Recurrent neural networks employing Lyapunov exponents for EEG signals classification

By Nihal Fatma Güler, Elif Derya Übeyli, Inan Güler. Expert systems with applications, 2005

535 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3263546c95177b29d5f5d4af8344d13dc5d0e34a

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. Samanwoy Ghosh-Dastidar
2. Hojjat Adeli
3. Dimitrios I. Fotiadis
4. Alexandros T. Tzallas
5. Markos G. Tsipouras
6. Nahid Dadmehr
7. Alejandro Pazos
8. Julian Dorado

In Cognitive Neuroscience

1. Hojjat Adeli
2. Samanwoy Ghosh-Dastidar
3. Nahid Dadmehr
4. Usha R. Acharya
5. Dimitrios I. Fotiadis
6. Alexandros T. Tzallas
7. Markos G. Tsipouras
8. Alejandro Pazos

Citations in the corpus

• Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “Artificial neural networks have been used as the most common lassifier for classifying EEGs (NigamandGraupe, 2004; Jahankhani t al., 2006; Güler et al., 2005; Übeyli, 2006a).”
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks (2015, methodology): “In other works, convolutional and recurrent neural networks have been used to extract representations from EEG time series (Mirowski et al., 2009; Cecotti & Gr¨aser, 2011; Guler et al., 2005).”
• Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection (2008, methodology): “based classifiers have also been employed in the nascent field of automated EEG analysis and epilepsy diagnosis [10], [12], [16], [17].”
• Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, methodology): “Principal component analysis (PCA), using 1% threshold, was employed to reduce the dimension of the feature set [44] resulting in three to four features in all cases.”
• Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks (2007, methodology): “These features can then be used to classify the EEG signal using statistical methods [28–30], nearest neighbour classifiers [36], decision trees [16], ANNs [21, 34], support vector machines (SVMs) [18, 37], or adaptive neurofuzzy inference systems [23, 35] in order to identify the occurrence of seiz”
• Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. (2010, background): “, 2005) such as correlation dimension (Lerner, 1996), Lyapunov exponents (Güler et al., 2005), and entropy (Kannathal et al.”