# Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning

arXiv: https://arxiv.org/abs/2209.10992 · HTML: https://mlsanity.com/paper/2209.10992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 86 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Appearance-and-Relation Networks for Video Classification](https://mlsanity.com/paper/1711.09125/) (2017)
- [Mental State Recognition via Wearable EEG](https://mlsanity.com/paper/1602.00985/) (2016)
- [An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload](https://mlsanity.com/paper/2202.12937/) (2022)

## Built on by (most cited first, 1 of 1)

- [Assessing Region-Level EEG Contributions to Cognitive Workload Prediction](https://mlsanity.com/paper/2606.02598/) (2026)

## Abstract (arXiv)

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This research presents a novel self-supervised method for mental workload modelling from EEG data employing Deep Learning and a continuous brain rate, an index of cognitive activation, without requiring human declarative knowledge. This method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data to fit the brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had a test Mean Absolute Percentage Error average of 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. Findings point to the existence of quasi-stable blocks of learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Across-subject models, induced with data from an increasing number of participants, thus containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable, does not rely on ad-hoc human-crafted models supporting replicability and falsifiability.