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RISS 인기검색어
- 검색키워드
- 저자 : Bartz, Daniel (검색결과 3 건)
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Analyzing neuroimaging data with subclasses: A shrinkage approach
Hohne, J.,Bartz, D.,Hebart, M.N.,Muller, K.R.,Blankertz, B. ACADEMIC PRESS 2016 NEUROIMAGE Vol.124 No.1
Among the numerous methods used to analyze neuroimaging data, Linear Discriminant Analysis (LDA) is commonly applied for binary classification problems. LDAs popularity derives from its simplicity and its competitive classification performance, which has been reported for various types of neuroimaging data. Yet the standard LDA approach proves less than optimal for binary classification problems when additional label information (i.e. subclass labels) is present. Subclass labels allow to model structure in the data, which can be used to facilitate the classification task. In this paper, we illustrate how neuroimaging data exhibit subclass labels that may contain valuable information. We also show that the standard LDA classifier is unable to exploit subclass labels. We introduce a novel method that allows subclass labels to be incorporated efficiently into the classifier. The novel method, which we call Relevance Subclass LDA (RSLDA), computes an individual classification hyperplane for each subclass. It is based on regularized estimators of the subclass mean and uses other subclasses as regularization targets. We demonstrate the applicability and performance of our method on data drawn from two different neuroimaging modalities: (I) EEG data from brain-computer interfacing with event-related potentials, and (II) fMRI data in response to different levels of visual motion. We show that RSLDA outperforms the standard LDA approach for both types of datasets. These findings illustrate the benefits of exploiting subclass structure in neuroimaging data. Finally, we show that our classifier also outputs regularization profiles, enabling researchers to interpret the subclass structure in a meaningful way. RSLDA therefore yields increased classification accuracy as well as a better interpretation of neuroimaging data. Since both results are highly favorable, we suggest to apply RSLDA for various classification problems within neuroimaging and beyond.
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Validity of Time Reversal for Testing Granger Causality
Winkler, Irene,Panknin, Danny,Bartz, Daniel,Muller, Klaus-Robert,Haufe, Stefan Institute of Electrical and Electronics Engineers 2016 IEEE transactions on signal processing Vol.64 No.11
<P>Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-reversed data. They show that this procedure, time-reversed Granger causality (TRGC), robustly rejects causal interpretations on mixtures of independent signals. While promising results have been achieved in simulations, it was so far unknown whether time reversal leads to valid measures of information flow in the presence of true interaction. Here, we prove that, for linear finite-order autoregressive processes with unidirectional information flow between two variables, the application of time reversal for testing Granger causality indeed leads to correct estimates of information flow and its directionality. Using simulations, we further show that TRGC is able to infer correct directionality with similar statistical power as the net Granger causality between two variables, while being much more robust to the presence of measurement noise.</P>
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INFLUENCE OF GEAR OIL FORMULATION ON OIL TEMPERATURE
D. WIENECKE,W. J. BARTZ 한국트라이볼로지학회 2002 한국트라이볼로지학회 학술대회 Vol.2002 No.10
Friction losses in complex tribe-technical system are revealed primarily through their effect on the operating temperature level. In order to assess the influence of the oil formulation on the temperature level comprehensive tests were run in a model test apparatus consisting of a special adapter for the 4-ball test rig. More than ten with different formulations (different base oils, additive packages and viscosity modifiers) were tested. The resulting temperature levels varied by nearly 25%. The objective of this model testing is to assess the influence of the oil formulation on the operating temperature of vehicle manual transmission. The correlation to the real tribotechnical system was confirmed by a VW Polo transmission test.
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