Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?
Neural Networks - Special issue on the global brain: imaging and modelling
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Signal Classification through Multifractal Analysis and Complex Domain Neural Networks
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Feedforward Neural Network Construction Using Cross Validation
Neural Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A local neural classifier for the recognition of EEG patterns associated to mental tasks
IEEE Transactions on Neural Networks
Mental Tasks Classification for a Noninvasive BCI Application
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data
Pattern Recognition Letters
| Hi-index | 0.00 |
In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21–32 years, volunteered to imagine left-and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.