TY - JOUR
T1 - An Improved Performance of Support Vector Machine to Classify EEG Motor Imagery based on Differential Asymmetry
AU - Putranto, Yulianto Tejo
AU - Putra, Oddy Virgantara
AU - Hafidz, Isa
AU - Sardjono, Tri Arief
AU - Hariadi, Mochamad
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2023 Wydawnictwo SIGMA-NOT. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Differential Asymmetry
KW - Discrete Wavelet Transform
KW - EEG Motor Imagery
KW - Empirical Mode Decomposition
UR - http://www.scopus.com/inward/record.url?scp=85164454926&partnerID=8YFLogxK
U2 - 10.15199/48.2023.06.40
DO - 10.15199/48.2023.06.40
M3 - Article
AN - SCOPUS:85164454926
SN - 0033-2097
VL - 2023
SP - 196
EP - 203
JO - Przeglad Elektrotechniczny
JF - Przeglad Elektrotechniczny
IS - 6
ER -