at infancy, with both challenges and opportunities for development of considerably more advanced BCIs. For example, the physical cabling of neural sensors such as microelectrode arrays to external electronics is now witnessing a transition to wireless sensors thereby enabling higher degree of mobility of subjects.
从大脑中读取信息并将其转化为控制机械手的动作意图,以及通过四肢瘫痪设备操作简单的平板通讯设备的技术的发展,是当代脑机接口在神经技术创新系统层面操作的一个例子。与此同时,脑机接口领域仍处于起步阶段,发展更先进的脑机接口既有挑战,也有机遇。例如,神经传感器(如微电极阵列)与外部电子设备的物理布线现在正在向无线传感器过渡,从而使受试者的移动性更高。
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引用次数: 5
Towards a versatile brain-machine interface: Neural decoding of multiple behavioral variables and delivering sensory feedback versatile brain-machine interface 迈向多功能脑机接口:多种行为变量的神经解码和传递感官反馈的多功能脑机接口
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311500
M. Lebedev
While brain-machine interfaces (BMIs) strive to provide neural prosthetic solutions to people with sensory, motor and cognitive disabilities, they have been typically tested in strictly controlled laboratory settings. Making BMIs versatile and applicable to real life situations is a significant challenge. For example, in real life we can flexibly and independently control multiple behavioral variables, such as programming motor goals, orienting attention in space, fixating objects with the eyes, and remembering relevant information. Several neurophysiological experiments, conducted in monkeys, manipulated multiple behavioral variables in a controlled way; these multiple variables were decoded from the activity of same neuronal ensembles. Additionally, in the other monkey experiments, multiple motor variables were extracted from cortical ensembles in real time, such as controlling two virtual arms using a BMI. The next improvement has been achieved using brain-machine-brain interfaces (BMBIs) that simultaneously extract motor intentions from brain activity and generate artificial sensations using intracortical microstimulation (ICMS). For example, a BMBI can perform active tactile exploration of virtual objects. Such versatile BMIs bring us closer to the development of clinical neural prostheses for restoration and rehabilitation of neural function.
虽然脑机接口(bmi)努力为有感觉、运动和认知障碍的人提供神经假肢解决方案,但它们通常在严格控制的实验室环境中进行测试。使bmi具有通用性并适用于现实生活是一项重大挑战。例如,在现实生活中,我们可以灵活独立地控制多个行为变量,如编程运动目标、空间注意力定向、眼睛注视物体、记忆相关信息等。几个在猴子身上进行的神经生理学实验,以一种可控的方式操纵了多个行为变量;这些多重变量是从相同神经元群的活动中解码出来的。此外,在其他猴子实验中,从皮质集合中实时提取多个运动变量,例如使用BMI控制两个虚拟手臂。下一个改进是通过脑机脑接口(BMBIs)实现的,该接口可以同时从大脑活动中提取运动意图,并通过皮质内微刺激(ICMS)产生人工感觉。例如,BMBI可以对虚拟物体进行主动触觉探索。这种多功能的bmi指数使我们更接近于临床神经假体的发展,用于神经功能的恢复和康复。
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引用次数: 3
EEG-based classification of learning strategies : Model-based and model-free reinforcement learning 基于脑电图的学习策略分类:基于模型和无模型的强化学习
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311522
Dongjae Kim, C. Weston, Sang Wan Lee
Human reinforcement learning (RL) has been known to utilize two distinctive learning strategies, model-based (MB) and model-free (MF) RL. The process of arbitration between MB and MF is thought to be located in the ventrolateral prefrontal cortex and frontopolar cortex. These loci are near the cortex, so we expect the related information can be represented in EEG signals. However, EEG signal patterns considering the arbitration of RL has not been investigated. In this paper, we tested a EEG-based classification model to separate these two different types of trials, each of which is meant to promote MB and MF RL. We found, for the first time, firm evidence to indicate that information pertaining to learning strategies is represented in prefrontal EEG signals.
人类强化学习(RL)利用两种不同的学习策略,基于模型(MB)和无模型(MF)强化学习。MB和MF之间的仲裁过程被认为位于腹外侧前额叶皮层和额极皮层。这些位点靠近大脑皮层,因此我们期望相关信息能够在脑电信号中得到表征。然而,考虑RL仲裁的脑电信号模式尚未被研究。在本文中,我们测试了一个基于脑电图的分类模型来分离这两种不同类型的试验,每一种试验都是为了促进MB和MF的RL。我们首次发现,有确凿的证据表明,与学习策略有关的信息在前额叶脑电图信号中得到了体现。
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引用次数: 3
An electrode selection approach in P300-based BCIs to address inter- and intra-subject variability 基于p300的脑机接口的电极选择方法,以解决受试者之间和受试者内部的可变性
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311497
Vinicio Changoluisa, P. Varona, F. B. Rodríguez
Brain Computer Interface (BCI) technologies use neural activity to implement a direct communication channel for healthy and disable subjects. To achieve this, many investigations look to improve BCI precision by increasing the number of electrodes with standard configurations, ignoring inter- and intra-subject variability. To control this variability in event-related potential (ERP)-based BCIs we propose to investigate the cumulative peak difference, an intrinsic characteristic of ERP, as a measure for electrode selection. The results shown in this work indicate that the proposed method improved accuracy and bitrate in all analyzed electrode sets. Our work contributes to the management of inter- and intra-subject variability which helps to design accurate and low-cost BCIs.
脑机接口(BCI)技术利用神经活动实现健康和残疾受试者之间的直接通信通道。为了实现这一目标,许多研究希望通过增加标准配置的电极数量来提高脑机接口的精度,而忽略受试者之间和受试者内部的可变性。为了控制基于事件相关电位(ERP)的脑机接口的这种可变性,我们建议研究ERP的固有特征——累积峰差,作为电极选择的衡量标准。研究结果表明,该方法提高了所有分析电极组的精度和比特率。我们的工作有助于管理受试者之间和受试者内部的可变性,这有助于设计准确和低成本的脑机接口。
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引用次数: 2
Detecting voluntary gait initiation/termination intention using EEG 利用脑电图检测自主步态启动/终止意图
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311532
Junhyuk Choi, S. Lee, Seung-jong Kim, Jong Min Lee, Hyungmin Kim
In this study, we employed a linear classifier to grasp the abstract features of electroencephalography (EEG) for recognizing voluntary gait intention and termination. We monitored Mu-band EEG to find gait intention and tried to detect a movement on/offset. Considerable gait-related (de) synchronization occurred hence, amplified by common spatial pattern (CSP). Performance of the classifier was evaluated in terms of classification success rates and false positive rates.
在这项研究中,我们使用线性分类器来掌握脑电图(EEG)的抽象特征,以识别自主步态的意图和终止。我们监测mu波段脑电图来发现步态意图,并试图检测运动/偏移。因此,大量的步态相关(非)同步发生,并被共同空间模式(CSP)放大。分类器的性能根据分类成功率和假阳性率进行评估。
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引用次数: 5
Cortical activation patterns of electrical pain stimulation using fNIRS fNIRS电痛觉刺激的皮层激活模式
Pub Date : 2018-01-01 DOI: 10.1109/IWW-BCI.2018.8311511
Inhwa Han, Sang-Hwa Won, Yungeui Kang, Kyungseok Oh, Kye-Yoep Kim, Janghwan Jekal, S. Jin, Gihyoun Lee, Seung Tae Yang, Seon Yoon Jung, J. An
Until recently, pain assessment has largely relied on subjective self-reports such as questionnaires or VAS. This paper attempts to objectively quantify pain from a neurological point of view through the characteristics of cerebral hemodynamics. Functional near-infrared spectroscopy (fNIRS) measures cortical blood flow changes during electrical pain stimulation. The selected feature of pain measure is the concentration change of oxygenated hemoglobin. Cortical activation patterns and time-series analysis for region of interest shows that premotor cortex and primary motor cortex as well as somatosensory cortex are involved in pain perception. These results are consistent with the findings of fMRI studies on physical pain. Oxygenated hemoglobin is therefore likely to be a quantitative biomarker of pain.
直到最近,疼痛评估在很大程度上依赖于主观的自我报告,如问卷调查或VAS。本文试图通过脑血流动力学的特点,从神经学的角度客观地量化疼痛。功能性近红外光谱(fNIRS)测量电刺激疼痛时皮质血流的变化。疼痛测量选择的特征是氧合血红蛋白的浓度变化。对感兴趣区域的皮层激活模式和时间序列分析表明,运动前皮层、初级运动皮层和躯体感觉皮层参与疼痛感知。这些结果与功能性磁共振成像对身体疼痛的研究结果一致。因此,氧合血红蛋白可能是疼痛的定量生物标志物。
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引用次数: 5
期刊
2018 6th International Conference on Brain-Computer Interface (BCI)
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