http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Villringer, A. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
Dahne, Sven,Biessmann, Felix,Samek, Wojciech,Haufe, Stefan,Goltz, Dominique,Gundlach, Christopher,Villringer, Arno,Fazli, Siamac,Muller, Klaus-Robert IEEE 2015 Proceedings of the Institute of Electrical and Ele Vol.103 No.9
<P>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.</P>
-
Vidaurre, C.,Nolte, G.,de Vries, I.E.J.,Gó,mez, M.,Boonstra, T.W.,Mü,ller, K.-R.,Villringer, A.,Nikulin, V.V. Elsevier 2019 NeuroImage Vol.201 No.-
<P><B>Abstract</B></P> <P>Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed a novel multivariate method for the detection of neural synchronization. </LI> <LI> Canonical Coherence (caCOH) maximizes coherence between two datasets. </LI> <LI> caCOH was validated in simulations and real data. </LI> <LI> caCOH is applicable for diverse brain-brain or brain-periphery signals (EEG/MEG/LFP/EMG). </LI> </UL> </P>
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
