Mohammad Zaki Zadeh
Maria Kyrarini
ABSTRACT
Cognitive Fatigue contributes to the degradation of performance in daily life. This work focuses on developing an automated system to predict Cognitive Fatigue from Functional Magnetic Resonance Imaging (fMRI) data that were collected while subjects were performing a cognitive task. The task had multiple level of difficulty to induce cognitive load. With the fMRI data, Machine Learning models were built to predict the fatigue level. Preliminary results from twenty two participants show an average accuracy of 73 percent over k-fold cross validation.
Supplemental Material
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Cross Ref
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Index Terms
- Towards cognitive fatigue detection from functional magnetic resonance imaging data
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