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Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization
2021
Frontiers in Human Neuroscience
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
doi:10.3389/fnhum.2021.799288
pmid:34975439
pmcid:PMC8714749
fatcat:3luyh6iosfa3bdqam3pqf5c2py