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EEG Signal Analysis for Numerical Digit Classification: Methodologies and Challenges

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  • Augoustos Tsamourgelis

    (School of Science and Technology, Hellenic Open University, 263 31 Patras, Greece
    Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 691 00 Alexandroupolis, Greece)

  • Adam Adamopoulos

    (School of Science and Technology, Hellenic Open University, 263 31 Patras, Greece
    Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 691 00 Alexandroupolis, Greece)

Abstract

Electroencephalography (EEG) has existed since the early 20th century. It has proven to be a vital tool for electrophysiological studies of conditions like epilepsy. Recently, it has been revitalized as the field of machine learning has been developing, widening its usefulness among a plethora of neurological conditions and in brain–computer interface (BCI) applications. This study delves into the intricate process of classifying EEG signals elicited by the visual stimuli of subjects viewing the digits 0 and 1 and a blank screen. We focus on developing a comprehensive workflow for EEG preprocessing, as well as feature extraction and signal classification. We achieve strong differentiation capabilities between digit and non-digit values in all classification algorithms. However, our study also highlights the profound neurological challenges encountered in distinguishing between the digit values, as our model, inspired by the related bibliography, was unable to differentiate between digit values 0 and 1. These findings underscore the complexity of numerical processing in the brain, revealing critical insights into the limitations and potential of EEG-based digit classification and the need for clarity in the bioinformatics community.

Suggested Citation

  • Augoustos Tsamourgelis & Adam Adamopoulos, 2025. "EEG Signal Analysis for Numerical Digit Classification: Methodologies and Challenges," Stats, MDPI, vol. 8(1), pages 1-23, February.
  • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:14-:d:1584145
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