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A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.

Sirine Ammar^1,2, Nesrine Triki³, Mohamed Karray²

¹Advanced Technologies for Image and Signal Processing (ATISP) Lab, École Nationale d'Électronique et des Télécommunications de Sfax, University of Sfax, Sfax 3018, Tunisia.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

Publicly available electroencephalography (EEG) datasets for brain-computer interfaces (BCIs) in driving lack diversity and standardization. This analysis highlights critical gaps hindering reliable driver safety systems.

Keywords:

BCI EEG cognitive load deep learning driver monitoring driving simulation emotion recognition intelligent transportation systems machine learning multimodal signals public datasets

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Area of Science:

Neuroscience
Human-Computer Interaction
Automotive Safety

Background:

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer potential for real-time driver monitoring.
Reliable BCIs for Advanced Driver Assistance Systems (ADAS) require high-quality, accessible EEG driving datasets.
Current datasets suffer from standardization issues and demographic biases, limiting BCI system robustness.

Purpose of the Study:

To conduct a multidimensional benchmark analysis of seven publicly available EEG driving datasets.
To identify critical gaps and limitations in existing datasets for BCI-driven ADAS.
To provide recommendations for future EEG dataset design and utilization in driving research.

Main Methods:

Synthesized existing literature on EEG driving datasets without new experiments.
Compared datasets across task design, modality integration, demographics, accessibility, and reported performance.
Performed a structured, quantitative benchmark analysis.

Main Results:

Identified significant age and gender biases in participant demographics.
Noted an overreliance on simulated driving environments.
Found insufficient affective state monitoring and restricted data accessibility.

Conclusions:

Existing EEG driving datasets have critical limitations hindering the development of generalizable BCI systems for ADAS.
Future datasets require balanced participant distributions, standardized emotional annotations, and open data practices.
Addressing these gaps is crucial for advancing driver safety through BCI technology.