Machine learning for brain signal analysis

Authors

  • Ainur S. Makhmet Al Farabi Kazakh National University
  • Maxim G. Sharaev Skolkovo Institute of Science and Technology
  • Anuar E. Dyusembaev Al Farabi Kazakh National University
  • Almira M Kustubayeva Head of the Department of Biophisics, Biomedicine and Neuroscience

DOI:

https://doi.org/10.26577/ijbch.2021.v14.i2.01

Abstract

Machine learning is an effective tool for analyzing signals from the human brain. Machine Learning techniques provide new insight into the understanding of brain function in healthy subjects and patients with neurological and mental disorders. Here we introduce the application of machine learning to brain signal analysis, specifically using two widely used brain signal collection methods: functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG). The article provides a brief overview of the theoretical concept of machine learning and its types: supervised, unsupervised and reinforcement learning. The potential of machine learning applications in pathology is discussed. Differences between EEG and fMRI methods regarding machine learning application and an overview of the techniques employed in different research studies are reviewed.  The new machine learning methods invented for analysis of brain signals in the resting state and during the performance of the different cognitive tasks would be useful and worth considering in other domains, not limited to medicine.

Keywords: EEG, fMRI, machine learning, MVPA, brain signal analysis.

 

Author Biographies

Ainur S. Makhmet, Al Farabi Kazakh National University

Department of Atificial Intellegence and Big Data

Anuar E. Dyusembaev, Al Farabi Kazakh National University

Department of Artificial Intellegence and Big Data, professor

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How to Cite

Makhmet, Ainur S., Maxim G. Sharaev, Anuar E. Dyusembaev, and Almira M Kustubayeva. 2021. “Machine Learning for Brain Signal Analysis”. International Journal of Biology and Chemistry 14 (2):4-11. https://doi.org/10.26577/ijbch.2021.v14.i2.01.