Browsing by Author "Chen, I-Tzu"

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Beamformer-based Spatiotemporal Imaging of Correlated Brain Activities
(2009)
Chen, I-Tzu
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Chen, Yong-Sheng
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Chen, Li-Fen
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資訊工程學系
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Department of Computer Science
The past findings have suggested that temporal correlation may relate the communications between the distributed areas. There are some studies in Magnetoencephalography and electroencephalography to analyze the functional connectivity between cortical areas with the oscillations feature of neuronal activity. However, it is also important to observe the functional connectivity through temporal correlation between cortical areas. We proposed a beamformer-based approach which exploits a maximum correlation criterion to maximize the significance level of correlation between brain activities. This criterion leads to a closed-form solution of the dipole orientation. Experiments with simulation data clearly demonstrate the effectiveness, necessity, and accuracy of the proposed method.
Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals
(2015-07-01)
Chan, Hui-Ling
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Chen, Li-Fen
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Chen, I-Tzu
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Chen, Yong-Sheng
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分子醫學與生物工程研究所
Functional connectivity calculated using multiple channels of electromagnetic brain signals is often over- or underestimated due to volume conduction or field spread. Considering connectivity measures, coherence is suitable for the detection of rhythmic synchronization, whereas temporal correlation is appropriate for transient synchronization. This paper presents a beamformer-based imaging method, called spatiotemporal imaging of linearly-related source component (SILSC), which is capable of estimating connectivity at the cortical level by extracting the source component with the maximum temporal correlation between the activity of each targeted region and a reference signal. The spatiotemporal correlation dynamics can be obtained by applying SILSC at every brain region and with various time latencies. The results of six simulation studies demonstrated that SILSC is sensitive to detect the source activity correlated to the specified reference signal and is accurate and robust to noise in terms of source localization. In a facial expression imitation experiment, the correlation dynamics estimated by SILSC revealed the regions with mirror properties and the regions involved in motor control network when performing the imitation and execution tasks, respectively, with the left inferior frontal gyrus specified as the reference region. (C) 2015 Elsevier Inc. All rights reserved.
Lead Field Space Projection for Spatiotemporal Imaging of Independent Brain Activities
(2009)
Chan, Huiling
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Chen, Yong-Sheng
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Chey, Li-Fen
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Chen, Tzu-Hua
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Chen, I-Tzu
Magnetoencephalography and electroencephalography are non-invasive instruments that can record magnetic fields and scalp potentials, respectively, induced from neuronal activities. The recordings are, superimposed signals contributed from the whole brain. Independent component analysis (ICA) can provide a way of decomposition by maximizing, the mutual independence of separated components. Beyond the temporal profile and topography provided by ICA, this work aims to estimate and map the cortical source distribution for each component. The proposed method first constructs a source space using lead field vectors for vertices on the cortical surface. By projecting the specified components to this source space, our method provides the corresponding spatiotemporal maps for these independent brain activities. Experiments using simulated brain activities clearly demonstrate the effectiveness and accuracy of the proposed method.