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In this paper, we propose a method to evaluate human's emotion and stress based on heart rate variability (HRV). Firstly, experiment scheme has been designed to induce 4 kinds of emotions and the corresponding electrocardiogram (ECG) changes have been measured in a laboratory setting; Secondly, an improved fast denoising method based on wavelet transform(More)
Cortical parcellation of the human brain typically serves as a basis for higher-level analyses such as connectivity analysis and investigation of brain network properties. Inferences drawn from such analyses can be significantly confounded if the brain parcels are inaccurate. In this paper, we propose a novel affinity matrix structure based on multiple(More)
The biomedical signals are often corrupted by noise in their acquisition or transmission resulting in lower Signal to Noise Ratio (SNR), which brings problematic obstacles to successive biomedical signal processing. So suppressing noise and improving SNR effectively is an essential procedure and key issue in the research on biomedical signal processing. In(More)
Reliable cortical parcellation is a crucial step in human brain network analysis since incorrect definition of nodes may invalidate the inferences drawn from the network. Cortical parcellation is typically cast as an unsupervised clustering problem on functional magnetic resonance imaging (fMRI) data, which is particularly challenging given the pronounced(More)
Functional subnetwork extraction is commonly employed to study the brain's modular structure. However, reliable extraction from functional magnetic resonance imaging (fMRI) data remains challenging. As representations of brain networks, brain graph estimates are typically noisy due to the pronounced noise in fMRI data. Also, confounds, such as region size(More)
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