Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Fatos T. Yarman Vural"'
Autor:
Lubna Shibly Mokatren, Rashid Ansari, Ahmet Enis Cetin, Alex D. Leow, Olusola A. Ajilore, Heide Klumpp, Fatos T. Yarman Vural
Publikováno v:
IEEE Access, Vol 9, Pp 19053-19065 (2021)
Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve performance in classifying the disorders and abnormalities.
Externí odkaz:
https://doaj.org/article/758e2a7eb3f249f69c52d313480faf10
Autor:
Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim, Sharlene D. Newman, Fatos T. Yarman Vural
Publikováno v:
Frontiers in Neuroinformatics, Vol 15 (2021)
Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the u
Externí odkaz:
https://doaj.org/article/e2da337043d345f3ac9925dd960a5f49
Autor:
Eryol Erkin, Fatos T. Yarman Vural
Publikováno v:
2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE).
Autor:
Erkin Eryol, Fatos T. Yarman Vural
Publikováno v:
2022 30th Signal Processing and Communications Applications Conference (SIU).
Publikováno v:
2022 30th Signal Processing and Communications Applications Conference (SIU).
Publikováno v:
2022 30th Signal Processing and Communications Applications Conference (SIU).
Publikováno v:
Brain Imaging and Behavior. 14:460-476
Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types of cognitive information. I
Publikováno v:
SIU
In this study, we aim to measure the information content of anatomic regions using the functional magnetic resonance images recorded during complex problem solving (CPS) task. We propose an information theoretic method for analyzing the activity in a
Publikováno v:
ICPR
Representing brain activities by networks is very crucial to understand various cognitive states. This study proposes a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence. The suggested brain networks are base
In this study, we introduce a measure for machine perception, inspired by the concept of Just Noticeable Difference (JND) of human perception. Based on this measure, we suggest an adversarial image generation algorithm, which iteratively distorts an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ebfd39e22b14ca06382ecb6d1f2f0c4