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Publicação
Identifying evoked potential response patterns using independent component analysis and unsupervised learning
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| 1.27 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Independent Component Analysis(ICA) is a pre-processing step widely used in brain studies. One of
the most common problems in artifact elimination or brain activity related studies is the ordering and
identification of the independent components(ICs). In this work, a novel procedure is proposed
which combines ICA decomposition at trial level with an unsupervised learning algorithm (K-means)
at participant level in order to enhance the related signal patterns which might represent interesting
brain waves. The feasibility of this methodology is evaluated with EEG data acquired with participants
performing on the Halstead Category Test. The analysis shows that it is possible to find the Feedback
Error Negativity (FRN) Potential at single-trial level and relate its characteristics with the performance
of the participant based on their knowledge of the abstract principle underlying the task.
Descrição
Palavras-chave
ICA HCT K-means ERP FRN
Contexto Educativo
Citação
Editora
IOP Publishing