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Mental State Classification Using EEG Signals: Ethics, Law and Challenges

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2133))

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Abstract

The widespread adoption of Artificial Intelligence (AI) in various domains has sparked concerns regarding privacy, security, transparency, fairness, reliability, and ethics. The General Data Protection Regulation (GDPR), implemented in 2018, has established crucial guidelines for data privacy and protection. Meanwhile, the AI Act, passed by the European Parliament in 2023, has been proposed to address the risks of specific uses of AI. However, the implications of these regulations for AI models, particularly in the context of mental state classification tasks, remain uncertain due to the inherent black-box nature of certain models. This paper delves into the specific challenges and risks associated with the development of EEG- (Electroencephalography) and AI-based systems for mental state classification, taking into account the principles outlined by the GDPR and the AI Act. This research sheds light on the challenges and responsibilities entailed in developing AI models for mental state classification while ensuring compliance with GDPR guidelines as well as the AI Act. It underscores the importance of adopting ethical and privacy-aware approaches within this critical domain and promotes the development of techniques that strike a balance between AI advancements and the protection of individual privacy.

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Notes

  1. 1.

    EEG is used to refer to either “electroencephalogram” or “electroencephalography”, which is the method by which electroencephalograms are acquired.

  2. 2.

    The trained models are available at this link.

  3. 3.

    The AI Act was proposed in 2021, and passed by the European Parliament in June 2023 [19, 57].

  4. 4.

    Implementation of ResNet-18 provided by torchvision at this link.

  5. 5.

    GitHub repository of pytorch-memory-utils at this link.

  6. 6.

    GitHub repository of pytorch-estimate-flops at this link.

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Authors and Affiliations

  1. LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France

    Rémi Nahon, Nasim Bagheri, Enzo Tartaglione & Van-Tam Nguyen

  2. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

    Giovanna Varni

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  1. Rémi Nahon
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  2. Nasim Bagheri
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  4. Enzo Tartaglione
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  5. Van-Tam Nguyen
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Correspondence to Rémi Nahon .

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  1. University of Turin, Turin, Italy

    Rosa Meo

  2. Sapienza University of Rome, Rome, Italy

    Fabrizio Silvestri

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Nahon, R., Bagheri, N., Varni, G., Tartaglione, E., Nguyen, VT. (2025). Mental State Classification Using EEG Signals: Ethics, Law and Challenges. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2133. Springer, Cham. https://doi.org/10.1007/978-3-031-74630-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-74630-7_28

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