Open AccessSignal Segmentation Using Maximum a Posteriori Probability Estimator with Application in Artifact ”corrected” EEG Data
Author(s) -
Theodor D. Popescu
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.144
Subject(s) - artifact (error) , estimator , segmentation , pattern recognition (psychology) , artificial intelligence , computer science , electroencephalography , maximum a posteriori estimation , feature (linguistics) , speech recognition , statistics , mathematics , maximum likelihood , psychology , psychiatry , linguistics , philosophy
The ”corrected” EEG data, after artifact removing, may be the subject of further investigations, for example segmentation, result- ing new information to be used for feature ex- traction, of great help for medical diagnosis. The paper has as object a generally approach for seg- mentation, making use of Maximum A posteriori Probability (MAP) estimator. The proposed pro- cedure has been used in the analysis of a sample lowpass EEG signals recorded with 13 scalp and 1 EOG electrodes, event-related potential (ERP) data.