EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

By P. Jahankhani, Vassilis S. Kodogiannis, Kenneth Revett. John Vincent Atanasoff Symposium, 2006

302 citations recorded by the source corpus.

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Automated EEG analysis of epilepsy: A review (2013, background): “Most of the methods fall under four broad categories: (1) time domain, (2) frequency domain, (3) time–frequency domain and (4) nonlinear methods.”
Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “Kalayci and Ozdamar (1995) used wavelet transform to capture characteristic features of the EEG signals and then combined with ANN to get satisfying classification result. Nigam and Graupe (2004) described a method for automated detection of epileptic seizures from EEG signals using a multistage non”
EEG Signal Analysis: A Survey (2010, methodology): “The EEG signals were decomposed into frequency sub bands using discrete wavelet transform and later fed to the neural network for classification into epileptic or normal signal [35].”
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques (2015, background): “The DWT is widely used for the time–frequency analysis of biomedical signals [2, 32], especially in an EEG signal analysis due to its non-stationary characteristics.”
A comparative study of wavelet families for EEG signal classification (2011, methodology): “Processing and analysis of bio-signals using software techniques have come into play since the early 1960s providing physicians with fast and accurate means toward more Q2 precise diagnosis [1].”