A new non-iterative approach for clustering
Pattern Recognition Letters
Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network
Expert Systems with Applications: An International Journal
Features for analysis of electrocardiographic changes in partial epileptic patients
Expert Systems with Applications: An International Journal
An efficient classifier to diagnose of schizophrenia based on the EEG signals
Expert Systems with Applications: An International Journal
Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Hi-index | 12.05 |
An electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed in this study. Unsupervised fuzzy Hopfield neural network (FHNN) clustering, together with active segment selection and multiresolution fractal features, is used in the classification of left and right MI data. Active segment selection is used to obtain the active segment in the time-scale domain with the continuous wavelet transform (CWT) and Student's two-sample t-statistics. The multiresolution fractal features are then extracted from the discrete wavelet transform (DWT) data by using the modified fractal dimension. Finally, FHNN clustering is used as the discriminant of multiresolution fractal features. FHNN clustering is capable of making flexible partitions of a finite data set, and it is an unsupervised and robust approach suitable for the classification of non-stationary biomedical signals. Compared with several popular supervised classifiers, FHNN clustering achieves promising results in classification accuracy.