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Challenges in Electroencephalography Data Processing Using Machine Learning Approaches

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Databases Theory and Applications (ADC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13459))

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Abstract

The future of neuro-science lies in Electroencephalography (EEG). EEG is the latest gold standard for diagnosing most neurological disorders like dementia, mild cognitive impairment (MCI), Alzheimer’s diseases, and so on. It is a cheap, portable and non-invasive option to discover neuro-disorders compared to the remaining expensive and time consuming options like computed tomography (CT) scan, positron emission tomography (PET), mini-mental state examination, and magnetic resonance imaging (MRI). Though EEG sounds promising option, but there are some challenges involved in EEG signal processing starting from EEG signal recording till disease classification. This study has reported all the challenges related to the detection of neuro-diseases from EEG data. This study will guide future EEG and neuro-disease investigators to be more attentive to the reported challenges and obstacles, which will ensure smooth and accurate neuro-disease detection models.

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Authors and Affiliations

  1. Victoria University, Melbourne, VIC, 3011, Australia

    Ashik Mostafa Alvi, Siuly Siuly & Hua Wang

Authors
  1. Ashik Mostafa Alvi
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  2. Siuly Siuly
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  3. Hua Wang
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Corresponding author

Correspondence to Ashik Mostafa Alvi .

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Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Wen Hua

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

    Lei Li

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Alvi, A.M., Siuly, S., Wang, H. (2022). Challenges in Electroencephalography Data Processing Using Machine Learning Approaches. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-15512-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15511-6

  • Online ISBN: 978-3-031-15512-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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