# Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals

arXiv: https://arxiv.org/abs/2504.13883 · HTML: https://mlsanity.com/paper/2504.13883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Machine learning in resting-state fMRI analysis](https://mlsanity.com/paper/1812.11477/) (2018)
- [A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data](https://mlsanity.com/paper/2101.05891/) (2021)
- [Cognitive Engagement for STEM+C Education: Investigating Serious Game Impact on Graph Structure Learning with fNIRS](https://mlsanity.com/paper/2307.13637/) (2023)
- [Functional Near-Infrared Spectroscopy (fNIRS) Analysis of Interaction Techniques in Touchscreen-Based Educational Gaming](https://mlsanity.com/paper/2405.08906/) (2024)
- [From Complexity to Simplicity: Using Python Instead of PsychoPy for fNIRS Data Collection](https://mlsanity.com/paper/2411.06523/) (2024)

## Abstract (arXiv)

This study estimates cognitive effort based on functional near-infrared spectroscopy data and performance scores using a hybrid DeepNet model. The estimation of cognitive effort enables educators to modify material to enhance learning effectiveness and student engagement. In this study, we collected oxygenated hemoglobin using functional near-infrared spectroscopy during an educational quiz game. Participants (n=16) responded to 16 questions in a Unity-based educational game, each within a 30-second response time limit. We used DeepNet models to predict the performance score from the oxygenated hemoglobin, and compared traditional machine learning and DeepNet models to determine which approach provides better accuracy in predicting performance scores. The result shows that the proposed CNN-GRU gives better performance with 73% than other models. After the prediction, we used the predicted score and the oxygenated hemoglobin to observe cognitive effort by calculating relative neural efficiency and involvement in our test cases. Our result shows that even with moderate accuracy, the predicted cognitive effort closely follow the actual trends. This findings can be helpful in designing and improving learning environments and provide valuable insights into learning materials.