Functional MRI (fMRI) is widely used to examine brain functionality by
detecting alteration in oxygenated blood flow that arises with brain activity.
This work aims to investigate the neurological variation of human brain
responses during viewing of images with varied complexity using fMRI time
series (TS) analysis. Publicly available BOLD5000 dataset is used for this
purpose which contains fMRI scans while viewing 5254 distinct images of diverse
categories, drawn from three standard computer vision datasets: COCO, Imagenet
and SUN. To understand vision, it is important to study how brain functions
while looking at images of diverse complexities. Our first study employs
classical machine learning and deep learning strategies to classify image
complexity-specific fMRI TS, represents instances when images from COCO,
Imagenet and SUN datasets are seen. The implementation of this classification
across visual datasets holds great significance, as it provides valuable
insights into the fluctuations in BOLD signals when perceiving images of
varying complexities. Subsequently, temporal semantic segmentation is also
performed on whole fMRI TS to segment these time instances. The obtained result
of this analysis has established a baseline in studying how differently human
brain functions while looking into images of diverse complexities. Therefore,
accurate identification and distinguishing of variations in BOLD signals from
fMRI TS data serves as a critical initial step in vision studies, providing
insightful explanations for how static images with diverse complexities are
perceived.

通过使用 fMRI 时间序列分析，本研究旨在研究人类大脑对不同复杂度图像的神经变化，通过对公开的 BOLD5000 数据集进行图像分类和时间语义分割的实验，以揭示人类大脑在感知图像时 BOLD 信号的波动和变化。