Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications
Neural Networks
Independent component analysis for noisy data: MEG data analysis
Neural Networks
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection
Computers in Biology and Medicine
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent Component Analysis Aided Diagnosis of Cuban Spino Cerebellar Ataxia 2
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Computers in Biology and Medicine
Paraconsistent artificial neural networks and EEG analysis
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Expert Systems with Applications: An International Journal
Applications of paraconsistent artificial neural networks in EEG
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Paraconsistent artificial neural networks and EEG
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers of KES2012-Part 2 of 2
Hi-index | 0.00 |
Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.