A Review on Deep Learning For Electroencephalogram Signal Classification


  • Sarah Kamil Gatfan Ministry of Education, General Administration of Al Qadisiyah Education, Al Diwaniyah 58001, Iraq




Artificial Intellegence, Convolutional Neural Network, Deep Learning, Electroencephalogram, Human Brain


 Recently, the research on Electroencephalogram (EEG) signals have been obtained more focus at the same time the EEG signal is regarded as the basis for the prediction of diagnosis disease and the brain behavior. EEG is as significant tool for many conditions that can be recorded the brain human waves which accommodate the brain activity. In the recent decades, EEG data has been extensively applied in the approaches of data analysis such as time series analysis. With the considerable achievement of deep learning (DL) implement on the time series data, multiple studies have been began applying deep learning algorithms on the processing of EEG signal. Several deep learning techniques that assistant in the detection various psycho-neuro disorders, have been proposed in order to automate EEG detection and classification with great development in multiple applications of EEG signals.  Also, different machine learning (ML) algorithms have been presented in such research for brain signals identification and classification in the era of Artificial intelligence (AI). In an attempt to summarize the EEG signal processing techniques, we have performed a literature review around deep learning algorithms for decoding the human’s brain activity as well as diagnosis disease and clarified particulars about several deep learning algorithms. We also conducted some of ML papers about EEG signals classification. Based on the achievement results of the research mentioned in this article appears an advanced scientific development in terms of deep learning.


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How to Cite

Kamil Gatfan, S. (2024). A Review on Deep Learning For Electroencephalogram Signal Classification. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(1), Comp. 137–151. https://doi.org/10.29304/jqcsm.2024.16.11453



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