Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.

By Inan Güler, Elif Derya Übeyli. Journal of Neuroscience Methods, 2005

587 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f33b5a71ce806ab24ed74a19184069b92ce0b552

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Citations in the corpus

EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, unknown): “…Koklukaya, & Subasi, 2005; Subasi, 2005a, 2005b, 2007; Subasi & Ercelebi, 2005; Ubeyli, 2009), adaptive neuro-fuzzy inference system (ANFIS) (Guler & Ubeyli, 2005), radial basis function neural network (RBFNN) (Aslan, Bozdemir, Sahin, Ogulata, & Erol, 2008), recurrent neural network (RNN)…”
Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains (2014, unknown): “Feature extraction scheme is meant to choose the features or informationwhich is themost important for classification exercise [15–17].”
COVID-19 Outbreak Prediction with Machine Learning (2020, background): “The idea of this model is to build a neural network whose outputs are a degree of the input that belongs to each class [91-93].”
Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform (2007, background): “There is a strong demand for the development of such automated devices, due to the increased use of prolonged and long-term video EEG recordings for proper evaluation and treatment of neurological diseases and prevention of the possibility of the analyst missing (or misreading) information [12,14].”
Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. (2010, methodology): “The multiresolution feature of theWT allows the decomposition of a signal into several sub-signals, each sub-signal representing a particular coarseness of the signal under study (Güler and Übeyli, 2005).”
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform (2015, background): “The feature vector consists of the maximum, minimum, average, and standard deviation of the wavelet coefficients of each subband [34], [35].”
A new approach to intelligent fault diagnosis of rotating machinery (2008, methodology): “Successful implementations of ANFIS have been reported in the medical field (Güler & Übeyli, 2005; Übeyli & Güler, 2005), chemical field (Gulbag & Temurtas, 2006; Lo & Lin, 2005) and fault diagnosis (Lou & Loparo, 2004; Ye, Sadeghian, & Wu, 2006).”
Applications of neuro fuzzy systems: A brief review and future outline (2014, methodology): “[15] proposed adaptive neuro fuzzy inference system (ANFIS) model for classification of electro encephalogram (EEG) signals, which was clinically used to investigate brain disorder.”