Wavelet/mixture of experts network structure for EEG signals classification

Authors:
Elif Derya íbeyli
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji íniversitesi, 06530 Söğütözü, Ankara, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 9
Cited 19

Hierarchical mixtures of experts and the EM algorithm

Neural Computation
Wavelet applications in medicine

IEEE Spectrum
Classification of EEG signals using the wavelet transform

Signal Processing
Detection of spikes with artificial neural networks using raw EEG

Computers and Biomedical Research
An improved neural classification network for the two-group problem

Computers and Operations Research
Improved learning algorithms for mixture of experts in multiclass classification

Neural Networks
A Mixture of Experts Network Structure Construction Algorithm for Modelling and Control

Applied Intelligence
Adaptive mixtures of local experts

Neural Computation
The wavelet transform, time-frequency localization and signal analysis

IEEE Transactions on Information Theory

Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States

Journal of Medical Systems
Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals

Neural Networks
Features for analysis of electrocardiographic changes in partial epileptic patients

Expert Systems with Applications: An International Journal
Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines

Expert Systems with Applications: An International Journal
Modified Mixture of Experts for Diabetes Diagnosis

Journal of Medical Systems
Statistics over features: EEG signals analysis

Computers in Biology and Medicine
Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

Expert Systems with Applications: An International Journal
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals

Expert Systems with Applications: An International Journal
Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Analysis of sleep EEG activity during hypopnoea episodes by least squares support vector machine employing AR coefficients

Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes

Digital Signal Processing
Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study

Computers in Biology and Medicine
Enhancing hand gesture recognition using fuzzy clustering-based mixture-of-experts model

Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Weighted dynamic time warping for time series classification

Pattern Recognition
Global/local hybrid learning of mixture-of-experts from labeled and unlabeled data

HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Clustering technique-based least square support vector machine for EEG signal classification

Computer Methods and Programs in Biomedicine
Adaptive mixture-of-experts models for data glove interface with multiple users

Expert Systems with Applications: An International Journal
Multi-feature Characterization of Epileptic Activity for Construction of an Automated Internet-based Annotated Classification

Journal of Medical Systems
Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data

Neurocomputing

Quantified Score

Hi-index	12.06

Visualization

Abstract

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide model selection for classification of electroencephalogram (EEG) signals. Expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.