Multilayer feedforward networks are universal approximators
Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brain inspired cognitive system for learning and memory
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Associative memory model of hippocampus CA3 using spike response neurons
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Hi-index | 0.00 |
Neural networks (NNs) can be deployed in many different ways in signal processing applications. This paper illustrates how neural networks are employed in a prediction based preprocessing framework, referred to as neural-time-series-prediction-preprocessing (NTSPP), in an electroencephalogram (EEG)-based brain-computer interface (BCI). NTSPP has been shown to increase feature separability by mapping the original EEG signals via time-series-prediction to a higher dimensional space. Preliminary results of a similar novel framework are also presented where, instead of using predictive NNs, auto-associative NNs are employed and features are extracted from the output of auto-associative NNs trained to specialize on EEG signals for particular brain states. The results show that this preprocessing framework referred to as auto-associative NN preprocessing (ANNP) also has the potential to improve the performance of BCIs. Both the NTSPP and ANNP are compared with and deployed in conjunction with the well know common spatial patterns (CSP) to produce a BCI system which significantly outperforms either approach operating independently and has the potential to produce good performances even with a lower number of EEG channels compared to a multichannel BCI. Multichannel BCIs normally perform better that 2-3 channel BCIs however reducing the number of EEG channels required can positively impact on the time needed to mount electrodes and minimize the obtrusiveness of the electrode montage for the user. It is also shown that NTSPP can improve the potential for employing existing BCI methods with minimal subject-specific parameter tuning to deploy the BCI autonomously. Results are presented with six different classification approaches including various statistical classifiers such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and a Bayes classifier.