Non-identifiability of Electroencephalograms (EEG) and similar sensor signals from Medical care for Open science

The use of personal and personally identifiable information, which also and especially include biometric data, is strictly regulated in the context of the General Data Protection Regulation (GDPR). Patients and users in Europe enjoy extensive legal protection against unauthorised use and disclosure of their data. In many cases, however, this strong protection also results in a data usability that is restricted to a greater or lesser degree, which in turn conflicts with its social value for the advancement of medical knowledge and the further development of technologies. Addressing and resolving this dilemma calls for technical solutions that enable a provision and use of data that comply with data protection regulations in the sense of open data so that requirements regarding data protection and data use can be addressed simultaneously.

Objectives and approach

In the »NEMO« project, experts are examining the extent to which a person can be clearly identified from recorded biosignals. The question then is how suitable anonymisation techniques can prevent the identification and disclosure of sensitive information without stripping the data of their scientific value.

The key objective of »NEMO« is to explore and validate innovative techniques for the re-identification analysis and adaptive anonymisation of biosignals, using the example of electroencephalograms (EEG) from sleep monitoring systems. An EEG records brain activity via electrodes attached to the head. On the one hand, these data are particularly sensitive due to their information density, and on the other hand they are also particularly challenging in terms of developing effective anonymisation techniques. At the same time, the number of products on the market that record EEG data in the consumer sector is increasing.

Within »NEMO«, the experts are first of all developing analysis methods to quantify the risks of disclosing identities and sensitive information in the recorded raw data. To minimise these risks and at the same time ensure the highest possible utility of the EEG data, they will then explore and test techniques for their adaptive anonymisation. Among other things, the basis for this is know-how in the anonymisation of audio data and other technical data protection methods.

To integrate users in the anonymisation process and explain to them the mode of action and the added value of the anonymisation variants deployed, a demonstrator is additionally being developed that enables an application-related exploration and analysis of EEG data with the help of the anonymisation techniques made available.

Innovations and outlook

The project aims to generate a significant knowledge gain, not only by illustrating specific risk scenarios but also by developing and testing anonymisation techniques in the sensitive area of health data. The intention is to create a sound foundation for data protection concepts and anonymisation techniques. On the basis of a tried and tested technical infrastructure, the goal is the comprehensive use of data in research and development while at the same time safeguarding data protection.

Task areas of the project partners

Mobile Neurotechnologies

Fraunhofer IDMT Oldenburg

  • Project coordination
  • Neurophysiological assessment of approaches and results for EEG re-identification analysis (domain knowledge)
  • Checking and description of the requirements for the parameters to be maintained from the perspective of research data access in sleep monitoring applications
  • Sleep stage recognition and detection of defined events during sleep for performance comparison in anonymised and non-anonymised data

Media Distribution and Security

Fraunhofer IDMT Ilmenau

  • Assessment of application requirements from the perspective of technical data protection
  • Exploration and development of methods for re-identification analysis, selection of suitable privacy metrics to evaluate the privacy-utility trade-off
  • Study and implementation of methods for the adaptive anonymisation of EEG data (including know-how transfer in the area of audio anonymisation), differential privacy
  • Development of a concept for the future use of homomorphic encryption and secure federated learning for joint AI training

Ascora GmbH

  • Specification of the data model and system design for an integrated data platform
  • Development of tools for application-related data exploration and analysis
  • Implementation of the integrated data platform as a demonstrator

Christian-Albrechts-Universität zu Kiel (CAU) and University Hospital Schleswig-Holstein Campus Kiel (UKSH)


  • Provision of sleep data for the development of the algorithms and the evaluation method
  • Assessment of the risk scenarios from a medical perspective
  • Handling of ELSI issues (ethical, legal and social issues)
  • Analysis of the project results from a medical and a medical informatics perspective
  • Implementation of a stakeholder analysis and organisation of a workshop
  • Documentation and assessment of requirements for the anonymisation of EEG and sleep EEG data from a clinical perspective

Further information


Press Release / 9.5.2023

EEG as an example of data protection for biosignals

The »NEMO« project is exploring anonymisation techniques.


Project SleepWell

Mobile multisensor system for sleep monitoring

Sleep disorders impair health in the long term. That's why we are working on a multi-sensor system close to the ear to record sleep behavior conveniently at home.


Project REMUS

Contactless health monitoring

The group »Mobile Neurotechnologies« is working on monitoring vital parameters: contactless, hygienic, and easy to set up.


Project MOND

Mobile EEG systems for better epilepsy therapy

Recorded biosignals at the time of seizure in daily routines help classify epilepsy disorders.


AVATAR competence cluster

Fraunhofer IDMT, Ilmenau is a member of this network, which deals with the data protection compliant provision of clinical data for research and innovation in healthcare.

Funded by the German Federal Ministry of Education and Research BMBF under the funding code 16KISA061K


Funded by the European Union - NextGenerationEU

Mobile Neurotechnologies

The Mobile Neurotechnologies group works on discrete EEG systems for the analysis of brain activities - among other things for safe workplace design or for use in health applications.