A comparative study of wavelet families for EEG signal classification

By Tapan Kumar Gandhi, Bijaya Ketan Panigrahi, Sneh Anand. Neurocomputing, 2011

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7812dca3d6b858d9f2b5910914b85c20ca361c07

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Muhammad Hussain
2. Ihsan Ullah
3. Hatim Aboalsamh
4. Emad-ul-Haq Qazi

In Cognitive Neuroscience

1. C. Wilkinson
2. A. Levin
3. L. J. Gabard-Durnam
4. Adriana S Méndez Leal

In Signal Processing

1. C. Wilkinson
2. Muhammad Hussain
3. Ihsan Ullah
4. Hatim Aboalsamh
5. A. Levin
6. L. J. Gabard-Durnam
7. Adriana S Méndez Leal
8. Emad-ul-Haq Qazi

Citations in the corpus

• The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “The Coiflets wavelet family was selected because it has been found to provide optimal extraction of neural from artifact signal across both typical and epileptic EEG recordings (Gandhi et al., 2011).”
• An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, methodology): “Gandhi et al. (Gandhi et al., 2011) extracted the entropy, standard deviation and energy features from EEG signals using DWT.”