Enhancing brain decoding using attention augmented deep neural networks

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

doi:10.14428/esann/2021.ES2021-67

Enhancing brain decoding using attention augmented deep neural networks

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

Sub Algorithmic Data Analysis

Maastricht University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	183-188
Number of pages	6
ISBN (Electronic)	9782875870827
DOIs	https://doi.org/10.14428/esann/2021.ES2021-67
Publication status	Published - 2021
Event	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 - Virtual, Online, Belgium Duration: 6 Oct 2021 → 8 Oct 2021

Conference

Conference	29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021
Country/Territory	Belgium
City	Virtual, Online
Period	6/10/21 → 8/10/21

Bibliographical note

Publisher Copyright:
© 2021 ESANN Intelligence and Machine Learning. All rights reserved.

Access to Document

10.14428/esann/2021.ES2021-67Licence: Unspecified

ES2021-67Final published version, 1.95 MBLicence: Unspecified

Cite this

@inproceedings{803a0a84f2fd4a42be31147ee3eeb0e2,

title = "Enhancing brain decoding using attention augmented deep neural networks",

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.",

author = "Abdellaoui, {Ismail Alaoui} and Fern{\'a}ndez, {Jes{\'u}s Garc{\'i}a} and Caner Sahinli and Siamak Mehrkanoon",

note = "Publisher Copyright: {\textcopyright} 2021 ESANN Intelligence and Machine Learning. All rights reserved.; 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021 ; Conference date: 06-10-2021 Through 08-10-2021",

year = "2021",

doi = "10.14428/esann/2021.ES2021-67",

language = "English",

pages = "183--188",

booktitle = "ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "i6doc.com publication",

}

Abdellaoui, IA, Fernández, JG, Sahinli, C & Mehrkanoon, S 2021, Enhancing brain decoding using attention augmented deep neural networks. in ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, pp. 183-188, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Virtual, Online, Belgium, 6/10/21. https://doi.org/10.14428/esann/2021.ES2021-67

Enhancing brain decoding using attention augmented deep neural networks. / Abdellaoui, Ismail Alaoui; Fernández, Jesús García; Sahinli, Caner et al.
ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2021. p. 183-188.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Enhancing brain decoding using attention augmented deep neural networks

AU - Abdellaoui, Ismail Alaoui

AU - Fernández, Jesús García

AU - Sahinli, Caner

AU - Mehrkanoon, Siamak

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85120987604&partnerID=8YFLogxK

U2 - 10.14428/esann/2021.ES2021-67

DO - 10.14428/esann/2021.ES2021-67

M3 - Conference contribution

AN - SCOPUS:85120987604

SP - 183

EP - 188

BT - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - i6doc.com publication

T2 - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

Y2 - 6 October 2021 through 8 October 2021

ER -

Enhancing brain decoding using attention augmented deep neural networks

Abstract

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this