Classification of EEG signals using the wavelet transform
Signal Processing
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
International Journal of Mobile Learning and Organisation
| Hi-index | 12.05 |
In this paper, we investigate the potentials of applying a kernel-based learning machine, the Relevance Vector Machine (RVM), to the task of epilepsy detection through automatic electroencephalogram (EEG) signal classification. For this purpose, some experiments have been conducted over publicly available data, contrasting the performance levels exhibited by RVM models with those achieved with Support Vector Machines (SVMs), both in terms of predictive accuracy and sensitivity to the choice of the kernel function. Four settings of both types of kernel machine were considered in this study, which vary in accord with the type of input data they receive, either raw EEG signal or some statistical features extracted from the wavelet-transformed data. The empirical results indicate that: (1) in terms of accuracy, the best-calibrated RVM models have shown very satisfactory performance levels, which are rather comparable to those of SVMs; (2) an increase of accuracy is sometimes accompanied by loss of sparseness in the resulting RVM models; (3) both types of machines present similar sensitivity profiles to the kernel functions considered, having some kernel parameter values clearly associated with better accuracy rate; (4) when not making use of a feature extraction technique, the choice of the kernel function seems to be very relevant for significantly leveraging the performance of RVMs; and (5) when making use of derived features, the choice of the feature extraction technique seems to be an important factor to one take into account.