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Convolutional Neural Network-Based EEG Signal Analysis: A Systematic Review

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Abstract

The identification and classification of human brain activities are essential for many medical and Brain-Computer Interface (BCI) systems, saving human lives and time. Electroencephalogram (EEG) proves to be an efficient, non-invasive, and cost-effective means of recording electrical signals in the human brain. With the advancement in Artificial Intelligence, various techniques have emerged that provide efficient ways of classifying EEG signals to solve real-life challenges. One such method is Convolutional Neural Network (CNN), which has received considerable research attention. This paper presents a systematic review of CNN techniques for the identification and classification of EEG signals and their main achievements. The review has considered the most reliable studies from various fields and application domains where CNN has been used for EEG signal classification or identification. The review also highlights the approaches taken so far. While there are many available survey types of research, none has provided a comprehensive view of a particular model for EGG-signal analysis. This survey focuses on the successful deployment of CNN models in various application domains that use EEG signals. Additionally, this paper attempts to answer research questions and discusses current challenges. The presented detailed review strengthens the belief in the futuristic potential that CNN has in solving real-world problems using EEG signals. All of this indicates that CNN-based EEG-signal analysis is a promising field with exciting opportunities for research enthusiasts.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India

    Swati Rajwal & Swati Aggarwal

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  1. Swati Rajwal
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  2. Swati Aggarwal
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Correspondence to Swati Aggarwal.

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Rajwal, S., Aggarwal, S. Convolutional Neural Network-Based EEG Signal Analysis: A Systematic Review. Arch Computat Methods Eng 30, 3585–3615 (2023). https://doi.org/10.1007/s11831-023-09920-1

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