One challenge in EEG motor imaging is th e low signal-to-noise ratio of brain signals. Its emergence in the accurate rendition of brain signals varies significantly from person to person. Here, we propose a framework to classify tasks based on fusion features using a Support Vector Machine. Our features are acquired from Discrete Wavelet Transform and Empirical Mode Decomposition. Subsequently, the disparity between measurements of left and right brain signals was calculated. Our proposed work significantly improves accuracy from 83.29 % to 93.16 % compared to previous work.

Original languageEnglish
Pages (from-to)196-203
Number of pages8
JournalPrzeglad Elektrotechniczny
Issue number6
Publication statusPublished - 2023


  • Differential Asymmetry
  • Discrete Wavelet Transform
  • EEG Motor Imagery
  • Empirical Mode Decomposition


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