Automated eye blink artefact removal from EEG using support vector machine and autoencoder
Author:
Affiliation:
1. Department of Computer Science and EngineeringNIT SilcharSilcharAssamIndia
2. Department of Electrical EngineeringNIT SilcharSilcharAssamIndia
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-spr.2018.5111
Reference29 articles.
1. Bhattacharyya A. Gupta V. Pachori R.B.: ‘Automated identification of epileptic seizure EEG signals using empirical wavelet transform based Hilbert marginal spectrum’.22nd Int. Conf. Digital Signal Processing (DSP) London 2017 pp.1–5
2. A novel approach for automated detection of focal EEG signals using empirical wavelet transform
3. A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform
4. Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis
5. Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
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