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dc.contributor.authorLopes-Dos-Santos, V-
dc.contributor.authorRey HG-
dc.contributor.authorNavajas J-
dc.contributor.authorQuian Quiroga R-
dc.date.accessioned2018-01-31T14:47:10Z-
dc.date.available2018-01-31T14:47:10Z-
dc.date.issued2017-12-23-
dc.identifier.urihttps://repositorio.ufrn.br/jspui/handle/123456789/24685-
dc.languageengpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectWavelet decompositionpt_BR
dc.subjectEvent-related potentialspt_BR
dc.subjectEEGpt_BR
dc.titleExtracting information from the shape and spatial distribution of evoked potentialspt_BR
dc.typearticlept_BR
dc.identifier.doihttps://doi.org/10.1016/j.jneumeth.2017.12.014-
dc.description.resumoBackground: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one ofthe most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. New method: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses. Results: Using simulations and real data from four experiments, we show that the proposed approach outperforms standard supervised analyses based on peak amplitude estimation. Moreover, the method can extract information using the raw data from all recorded channels using no a priori knowledge or pre-processing steps. Comparison with existing method(s): We show that traditional approaches often disregard important features of the signal such as the shape of EEG waveforms. Also, other approaches often require some form of a priori knowledge for feature selection and lead to problems of multiple comparisons. Conclusions: This approach offers a new and complementary framework to design experiments that go beyond the traditional analyses of ERPs. Potentially, it allows a wide usage beyond basic research; such as for clinical diagnosis, brain-machine interfaces, and neurofeedback applications requiring single-trial analysespt_BR
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