Enhancing EEG-based brain-computer interfaces through multitask learning

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Rodrigues, Renato Botter Maio Lopes; Fantinato, Denis Gustavo, 2026, "Enhancing EEG-based brain-computer interfaces through multitask learning", https://doi.org/10.25824/redu/ZKBLY2, Repositório de Dados de Pesquisa da Unicamp, V1

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Description	Electroencephalography (EEG) is a method that allows the measurement of brain activity with high-temporal resolution in a continuous and portable way. It could be used in a wide range of use-cases, from medical clinical applications to the development of Brain-Computer Interface (BCI) technologies. When developing BCI systems, it is crucial to identify meaningful attributes from the EEG signals that could be used to train reliable classifiers, and to accurately decode brain signals to control external devices. Multitask Learning (MTL), a machine learning technique, can be employed to address this challenge by simultaneously denoising EEG signals and learning latent representations in order to train a classifier to predict diferent brain states. In light of this, the present research project aims to explore the usage of MTL in the context of an EEG-based BCI system, with the goal of improving the accuracy and reliability of decoding brain signals during motor imagery tasks. (2025-07-15)
Subject	Engineering
Keyword	Deep learning, Brain-computer interface, Motor imagery
Related Publication	RODRIGUES, Renato Botter Maio Lopes; FANTINATO, Denis Gustavo. Evaluating multitask learning for EEG denoising and classification using synthetic data. In: ANAIS do XVII Congresso Brasileiro de Inteligência Computacional. Belo Horizonte, MG, Sociedade Brasileira de Inteligência Computacional (SBIC), 2025. Disponível em: https://sbia.org.br/eventos/cbic_2025/cbic2025-1156806/
Notes	Due to intellectual property restrictions, the data and code cannot be shared. For additional details, please reach out to the repository’s point of contact.
License/Data Use Agreement	CC BY-NC 4.0


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Citation Metadata

Persistent Identifier	doi:10.25824/redu/ZKBLY2
Publication Date	2026-03-09
Title	Enhancing EEG-based brain-computer interfaces through multitask learning
Author	Rodrigues, Renato Botter Maio Lopes (Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação.) - ORCID: https://orcid.org/0009-0008-5023-3802 Fantinato, Denis Gustavo (Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação.) - ORCID: https://orcid.org/0000-0002-5009-3431
Point of Contact	Use email button above to contact. Fantinato, Denis Gustavo (Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação.)
Description	Electroencephalography (EEG) is a method that allows the measurement of brain activity with high-temporal resolution in a continuous and portable way. It could be used in a wide range of use-cases, from medical clinical applications to the development of Brain-Computer Interface (BCI) technologies. When developing BCI systems, it is crucial to identify meaningful attributes from the EEG signals that could be used to train reliable classifiers, and to accurately decode brain signals to control external devices. Multitask Learning (MTL), a machine learning technique, can be employed to address this challenge by simultaneously denoising EEG signals and learning latent representations in order to train a classifier to predict diferent brain states. In light of this, the present research project aims to explore the usage of MTL in the context of an EEG-based BCI system, with the goal of improving the accuracy and reliability of decoding brain signals during motor imagery tasks. (2025-07-15)
Subject	Engineering
Keyword	Deep learning Brain-computer interface Motor imagery
Related Publication	RODRIGUES, Renato Botter Maio Lopes; FANTINATO, Denis Gustavo. Evaluating multitask learning for EEG denoising and classification using synthetic data. In: ANAIS do XVII Congresso Brasileiro de Inteligência Computacional. Belo Horizonte, MG, Sociedade Brasileira de Inteligência Computacional (SBIC), 2025. Disponível em: https://sbia.org.br/eventos/cbic_2025/cbic2025-1156806/ handle: https://hdl.handle.net/20.500.12733/41749
Notes	Due to intellectual property restrictions, the data and code cannot be shared. For additional details, please reach out to the repository’s point of contact.
Funding Information	No Funder: 0000
Depositor	Rodrigues, Renato Botter Maio Lopes
Deposit Date	2025-09-18
Declarações obrigatórias sobre ética e privacidade	o projeto que gerou os dados foi aprovado pelo Comite de Ética em Pesquisa da Unicamp ou não envolve questões que requeiram tal aprovação; os dados que serão depositados estão de acordo com a LGPD (Lei Geral de Proteção de Dados)

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