Detection of Epileptic Seizures with Tangent Space Mapping Features of EEG Signals

Detection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many p...

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Bibliographic Details
Main Authors: Altindis, Fatih, Yilmaz, Bulent
Format: Conference Proceeding
Language:English
Subjects:
Brain-computer interfaces
EEG
Electroencephalography
Feature extraction
Machine learning
Riemannian geometry
Seizure detection
Signal processing
Tangent space mapping
Tuning
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Summary:Detection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.
ISSN:2687-7783
DOI:10.1109/TIPTEKNO53239.2021.9632959