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Modeling the Brain’s Probabilistic Prediction of Oddball Paradigm
, Ph.D. Dissertation Sharif University of Technology ; Karbalai Aghajan, Hamid (Supervisor)
Abstract
The brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Surprise leads to garnering attention, causes arousal, and motivates engagement. It motivates the formation of an explanation or updating of current models. Three models have been proposed for quantifying surprise as the Shannon, Bayesian, and confidence-corrected surprises. In this thesis, we analyze EEG and MEG signals recorded during oddball tasks to examine and statistically compare the value of temporal/ spatial components in decoding the brain’s surprise. We...
Ensemble multi-modal brain source localization using theory of evidence
, Article Biomedical Signal Processing and Control ; Volume 69 , 2021 ; 17468094 (ISSN) ; Hajipour Sardouie, S ; Shamsollahi, M. B ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
The primary aim in pre-surgical evaluations in patients with neurological disorders such as epilepsy is determining the precise location of the cortical region responsible for the malfunctions which is called source localization. Different modalities unravel different views of brain activity. Combining these complementary aspects of the brain yields more accurate source localization. In this paper, a method is proposed for combining localization methods in different modalities based on the theory of evidence, the result of some localization methods in modalities are integrated using weights in accordance to their relative performance and are combined using Dempster's rule of combination and...