# Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines

arXiv: https://arxiv.org/abs/2304.04273 · HTML: https://mlsanity.com/paper/2304.04273/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 100 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)
- [Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data](https://mlsanity.com/paper/2008.10726/) (2020)
- [Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation](https://mlsanity.com/paper/1912.07812/) (2019)
- [Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2107.13505/) (2021)
- [Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning](https://mlsanity.com/paper/1908.00385/) (2019)
- [Predicting Driver Takeover Time in Conditionally Automated Driving](https://mlsanity.com/paper/2107.09545/) (2021)

## Built on by (most cited first, 13 of 13)

- [CLARE: Cognitive Load Assessment in REaltime with Multimodal Data](https://mlsanity.com/paper/2404.17098/) (2024)
- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [EEGDM: Learning EEG Representation with Latent Diffusion Model](https://mlsanity.com/paper/2508.20705/) (2025)
- [GazeMind: A Gaze-Guided LLM Agent for Personalized Cognitive Load Assessment](https://mlsanity.com/paper/2605.05790/) (2026)
- [MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data](https://mlsanity.com/paper/2605.22775/) (2026)
- [Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs](https://mlsanity.com/paper/2506.23458/) (2025)
- [A Simulator Dataset to Support the Study of Impaired Driving](https://mlsanity.com/paper/2507.02867/) (2025)
- [Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification](https://mlsanity.com/paper/2511.12394/) (2025)
- [Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces](https://mlsanity.com/paper/2512.07820/) (2025)
- [Understanding Mental States in Active and Autonomous Driving with EEG](https://mlsanity.com/paper/2512.09190/) (2025)
- [DECODE: Dual-Enhanced Conditioned Diffusion for EEG Forecasting](https://mlsanity.com/paper/2603.16885/) (2026)
- [Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals](https://mlsanity.com/paper/2605.05120/) (2026)
- [CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment](https://mlsanity.com/paper/2605.22774/) (2026)

## Abstract (arXiv)

Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data. The data was collected from 21 subjects while driving in an immersive vehicle simulator, in various driving conditions, to induce different levels of cognitive load in the subjects. The tasks consisted of 9 complexity levels for 3 minutes each. Each driver reported their subjective cognitive load every 10 seconds throughout the experiment. The dataset contains the subjective cognitive load recorded as ground truth. In this paper, we also provide benchmark classification results for different machine learning and deep learning models for both binary and ternary label distributions. We followed 2 evaluation criteria namely 10-fold and leave-one-subject-out (LOSO). We have trained our models on both hand-crafted features as well as on raw data.