A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals-Reference-Cited by-同舟云学术

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A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals

Published:2015-11-27 Issue:2 Volume:29 Page:207-217
ISSN:0896-0267
Container-title:Brain Topography
language:en
Short-container-title:Brain Topogr

Author:

Amin Hafeez Ullah,Malik Aamir Saeed,Kamel Nidal,Hussain Muhammad

Funder

This research work was supported by the HICoE grant for CISIR, Ministry of Education (MOE), Malaysia

NSTIP strategic technologies programs in the Kingdom of Saudi Arabia

Publisher

Springer Science and Business Media LLC

Subject

Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Link

http://link.springer.com/content/pdf/10.1007/s10548-015-0462-2.pdf

Reference50 articles.

1. Acharya UR, Sree SV, Ang PCA, Yanti R, Suri JS (2012a) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22:1250002

2. Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng K-H, Suri JS (2012b) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7:401–408

3. Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123:69–87

4. Ahammad N, Fathima T, Joseph P (2014) Detection of epileptic seizure event and onset using EEG. BioMed Res Int 2014

5. Amin HU, Malik AS, Subhani AR, Badruddin N, Chooi W-T (2013) Dynamics of scalp potential and autonomic nerve activity during intelligence test. In: Lee M et al (eds) Neural Information Processing, vol 8226. Springer, Berlin, pp 9–16

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