Understanding Cognitive Fatigue from fMRI Scans with Self-supervised
Learning
- URL: http://arxiv.org/abs/2106.15009v2
- Date: Wed, 30 Jun 2021 17:09:18 GMT
- Title: Understanding Cognitive Fatigue from fMRI Scans with Self-supervised
Learning
- Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia
Makedon, Glenn Wylie
- Abstract summary: This paper proposes dividing state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions.
We built a-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans.
This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that
records neural activations in the brain by capturing the blood oxygen level in
different regions based on the task performed by a subject. Given fMRI data,
the problem of predicting the state of cognitive fatigue in a person has not
been investigated to its full extent. This paper proposes tackling this issue
as a multi-class classification problem by dividing the state of cognitive
fatigue into six different levels, ranging from no-fatigue to extreme fatigue
conditions. We built a spatio-temporal model that uses convolutional neural
networks (CNN) for spatial feature extraction and a long short-term memory
(LSTM) network for temporal modeling of 4D fMRI scans. We also applied a
self-supervised method called MoCo to pre-train our model on a public dataset
BOLD5000 and fine-tuned it on our labeled dataset to classify cognitive
fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury
(TBI) patients and healthy controls (HCs) while performing a series of
cognitive tasks. This method establishes a state-of-the-art technique to
analyze cognitive fatigue from fMRI data and beats previous approaches to solve
this problem.
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