Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces
- Resource Type
- Conference
- Authors
- Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, Kutay
- Source
- 2018 26th European Signal Processing Conference (EUSIPCO) Signal Processing Conference (EUSIPCO), 2018 26th European. :1695-1699 Sep, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Electroencephalography
Three-dimensional displays
Data analysis
Electrodes
Signal processing
Feature extraction
Shape
EEG
brain-computer interfaces
topological data analysis
motor intention waves
JPlex
- Language
- ISSN
- 2076-1465
This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on braincomputer 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.