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Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing.

, , , , , , и . Expert Syst. Appl., 40 (6): 2102-2108 (2013)

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Isabel Alves dos Santos

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EEG study on affective valence elicited by novel and familiar pictures using ERD/ERS and SVM-RFE., , , , , , и . Medical Biol. Eng. Comput., 52 (2): 149-158 (2014)Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing., , , , , , и . Expert Syst. Appl., 40 (6): 2102-2108 (2013)EEG-based Subject Independent Affective Computing Models., , , , и . INNS Conference on Big Data, том 53 из Procedia Computer Science, стр. 375-382. Elsevier, (2015)Mining EEG scalp maps of independent components related to HCT tasks., , , и . EMBC, стр. 3888-3891. IEEE, (2019)Approach on affective valence detection from EEG signals based on global field power measure and SVM-RFE algorithm., , , , и . IWBBIO, стр. 163-170. Copicentro Editorial, (2013)Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG Signals., , , , , , , и . BIOSIGNALS, стр. 54-60. SciTePress, (2013)Analysis of eyewitness testimony using electroencephalogram signals., , , и . EMBC, стр. 894-897. IEEE, (2021)Nonlinear regression metamodels: a systematic approach.. Int. J. Simul. Process. Model., 5 (3): 241-255 (2009)Estimating and Validating Nonlinear Regression Metamodels in Simulation., и . Communications in Statistics - Simulation and Computation, 36 (1): 123-137 (2007)Sequential experimental designs for nonlinear regression metamodels in simulation., и . Simul. Model. Pract. Theory, 16 (9): 1365-1378 (2008)