Expanding Semantic BCI for Low-Density EEG via Deep Learning

This study investigates the potential of Semantic Brain-Computer Interfaces (BCIs) using low- density electroencephalography (EEG) systems in conjunction with advanced deep learning models. By analyzing both reflexive and cognitive event-related potentials elicited by visual stimuli, the research a...

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Detalles Bibliográficos
Autores principales: Langone, Mila, Hadad, Santiago, Beade, Gonzalo
Formato: Proyecto final de grado
Lenguaje:Español
Publicado: Bioingeniería 2025
Materias:
BCI, EEG, ELECTROENCEFALOGRAFÍA, REDES NEURONALES, NEUROCIENCIA,
Acceso en línea:https://hdl.handle.net/20.500.14769/4974
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-4974
record_format dspace
spelling I32-R138-20.500.14769-49742025-08-29T07:30:57Z Expanding Semantic BCI for Low-Density EEG via Deep Learning Langone, Mila Hadad, Santiago Beade, Gonzalo BCI, EEG, ELECTROENCEFALOGRAFÍA, REDES NEURONALES, NEUROCIENCIA, This study investigates the potential of Semantic Brain-Computer Interfaces (BCIs) using low- density electroencephalography (EEG) systems in conjunction with advanced deep learning models. By analyzing both reflexive and cognitive event-related potentials elicited by visual stimuli, the research aims to develop effective methods for the semantic interpretation of brain signals using minimal electrode setups. Emphasizing the use of low-density EEG systems, this work demonstrates that high classification accuracy can be achieved even with limited equipment. Additionally, the study ensures that the deep learning model used, namely EEGNet, align with established physiological EEG knowledge by following procedures that validate the learned features against known EEG patterns. 2025-08-28T19:30:05Z 2025-08-28T19:30:05Z 2024-07 Proyecto final de grado https://hdl.handle.net/20.500.14769/4974 es application/pdf Bioingeniería
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Español
topic BCI, EEG, ELECTROENCEFALOGRAFÍA, REDES NEURONALES, NEUROCIENCIA,
spellingShingle BCI, EEG, ELECTROENCEFALOGRAFÍA, REDES NEURONALES, NEUROCIENCIA,
Langone, Mila
Hadad, Santiago
Beade, Gonzalo
Expanding Semantic BCI for Low-Density EEG via Deep Learning
topic_facet BCI, EEG, ELECTROENCEFALOGRAFÍA, REDES NEURONALES, NEUROCIENCIA,
description This study investigates the potential of Semantic Brain-Computer Interfaces (BCIs) using low- density electroencephalography (EEG) systems in conjunction with advanced deep learning models. By analyzing both reflexive and cognitive event-related potentials elicited by visual stimuli, the research aims to develop effective methods for the semantic interpretation of brain signals using minimal electrode setups. Emphasizing the use of low-density EEG systems, this work demonstrates that high classification accuracy can be achieved even with limited equipment. Additionally, the study ensures that the deep learning model used, namely EEGNet, align with established physiological EEG knowledge by following procedures that validate the learned features against known EEG patterns.
format Proyecto final de grado
author Langone, Mila
Hadad, Santiago
Beade, Gonzalo
author_facet Langone, Mila
Hadad, Santiago
Beade, Gonzalo
author_sort Langone, Mila
title Expanding Semantic BCI for Low-Density EEG via Deep Learning
title_short Expanding Semantic BCI for Low-Density EEG via Deep Learning
title_full Expanding Semantic BCI for Low-Density EEG via Deep Learning
title_fullStr Expanding Semantic BCI for Low-Density EEG via Deep Learning
title_full_unstemmed Expanding Semantic BCI for Low-Density EEG via Deep Learning
title_sort expanding semantic bci for low-density eeg via deep learning
publisher Bioingeniería
publishDate 2025
url https://hdl.handle.net/20.500.14769/4974
work_keys_str_mv AT langonemila expandingsemanticbciforlowdensityeegviadeeplearning
AT hadadsantiago expandingsemanticbciforlowdensityeegviadeeplearning
AT beadegonzalo expandingsemanticbciforlowdensityeegviadeeplearning
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