EEG signal classification using wavelet feature extraction and a mixture of expert model

By Abdulhamit Subasi. Expert systems with applications, 2007

1,251 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/750d83703552fab938b616c2722c290c88c2a29b

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. Dimitrios I. Fotiadis
2. Alexandros T. Tzallas
3. Markos G. Tsipouras
4. Amjed Subhi Al-Fahoum
5. Ausilah Alfraihat
6. Usha R. Acharya
7. M. Ismail Gursoy
8. Mahmut Ozer

In Cognitive Neuroscience

1. Usha R. Acharya
2. Bao-Liang Lu
3. Subbhuraam Vinitha Sree
4. Jasjit S. Suri
5. Roshan Joy Martis
6. Mahmut Ozer
7. Mahmut Hekim
8. Umut Orhan

In Signal Processing

1. Usha R. Acharya
2. Amjed Subhi Al-Fahoum
3. Ausilah Alfraihat
4. Jasjit S. Suri
5. Subbhuraam Vinitha Sree
6. Roshan Joy Martis
7. G. Swapna
8. Dimitrios I. Fotiadis

Citations in the corpus

• EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, methodology): “The number of decomposition levels is determined depending on the dominant frequency components of the signals (Adeli et al., 2003; Akay, 1997; Ocak, 2008; Soltani, 2002; Subasi, 2007).”
• The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery (2011, background): “Works in the field of audio analysis [8] and biological signals, such as EEG [9] and electrocardiograph signals [10], have provided exceptional results proving that wavelet based feature extraction possess great potential for on-line automated monitoring and control.”
• Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, neutral): “[28–30].”
• Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “Subasi (2005, 2007) decomposed the EEG signals into time–frequency representations using discrete wavelet transform (DWT).”
• Emotional state classification from EEG data using machine learning approach (2014, methodology): “This wavelet function was chosen due to its near optimal time–frequency location properties [50].”
• EEG Signal Analysis: A Survey (2010, methodology): “The discrete wavelet transform (DWT) coefficients of EEG signals were fed into a modular neural network structure (mixture of experts ’ network) for detecting epileptic EEG signals [83].”
• Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques (2015, background): “The high pass filter g(n) is the discrete mother wavelet and the low pass filter h(n) is its mirror version [33].”
• Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains (2014, methodology): “However, there are many sources of artifacts.”