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Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia., , , и . Inf. Fusion, (2021)Computationally Efficient Early Prognosis of the Outcome of Comatose Cardiac Arrest Survivors Using Slow-Wave Activity Features in EEG., , , и . CinC, стр. 1-4. IEEE, (2023)TRECVID 2004 Experiments at MediaTeam Oulu., , , , , , и . TRECVID, National Institute of Standards and Technology (NIST), (2004)Using Hilbert-Huang Transform to assess EEG slow wave activity during anesthesia in post-cardiac arrest patients., , , , , , , , и . EMBC, стр. 1850-1853. IEEE, (2016)EEG-based recognition of video-induced emotions: Selecting subject-independent feature set., и . EMBC, стр. 4287-4290. IEEE, (2013)Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform., и . EMBC, стр. 117-120. IEEE, (2015)EEG-based detection of awakening from isoflurane anesthesia in rats., , , , и . EMBC, стр. 4279-4282. IEEE, (2012)Correlation between muscular and nerve signals responsible for hand grasping in non-human primates., , , , , , , , , и 7 other автор(ы). EMBC, стр. 2314-2317. IEEE, (2014)Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing., , , , , и . EMBC, стр. 138-141. IEEE, (2020)Depth of Anesthesia During Multidrug Infusion: Separating the Effects of Propofol and Remifentanil Using the Spectral Features of EEG., , и . IEEE Trans. Biomed. Eng., 58 (5): 1216-1223 (2011)