TY - GEN
T1 - Mental State Classification Using EEG Signals
T2 - 23rd Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023
AU - Nahon, Rémi
AU - Bagheri, Nasim
AU - Varni, Giovanna
AU - Tartaglione, Enzo
AU - Nguyen, Van Tam
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - 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.
AB - 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.
KW - AI Act
KW - EEG
KW - GDPR
KW - Mental state classification
KW - Tiny ML
UR - https://www.scopus.com/pages/publications/85219186570
U2 - 10.1007/978-3-031-74630-7_28
DO - 10.1007/978-3-031-74630-7_28
M3 - Conference contribution
AN - SCOPUS:85219186570
SN - 9783031746291
T3 - Communications in Computer and Information Science
SP - 401
EP - 419
BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers
A2 - Meo, Rosa
A2 - Silvestri, Fabrizio
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2023 through 22 September 2023
ER -