Skip to Content
Investigating Variability in EEG-Based Brain-Computer Interfaces: Insights from the NEARBY Project
Location: Alte Kornkammer - 1/15/26, 12:00 PM - 1/15/26, 12:30 PM (Europe/Berlin) (30 minutes)
Investigating Variability in EEG-Based Brain-Computer Interfaces: Insights from the NEARBY Project
Dr. Maurice Rekrut
Senior Researcher at Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
Dr. Maurice Rekrut
Senior Researcher at Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)

Dr. Maurice Rekrut is a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI), where he has been working since 2013 in the Cognitive Assistants research department. He received his MSc in Biomedical Engineering with a focus on Neural Engineering in 2013 and completed his PhD in Computer Science in 2023 under the supervision of Prof. Dr. Antonio Krüger. 

His research centers on Brain-Computer Interfaces (BCIs) and their application in real-world environments. He has led several national and international projects in this field, including EXPECT, BISON, and the French-German collaboration NEARBY. Since 2020, he's the head of the Cognitive Assistants BCI Lab at DFKI, which develops BCIs beyond controlled laboratory conditions. The lab explores applications across diverse domains such as aviation, automotive, health and well-being, and industrial contexts. 

Dr. Rekrut’s work focuses on making BCIs more robust, user-centered, and adaptable, with particular emphasis on neural human-machine interaction, neuroadaptive systems, and neurodiagnostics. The Cognitive Assistants BCI Lab contributes to advancing BCIs from experimental setups toward practical solutions that connect neuroscience, machine learning, and human-computer interaction. 

Brain-computer interfaces (BCIs) hold great promise for enabling direct communication between the human brain and external devices. However, their application outside controlled laboratory environments remains limited. One of the main obstacles is the inherent variability of brain signals—both between individuals and within the same person across different mental and physical states. This variability poses a significant challenge for developing robust and reliable BCIs suitable for real-world use, especially when relying on electroencephalography (EEG) as the primary measurement modality.

The NEARBY project, a joint initiative between DFKI and Inria, aims to better understand and address this variability. To this end, the project collects a large-scale, longitudinal EEG dataset using standardized recording protocols across multiple sessions, subjects, and environments. This unique resource allows for a systematic analysis of the impact of internal and external factors on EEG-based BCI performance and supports the development of adaptive algorithms capable of compensating for these fluctuations.

This talk will provide an overview of the goals and methodology of the NEARBY project, highlight the design of the long-term studies, and present preliminary findings from the ongoing data collection and analysis.