Machine learning approaches for eeg signal classification: Algorithms and applications

Authors

  • Aurela Qamili
  • Jozef Kola

DOI:

https://doi.org/10.55312/op.vi1.5889

Abstract

The fusion of artificial intelligence (AI) with medical science, particularly through the analysis of Electroencephalography (EEG) data, represents a significant leap toward understanding and interpreting the complexities of human brain activity. The utility of EEG data spans various domains, including, but not limited to, neurology, psychiatry, and brain-computer interfaces (BCI), offering a non-invasive peek into the electrical activities of the brain with implications for diagnostics, therapeutic interventions, and the development of assistive technologies. With the advent of sophisticated machine learning (ML) and deep learning (DL) techniques, the potential to decode and classify these signals has grown exponentially, promising breakthroughs in medical science, neuropsychology, and beyond. This review systematically explores the breadth of machine learning methodologies applied to EEG signal classification, scrutinizing both traditional ML algorithms and advanced DL models. Our study compiles and assesses various algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), among others, for their efficacy in accurately classifying EEG signals for a multitude of applications ranging from seizure detection to mental state analysis. We delve into the technical nuances of these algorithms, highlighting their strengths, limitations, and the specific EEG datasets they have been applied. This review article is poised to serve as an invaluable resource for researchers and practitioners in the fields of neuroscience, artificial intelligence, and beyond, offering insights into the intricate relation between brain activity and machine learning.

Keywords:

Inteligjenca Artificiale, EEG, marrja e të dhënave, algoritmet e klasifikimit, trendet në zhvillim.

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Published

2024-11-24

How to Cite

Qamili, A., & Kola, J. (2024). Machine learning approaches for eeg signal classification: Algorithms and applications. Optime, (1), 273–284. https://doi.org/10.55312/op.vi1.5889

Issue

No. 1 (2024): Optime

Section

Articles