scholarly journals Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiulin Wang ◽  
Wenya Liu ◽  
Xiaoyu Wang ◽  
Zhen Mu ◽  
Jing Xu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Major Depression ◽  
Music Perception ◽  
Spectral Feature ◽  
Current Data ◽  
Music Listening ◽  
Tensor Factorization ◽  
Eeg Signals ◽  
Spatio Temporal ◽  
Music Information

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

scholarly journals Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

2021 ◽  
Author(s):  
Wenya. Liu ◽  
Xiulin. Wang ◽  
Jing. Xu ◽  
Yi. Chang ◽  
Timo. Hämäläinen ◽  
...  
Keyword(s):  
Major Depression ◽  
Music Perception ◽  
Permutation Test ◽  
Tensor Decomposition ◽  
Brain Regions ◽  
Music Listening ◽  
Time Frequency ◽  
Oscillatory Networks ◽  
Coupled Constraints ◽  
The Individual

AbstractPrevious researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

scholarly journals Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Fanqiang Kong ◽  
Kedi Hu ◽  
Yunsong Li ◽  
Dan Li ◽  
Shunmin Zhao
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Multispectral Imaging ◽  
Rapid Development ◽  
Spectral Feature ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spatial Feature ◽  
Spatial Features ◽  
Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

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