Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces. Altindis, F., Yilmaz, B., Borisenok, S., & Icoz, K. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 1695-1699, Sep., 2018.
Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces [pdf]Paper  doi  abstract   bibtex   
This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions.

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