DocumentCode :
3599868
Title :
A new time-frequency method for EEG artifacts removing
Author :
Jiawei Wang ; Fei Su
Author_Institution :
Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2014
Firstpage :
341
Lastpage :
346
Abstract :
Previous studies have proved the reliability of using EEG signal as a biometric modality. Nowadays, some portable EEG recording systems are emerged as the peripheral devices to allow people use their EEG to play computer games or control toys, and it was also demonstrated that the single-channel EEG signals recorded by the portable equipment can be used for personal identification. However, unlike multi-electrodes devices for medical use, this kind of portable device will amplify noises introducing by power line interference, poor connection of electrode and eyeball movements. So the key point becomes how to effectively remove artifacts and maximally preserve neural activity to reduce adverse effects on EEG identification system. In this paper, a new time-frequency EEG artifacts removing method is proposed. Experimental results show the better performance of the proposed one comparing with the common used EEMD-ICA method.
Keywords :
biometrics (access control); electroencephalography; independent component analysis; medical signal processing; reliability; time-frequency analysis; EEG identification system; EEG signal reliability; EEMD-ICA method; biometric modality; ensemble empirical-mode decomposition; eyeball movements; independent component analysis; multielectrode devices; neural activity; peripheral devices; personal identification; portable EEG recording systems; power line interference; single-channel EEG signals; time-frequency EEG artifact removal method; Discrete wavelet transforms; Electroencephalography; SONOS devices; EEG; Ensemble empirical-mode decomposition (EEMD); Independent component analysis (ICA); Personal identification; Stationary wavelet transformation (SWT);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on
Print_ISBN :
978-1-4799-4720-1
Type :
conf
DOI :
10.1109/CCIS.2014.7175756
Filename :
7175756
Link To Document :
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