# Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach

arXiv: https://arxiv.org/abs/2607.01795 · HTML: https://mlsanity.com/paper/2607.01795/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [An Empirical Analysis of Community and Coding Patterns in OSS4SG vs. Conventional OSS](https://mlsanity.com/paper/2601.03430/) (2026)
- [Reverse Engineering User Stories from Code using Large Language Models](https://mlsanity.com/paper/2509.19587/) (2025)
- [Do Good, Stay Longer? Temporal Patterns and Predictors of Newcomer-to-Core Transitions in Conventional OSS and OSS4SG](https://mlsanity.com/paper/2601.23142/) (2026)
- [Same Project, Different Start: How Contribution Events Shape Activity and Retention in Open Source](https://mlsanity.com/paper/2604.22120/) (2026)

## Abstract (arXiv)

Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the visual cues that support this judgement in a classroom. We study whether a single-channel, consumer-grade EEG device (the NeuroSky MindWave Mobile 2) can distinguish easy from difficult educational-video content, using the publicly available dataset of Wang et al. [24] (ten learners, one excluded for excessive noise, leaving nine). We implement a hybrid CNN+LSTM+Attention model that combines the raw waveform with band-power features. In a within-subject setting, the model reaches up to 78.5% accuracy, compared with 55% for conventional feature-based classifiers; regularization (dropout and L2) closes the large gap between training and validation accuracy that we observe without it, keeping validation accuracy stable at roughly 68-73%. We are deliberately cautious about these numbers: with only nine subjects, within-subject evaluation is optimistic, and we argue that subject-independent evaluation -- in which no learner appears in both training and test data -- should be the standard for this task. To that end we release a reproducible evaluation pipeline. We frame the work as a feasibility study rather than a deployable system, and pair it with an open, notebook-based tool that records EEG, runs inference, and visualizes estimated cognitive load as a heatmap over the video timeline to help educators locate potentially challenging segments.