A Convolutional Neural Network for Artifacts Detection in EEG Data

Amal Boudaya*, Siwar Chaabene, Bassem Bouaziz, Hadj Batatia, Hela Zouari, Sana ben Jemea, Lotfi Chaari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electroencephalography (EEG) is an effective tool for neurological disorders diagnosis such as seizures, chronic fatigue, sleep disorders, and behavioral abnormalities. Various artifacts types may impact EEG signals regardless the used, resulting in an erroneous diagnosis. Various data analysis tools have therefore been developed in the biomedical engineering literature to detect and/or remove these artifacts. In this sense, deep learning (DL) is one of the most promising methods. In this paper, we develop a novel method based on artifacts detection using a convolutional neural network (CNN) architecture. The available EEG data was collected using 32 channels from the Nihon Kohden Neurofax EEG-1200. The data are preprocessed and analyzed using our CNN to perform binary artifact detection. The suggested method highlights the best classification results with a maximal accuracy up to 99.20%.

Original languageEnglish
Title of host publicationProceedings of International Conference on Information Technology and Applications. ICITA 2021
EditorsAbrar Ullah, Steve Gill, Álvaro Rocha, Sajid Anwar
PublisherSpringer
Pages3-13
Number of pages11
ISBN (Electronic)9789811676185
ISBN (Print)9789811676178
DOIs
Publication statusPublished - 21 Apr 2022
Event15th International Conference on Information Technology and Applications 2021 - Dubai, United Arab Emirates
Duration: 13 Nov 202114 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
Volume350
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference15th International Conference on Information Technology and Applications 2021
Abbreviated titleICITA 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period13/11/2114/11/21

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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