Enhancing brain decoding using attention augmented deep neural networks

Ismail Alaoui Abdellaoui, Jesús García Fernández, Caner Sahinli, Siamak Mehrkanoon

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Neuroimaging techniques have shown to be valuable when studying brain activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), and different deep learning models to perform brain decoding. Specifically, we investigate to which extent one can infer the task performed by a subject based on its MEG data. In order to capture the most relevant features of the signals, self and global attention are incorporated into our models. The obtained results show that the inclusion of attention improves the performance and generalization of the models across subjects.

Original languageEnglish
Title of host publicationESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages183-188
Number of pages6
ISBN (Electronic)9782875870827
DOIs
Publication statusPublished - 2021
Event29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 - Virtual, Online, Belgium
Duration: 6 Oct 20218 Oct 2021

Conference

Conference29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021
Country/TerritoryBelgium
CityVirtual, Online
Period6/10/218/10/21

Fingerprint

Dive into the research topics of 'Enhancing brain decoding using attention augmented deep neural networks'. Together they form a unique fingerprint.

Cite this