EEG channel and feature investigation in binary and multiple motor imagery task predictions.

Journal: Frontiers in human neuroscience
Published Date: Dec 17, 2024

Abstract

INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.

Authors

  • Murside Degirmenci
    Kutahya Vocational School, Kutahya Health Sciences University, Kutahya, Türkiye.
  • Yilmaz Kemal Yuce
    Department of Computer Engineering, Alanya Alaaddin Keykubat University, Antalya, Türkiye.
  • Matjaž Perc
    Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia.
  • Yalcin Isler
    Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Türkiye.

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