Title: A comprehensive review of electroencephalography data analytics

Authors: Marwa Saieed Khlief; Ali Kadhum Idrees

Addresses: Department of Computer Science, University of Babylon, Babylon, Iraq ' Department of Computer Science, University of Babylon, Babylon, Iraq

Abstract: This paper proposes a comprehensive review of Electroencephalography (EEG) data analytics. The EEG signal definition and the analysis process are presented. The public EEG data sets that were utilised by the researchers are explored. EEG data acquisition methods are investigated. This paper covers and summarises the work and techniques that have been done to compress EEG data. Significant approaches for feature extraction for EEG signal processing are illustrated. The collected features are then utilised to classify signals based on their properties. Machine learning techniques have become very important in this field in recent years because of their incredible ability to assess complicated volumes of data. Therefore, machine learning and deep learning for EEG data have been introduced. For researchers interested in EEG data analysis, this work can serve as a basic strategy and a roadmap.

Keywords: EEG; electroencephalography; EEG signal processing; data compression; machine learning; deep learning.

DOI: 10.1504/IJCAT.2023.131066

International Journal of Computer Applications in Technology, 2023 Vol.71 No.1, pp.78 - 88

Received: 23 Apr 2022
Received in revised form: 27 May 2022
Accepted: 15 Jun 2022
Published online: 23 May 2023 *

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