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Review
. 2025 Dec 6;25(24):7426.
doi: 10.3390/s25247426.

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

Sirine Ammar  1   2 , Nesrine Triki  3 , Mohamed Karray  2 , Mohamed Ksantini  1
Affiliations
Review

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

Sirine Ammar et al. Sensors (Basel). .

Abstract

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.

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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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
EEG-based BCI setup for monitoring driver cognitive states during simulated driving [1].
Figure 2
Figure 2
Systematic review workflow.
Figure 3
Figure 3
Modality integration heatmap across seven EEG-based driving datasets.
Figure 4
Figure 4
Dataset size and gender distribution.
Figure 5
Figure 5
Age distribution (mean ages across datasets).
See this image and copyright information in PMC

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