Representation learning for improved interpretability and classification accuracy of clinical factor...
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based neural measures can function as reliable objective correlates of depression, or even predictors of depression and its course. However, their clinical utility has not been f...
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Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
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TN_cdi_proquest_journals_2456036303
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2456036303
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2331-8422
