Publication: Classification of motor imagery eeg signals for using in neuro-rehabilitation applications
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Date
2022-10-21
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Abstract
Brain computer interfaces which are developed
for the rehabilitation systems decode motor imagery EEG
signals to control external devices. However, the extraction of
the features from the EEG imagery signals and classification of
it is an important problem. In this paper, common spatial
pattern analysis, which is widely used in motor image
applications, was preferred for getting features.
As a classifier, the accuracy performances of Artificial
neural network (%93), Convolutional Neural Network (%91),
Support Vector Machine (%84) and K- Nearest Neighbour
Algorithm (%90) were compared. As a result of the comparison,
Artificial Neural Network method was the most successful
classifier with %93.9 accuracy.
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Keywords
Motor Imagery EEG, common spatial pattern, deep learning, machine learning, classification
Citation
BAŞPINAR U., Taştan Y., Bulut Okay B., \"Classification of Motor Imagery EEG Signals for Using in Neuro-Rehabilitation Applications\", 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Türkiye, 07 Eylül 2022