TY - GEN
T1 - Concept-level recognition from neuroimages for understanding learning in the brain
AU - Zhang, Yiping
AU - Sun, Liqian
AU - Wang, Jingheng
AU - Shen, Yan
AU - Zhang, Yupei
AU - Liu, Shuhui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Functional magnetic resonance imaging (fMRI) can measure changes in blood oxygenation level-dependent (BOLD) in the human brain caused by some stimuli or tasks, which helps us understand human brain mechanisms and functions. Recent studies demonstrate that there are both shared and specific neural representations between nature and drawing images. However, there is a lack of more detailed studies on the differences at the various levels of image abstraction. In this paper, we proposed to recognize concept levels from the image abstractions, including photographs, drawings, and sketches. More specifically, this study first conducts preprocessing processes to mitigate fMRI noise, such as respiratory and head movement, and then constructs the functional connectivity matrix based on the fMRI segmentation corresponding to different stimuli, leading to our used datasets. On the resulting dataset, we trained several data classifiers to obtain the mapping from fMRIs to the three types of stimuli. In addition, we discussed experimental parameters to check their impacts on classification performance. The evaluated results show that different concept-level images could be recognized at an effective accuracy, where the deep learning model achieves the best performance. This study contributes to an understanding of the abstraction level of concept formulation in the brain. The results can help treat brain disorders and make learning plans.
AB - Functional magnetic resonance imaging (fMRI) can measure changes in blood oxygenation level-dependent (BOLD) in the human brain caused by some stimuli or tasks, which helps us understand human brain mechanisms and functions. Recent studies demonstrate that there are both shared and specific neural representations between nature and drawing images. However, there is a lack of more detailed studies on the differences at the various levels of image abstraction. In this paper, we proposed to recognize concept levels from the image abstractions, including photographs, drawings, and sketches. More specifically, this study first conducts preprocessing processes to mitigate fMRI noise, such as respiratory and head movement, and then constructs the functional connectivity matrix based on the fMRI segmentation corresponding to different stimuli, leading to our used datasets. On the resulting dataset, we trained several data classifiers to obtain the mapping from fMRIs to the three types of stimuli. In addition, we discussed experimental parameters to check their impacts on classification performance. The evaluated results show that different concept-level images could be recognized at an effective accuracy, where the deep learning model achieves the best performance. This study contributes to an understanding of the abstraction level of concept formulation in the brain. The results can help treat brain disorders and make learning plans.
KW - Concept-level recognition
KW - data classification
KW - fMRIs
KW - image abstraction
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85184888830&partnerID=8YFLogxK
U2 - 10.1109/BIBM58861.2023.10385947
DO - 10.1109/BIBM58861.2023.10385947
M3 - 会议稿件
AN - SCOPUS:85184888830
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 3984
EP - 3990
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -