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Effective EEG Connectivity Analysis of Episodic Memory Retrieval 36

Effective EEG Connectivity Analysis of Episodic Memory Retri...

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36th Annual Meeting of the cognitive science Society, CogSci 2014

作者： Lee, Chung-Yeon Zhang, Byoung-Tak Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of Computer Science and Engineering & Cognitive Science and Brain Science Programs Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)9780991196708

Episodic memory formation is associated with large-scale neuronal activity distributed across the cortex. Decades of neuroimaging and patient lesion studies demonstrated the correlation between the roles of specific brain structures in episodic memory retrieval. Distributed, coordinated and synchronized activities across brain regions have also been investigated. However, neuronal mechanisms based on effective connectivity underlying the coordination of this anatomically distributed information processing into introspectively coherent cognition have remained largely unknown. Here we investigate the information flow network of the human brain during episodic memory retrieval. We have estimated local oscillation amplitudes and asymmetric inter-areal synchronization from EEG recordings in individual cortical anatomy by using source reconstruction techniques and effective connectivity methods during episodic memory retrieval. The strength and spectro-anatomical patterns of these inter-areal interactions in sub-second time-scales reveal that the episodic memory retrieval involves the increase of information flow and densely interconnected networks between the prefrontal cortex, the medial temporal lobe, and some subregions of the parietal cortex. In this network, interestingly, the SFG acted as a hub, globally interconnected across broad brain regions. © 2014 Proceedings of the 36th Annual Meeting of the cognitive science Society, CogSci 2014. All rights reserved.

关键词： Neuroimaging

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A Novel Approach to Driving Fatigue Detection Using Forehead EOG

A Novel Approach to Driving Fatigue Detection Using Forehead...

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International IEEE/EMBS Conference on Neural engineering

作者： Yu-Fei Zhang Xiang-Yu Gao Jia-Yi Zhu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering and the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781467363877

Various studies have shown that the traditional electrooculograms (EOGs) are effective for driving fatigue detection. However, the electrode placement of the traditional EOG recording method is around eyes, which may disturb the subjects' activities, and is not convenient for practical applications. To deal with this problem, we propose a novel electrode placement on forehead and present an effective method to extract horizon electrooculogram (HEO) and vertical electrooculogram (VEO) from forehead EOG. The correlation coefficients between the extracted HEO and VEO and the corresponding traditional HEO and VEO are 0.86 and 0.78, respectively. To alleviate the inconvenience of manually labelling fatigue states, we use the videos recorded by eye tracking glasses to calculate the percentage of eye closure over time, which is a conventional indicator of driving fatigue. We use support vector machine (SVM) for regression analysis and get a rather high prediction correlation coefficient of 0.88 on average.

关键词： Electrooculography Forehead Fatigue Feature extraction Electrodes Correlation Glass

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A light rule-based approach to english subject-verb agreement errors on the third person singular forms 29

A light rule-based approach to english subject-verb agreemen...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Wang, Yuzhu Zhao, Hai Shi, Dan Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China LangYing NLP Research Institute Shanghai Lang Ying Education Technology Co. Ltd China

Verb errors are one of the most common grammar errors made by non-native writers of English. This work especially focus on an important type of verb usage errors, subject-verb agreement for the third person singular forms, which has a high proportion in errors made by non-native English learners. Existing work has not given a satisfied solution for this task, in which those using supervised learning method usually fail to output good enough performance, and rule-based methods depend on advanced linguistic resources such as syntactic parsers. In this paper, we propose a rule-based method to detect and correct the concerned errors. The proposed method relies on a series of rules to automatically locate subject and predicate in four types of sentences. The evaluation shows that the proposed method gives state-of-The-Art performance with quite limited linguistic resources.

关键词： Errors

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Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information

Fusing Simultaneous EEG and fMRI Using Functional and Anatom...

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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Sofie Therese Hansen Irene Winkler Lars Kai Hansen Klaus-Robert Müller Sven Dähne Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Department of Computer Science Technical University of Berlin Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Technische Universitat Berlin Berlin Berlin DE

Simultaneously measuring electro physical and hemodynamic signals has become more accessible in the last years and the need for modeling techniques that can fuse the modalities is growing. In this work we augment a specific fusion method, the multimodal Source Power Co-modulation (mSPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Anatomical information enters our proposed extension to mSPoC via the forward model, which relates the activity on cortex level to the EEG sensors. The augmented mSPoC is shown to outperform the original version in realistic simulations where the signal to noise ratio is low or where training epochs are scarce.

关键词： Electroencephalography Correlation Signal to noise ratio brain modeling Training Neuroimaging Lead

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Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Multivariate Machine Learning Methods for Fusing Multimodal ...

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作者： Dahne, Sven Bieszmann, Felix Samek, Wojciech Haufe, Stefan Goltz, Dominique Gundlach, Christopher Villringer, Arno Fazli, Siamac Muller, Klaus-Robert Machine Learning Group Department of Computer Science Berlin Institute of Technology Berlin10623 Germany Amazon Berlin10178 Germany Machine Learning Group Department of Video Coding and Analytics Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Laboratory for Intelligent Imaging and Neural Computing Columbia University New YorkNY10027 United States Institute of Psychology University of Leipzig Leipzig04109 Germany Department for Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig04103 Germany Mind-Brain Institute Charite Universitätsmedizin Berlin Humboldt-University Berlin10117 Germany Clinic for Cognitive Neurology University of Leipzig Leipzig04109 Germany Department of Brain and Cognitive Engineering Korea University Seoul136-713 Korea Republic of

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding;2) enhance diagnostic performance;and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader. © 2015 IEEE.

关键词： Neurophysiology

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A LightWAVE client for semi-automated annotation of Heart Beats from ECG Time Series

A LightWAVE client for semi-automated annotation of Heart Be...

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computers in Cardiology (CinC)

作者： Luca Citi Claudia Olariu Riccardo Barbieri School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park Colchester U.K School of Computer Science and Electronic Engineering University of Essex Colchester UK Dept. Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA Dept. Anesthesia Massachusetts General Hospital - Harvard Medical School Boston MA USA

LightWAVE is an open-source web-based software for viewing ECGs and other physiologic waveforms and associated annotations (such as heart-beat markers). At present, most users run the raw ECG through an automated QRS detector and later use LightWAVE to review and correct the detected heart beats. Although this 2-stage procedure may work well with clean signals, it is inefficient and time consuming when the recordings are contaminated by noise, artefacts or recurring ectopic events. To overcome this limitation, we customized the LightWAVE client to allow automated and semi-automated annotation of heart beats from ECG time series. In semi-automatic mode, the algorithm automatically identifies most QRS complexes and stops - asking for manual intervention - whenever the confidence of a detection falls below a given threshold. Additionally, the software now shows the series of inter-beat intervals, which is an invaluable tool to easily spot R-wave misdetections and genuine arrhythmias. The new client introduces further additional features compared to the standard version, for example the possibility of importing raw signals from local CSV files and of exporting the current plot in SVG format. Overall, our customized client extends the functionality of LightWAVE and brings it closer to one of its design goals, i.e. to provide a comfortable and efficient method of annotating physiologic data.

关键词： Electrocardiography Heart beat Physiology Detection algorithms Software Software algorithms Servers

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Measurement of shear and slip with a GelSight tactile sensor

Measurement of shear and slip with a GelSight tactile sensor

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenzhen Yuan Rui Li Mandayam A. Srinivasan Edward H. Adelson Department of Mechanical Engineering MIT Cambridge MA USA Department of Electrical Engineering and Computer Science (EECS) and CSAIL MIT Cambridge MA USA Massachusetts Institute of Technology Cambridge MA US Department of Brain and Cognitive Sciences and CSAIL MIT Cambridge MA USA

Artificial tactile sensing is still underdeveloped, especially in sensing shear and slip on a contact surface. For a robot hand to manually explore the environment or perform a manipulation task such as grasping, sensing of shear forces and detecting incipient slip is important. In this paper, we introduce a method of sensing the normal, shear and torsional load on the contact surface with a GelSight tactile sensor [1]. In addition, we demonstrate the detection of incipient slip. The method consists of inferring the state of the contact interface based on analysis of the sequence of images of GelSights elastomer medium, whose deformation under the external load indicates the conditions of contact. Results with a robot gripper like experimental setup show that the method is effective in detecting interactions with an object during stable grasp as well as at incipient slip. The method is also applicable to other optical based tactile sensors.

关键词： Force Surface topography Tactile sensors Entropy

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Feature selection for brain-computer interface using nearest neighbor information

Feature selection for brain-computer interface using nearest...

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2014 International Winter Workshop on brain-computer Interface, BCI 2014

作者： Noh, Yung-Kyun Min, Byoung-Kyong Department of Computer Science KAIST Daejeon Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9781479925889

We consider the feature selection problem for a braincomputer interface (BCI). A BCI collects data from sensors, and the data are discriminated using information in a high-dimensional space. We show how relevant features in a high dimensional space can be selected using a simple nearest neighbor method for estimating an information-theoretic measure, Jensen-Shannon divergence. Conventional nonparametric estimation using nearest neighbors already works very well for the feature selection problem and outperforms many other methods. In this paper, we show how this nearest neighbor method can be further exploited by properly trimming the non-informative direction for a distance calculation, and estimate the Jensen-Shannon divergence more accurately. Through experiments with synthetic data, we show how the proposed method outperforms a conventional nearest neighbor method as well as other feature selection methods with a large margin. © 2014 IEEE.

关键词： Information theory

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Hybrid brain-computer interface based on EEG and NIRS modalities

Hybrid brain-computer interface based on EEG and NIRS modali...

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2014 International Winter Workshop on brain-computer Interface, BCI 2014

作者： Lee, Min-Ho Fazli, Siamac Mehnert, Jan Lee, Seong-Whan Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Department of Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (纸本)9781479925889

Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous, which means, they rely on cues or tasks to which a subject has to react. It would be more useful for users if they could control a device at their own will (i.e., asynchronous BCIs). However, previous asynchronous BCI systems that rely on non-invasive electroencephalogram (EEG) measurements, are not accurate and stable enough for real world applications. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of synchronous motor imagery (MI) tasks. In this study, we present a first report on an asynchronous multi-modal hybrid BCI, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements and propose novel subject-dependent classification strategies for combining both measurements. © 2014 IEEE.

关键词： brain computer interface

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Information geometry meets BCI: Spatial filtering using divergences

Information geometry meets BCI: Spatial filtering using dive...

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2014 International Winter Workshop on brain-computer Interface, BCI 2014

作者： Samek, Wojciech Müller, Klaus-Robert Department of Computer Science Berlin Institute of Technology TU Berlin Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9781479925889

Algorithms using concepts from information geometry have recently become very popular in machine learning and signal processing. These methods not only have a solid mathematical foundation but they also allow to interpret the optimization process and the solution from a geometric perspective. In this paper we apply information geometry to brain-computer Interfacing (BCI). More precisely, we show that the spatial filter computation in BCI can be cast into an information geometric framework based on divergence maximization. This formulation not only allows to integrate many of the recently proposed CSP algorithms in a principled manner, but also enables us to easily develop novel CSP variants with different properties. We evaluate the potentials of our information geometric framework on a data set containing recordings from 80 subjects. © 2014 IEEE.

关键词： Beamforming

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