Gate-Layer Autoencoders with Application to Incomplete EEG Signal Recovery

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

View graph of relations

Author(s)

  • Heba El-Fiqi
  • Kathryn Kasmarik
  • Anastasios Bezerianos
  • Kay Chen Tan
  • Hussein A. Abbass

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIJCNN 2019. International Joint Conference on Neural Networks
PublisherIEEE
ISBN (Electronic)978-1-7281-1985-4
ISBN (Print)978-1-7281-1986-1
Publication statusPublished - Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Title2019 International Joint Conference on Neural Networks, IJCNN 2019
LocationInterContinental Budapest
PlaceHungary
CityBudapest
Period14 - 19 July 2019

Abstract

Autoencoders (AE) have been used successfully as unsupervised learners for inferring latent information, learning hidden features and reducing the dimensionality of the data. In this paper, we propose a new AE architecture: Gate-Layer AE (GLAE). The novelty of GLAE lies in its ability to encourage learning of the relationships among different input variables, which affords it with an inherent ability to recover missing variables from the available ones and to act as a concurrent multi-function approximator.

GLAE uses a network architecture that associates each input with a binary gate acting as a switch that turns on or off the flow to each input unit, while synchronising its action with data flow to the network. We test GLAE with different coding sizes and compare its performance against the Classic AE, Denoising AE and Variational AE. The evaluation uses Electroencephalograph (EEG) data with an aim to reconstruct the EEG signal when some data are missing. The results demonstrate GLAE's superior performance in reconstructing EEG signals with up to 25% missing data in an input stream.

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Gate-Layer Autoencoders with Application to Incomplete EEG Signal Recovery. / El-Fiqi, Heba; Kasmarik, Kathryn; Bezerianos, Anastasios; Tan, Kay Chen; Abbass, Hussein A.

IJCNN 2019. International Joint Conference on Neural Networks. IEEE, 2019. 8852101 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review