Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification

We propose an algorithm targeting the identification of more sources than channels for electroencephalography (EEG). Our overcomplete source identification algorithm, Cov-DL, leverages dictionary learning methods applied in the covariance-domain. Assuming that EEG sources are uncorrelated within moving time-windows and the scalp mixing is linear, the forward problem can be transferred to the covariance domain which has higher dimensionality than the original EEG channel domain. This allows for learning the overcomplete mixing matrix that generates the scalp EEG even when there may be more sources than sensors active at any time segment, i.e. when there are non-sparse sources. This is contrary to straight-forward dictionary learning methods that are based on the assumption of sparsity, which is not a satisfied condition in the case of low-density EEG systems. We present two different learning strategies for Cov-DL, determined by the size of the target mixing matrix. We demonstrate that Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios of EEG simulations and real EEG experiments

Teaching the Academic Word List in Foreign Language Speaking Classes

WOS: 00043485270039

Support Recovery and Dictionary Learning for Uncorrelated EEG Sources

Sparse signal recovery and dictionary learning methods have found a vast number of applications including but not limited to data compression, machine learning and biomedical source localization/separation. The common underlying assumption in domains of application of these methods is that signals of interest are either sparse or can be sparsified in a transform domain. For source localization or identification this implies that the number of coefficients needed to represent the source signals in the transform domain should be less than the number of sensors. This evidently imposes constraints on the types of signals that can be recovered. In this work, we show that these constraints can be relaxed if the source signals are uncorrelated. Our work is inspired by the nature of electroencephalography (EEG) sources for which the independence assumption has been widely and successfully used. We focus on the multiple-measurement- vector (MMV) model of the sparse inverse problem. Under the assumption of uncorrelated sources, we first show that the required sparsity conditions for accurate signal support recovery can be relaxed which enables EEG source localization when more sources than sensors are simultaneously active. Later, we show that one can transform the traditional dictionary learning formulation into the covariance-domain to leverage the correlation information of the sources. Our covariance-domain dictionary learning framework can accurately identify the EEG scalp mixing matrix even when sources are not sparse in the traditional sense. This method enables the use of low-cost, low-density systems for high-density EEG brain imaging, which traditionally suffers from poor performance when using constraint-sensitive source separation algorithms like Independent Component Analysis. We also present locally-complete source separation algorithms that tackle the non-stationary nature of EEG sources. Finally, we present algorithms that targets identification of independent sources given an overcomplete dictionary. Our algorithms differ from the usual MMV sparse recovery algorithms in the sense that they optimize independence of the sources rather than their sparsity. We also a present robust bayesian algorithm for joint-sparse recovery in the MMV formulatio

Support Recovery and Dictionary Learning for Uncorrelated EEG Sources

Sparse signal recovery and dictionary learning methods have found a vast number of applications including but not limited to data compression, machine learning and biomedical source localization/separation. The common underlying assumption in domains of application of these methods is that signals of interest are either sparse or can be sparsified in a transform domain. For source localization or identification this implies that the number of coefficients needed to represent the source signals in the transform domain should be less than the number of sensors. This evidently imposes constraints on the types of signals that can be recovered. In this work, we show that these constraints can be relaxed if the source signals are uncorrelated. Our work is inspired by the nature of electroencephalography (EEG) sources for which the independence assumption has been widely and successfully used. We focus on the multiple-measurement- vector (MMV) model of the sparse inverse problem. Under the assumption of uncorrelated sources, we first show that the required sparsity conditions for accurate signal support recovery can be relaxed which enables EEG source localization when more sources than sensors are simultaneously active. Later, we show that one can transform the traditional dictionary learning formulation into the covariance-domain to leverage the correlation information of the sources. Our covariance-domain dictionary learning framework can accurately identify the EEG scalp mixing matrix even when sources are not sparse in the traditional sense. This method enables the use of low-cost, low-density systems for high-density EEG brain imaging, which traditionally suffers from poor performance when using constraint-sensitive source separation algorithms like Independent Component Analysis. We also present locally-complete source separation algorithms that tackle the non-stationary nature of EEG sources. Finally, we present algorithms that targets identification of independent sources given an overcomplete dictionary. Our algorithms differ from the usual MMV sparse recovery algorithms in the sense that they optimize independence of the sources rather than their sparsity. We also a present robust bayesian algorithm for joint-sparse recovery in the MMV formulatio

English School theory of international relations:Its origins, concepts, and debates

Bu makale uluslararası ilişkilere kuramsal yaklaşımlardan biri olan İngiliz Okulu’nu tanıtmayı ve değerlendirmeyi amaçlamaktadır. İngiliz Okulu’nun ne olduğunu anlatmak için, öncelikle Okul’un kökleri, kurucuları, ontolojik ve yöntembilimsel duruşu tanıtılmış ve tartışılmıştır. Daha sonra Okul’un özünü oluşturan temel argümanlar belirlenmiş ve kurucularının tartıştıkları önemli sorular ele alınmıştır. Makalenin ikinci bölümünde ise, Okulun ikinci nesli diyebileceğimiz kuramcıların ürettikleri normatif ve yapısalcı eserlere ve tartışmalara yer verilmiştir. Sonuç olarak makale Okulun hem normatif hem de analitik analizlere imkân sağladığı için bir ‘büyük kuram’ potansiyeline sahip olduğunu iddia etmektedir.This article introduces and evaluates the English School of international relations theories. First we discuss the School’s roots, traditions, founding fathers and its ontology and methodology. Then the main arguments of the English School (ES) that constitute its core tenets and the grand questions that the founding fathers discussed are presented. In the second part, the two paths – normative and structural – taken by second generation ES scholars are discussed. This article argues that ES has a potential to be a ‘grand theory’ for the IR literature due to its rich theoretical background that allows both analytical and normative analyses

Optimal posture control for a 7 DOF haptic device based on power minimization

The aim of this study is to put forward potential advantages of redundant haptic devices. The use of redundancy in haptic devices basically piovides a larger workspace without changing kinematics parameters such as joint variables, joint offsets, effective link lengths and twist angles. Besides an increase in the workspace, redundant manipulators allow appropriate posture selection for different purposes, such as singularity avoidance, obstacle avoidance, inertia minimization, power minimization. These purposes can be considered either together or separately in order to determine optimal posture. The study in this paper is focused on optimal posture control of a 7 DOF haptic device based on power minimization. The designed haptic device has 4 DOF for positioning stage and 3 DOF for orientation stage

Prospective Evaluation of Chromosomal Breakages in Hemophiliac Children after Radioisotope Synovectomy with Yttrium(90) and Rhenium(186).

50th Annual Meeting of the American-Society-of-Hematology/ASH/ASCO Joint Symposium -- DEC 06-09, 2008 -- San Francisco, CAWOS: 000262104701441Amer Soc Hematol, Sanofi Aventis U.S