Results 21 to 30 of about 5,709,929 (152)

Neuroadaptive changes in brain structural–functional coupling among pilots [PDF]

Frontiers in Neuroscience
BackgroundInvestigating the neural mechanisms underlying pilots’ brains is crucial for enhancing aviation safety. However, prior research has predominantly focused on identifying structural and functional differences in the brain, while the relationship ...
Xi Chen, Qingbin Meng, Qingsong Song, Peiran Xu, Shicong Zhang, Donglin Huang, Qi Chu, Jiamin Fan, Cheng Luo, Xiuyi Li   +9 more
doaj   +2 more sources

Decoding preparatory movement state-based motor imagery with multi layer energy decoder [PDF]

Journal of NeuroEngineering and Rehabilitation
Background Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation.
Yuxin Zhang, Mengfan Li, Miaomiao Guo, Guizhi Xu, Alan Wang   +4 more
doaj   +2 more sources

Wide-Sense Stationarity in Generalized Graph Signal Processing [PDF]

, 2022
We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space.
Tay, Wee Peng, Jian, Xingchao, Wee Peng Tay, Xingchao Jian   +3 more
core   +1 more source

Neural decoding of imagined speech from EEG signals using the fusion of graph signal processing and graph learning techniques

Neuroscience Informatics, 2022
Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) applications. Electroencephalography (EEG) signals, which record brain
Aref Einizade, Mohsen Mozafari, Shayan Jalilpour, Sara Bagheri, Sepideh Hajipour Sardouie   +4 more
doaj   +1 more source

Modelling and studying the effect of graph errors in graph signal processing

, 2021
Publisher Copyright: © 2021The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix.
Miettinen, Jari, Ollila, Esa, Vorobyov, Sergiy A., Jari Miettinen, Esa Ollila, Sergiy A. Vorobyov   +5 more
core   +1 more source

Correlation-based Graph Smoothness Measures In Graph Signal Processing

, 2023
Publisher Copyright: © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.Graph smoothness is an important prior used for designing sampling strategies for graph signals as well as for regularizing the problem of graph learning ...
Miettinen, Jari, Ollila, Esa, Wang, Xinjue, Vorobyov, Sergiy A.   +3 more
core   +1 more source

An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph

EURASIP Journal on Audio, Speech, and Music Processing, 2023
The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit from the learned multiple graphs’ property
Tingting Wang, Haiyan Guo, Zirui Ge, Qiquan Zhang, Zhen Yang   +4 more
doaj   +1 more source

Graph signal processing for narrowband direction of arrival estimation [PDF]

, 2023
For direction of arrival (DOA) estimation based on graph signal processing (GSP), it has been assumed that there is a phase shift between adjacent snapshots of the received signals.
Mitchell, P.D., Zakharov, Y., Li, D., Liu, W.   +3 more
core   +1 more source

Graph signal processing over a probability space of shift operators

, 2023
Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology.
Ji, Feng, Tay, Wee Peng, Ortega, Antonio
core   +2 more sources

Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a
Xiaoying Song, Li Chai
doaj   +1 more source