Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Toward Brain-Computer Interfacing (Neural Information Processing)
Toward Brain-Computer Interfacing (Neural Information Processing)
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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A new convolutional neural network architecture is presented. It includes the fast Fourier transform between two hidden layers to switch the signal analysis from the time domain to the frequency domain inside the network. This technique allows the signal classification without any special pre-processing and uses knowledge from the problem in the network topology. The first step allows the creation of different spatial and time filters. The second step is dedicated to the signal transformation in the frequency domain. The last step is the classification. The system is tested offline on the classification of EEG signals that contain steady-state visual evoked potential (SSVEP) responses. The mean recognition rate of the classification of five different types of SSVEP response is 95.61% on a time segment length of 1s. The proposed strategy outperforms other classical neural network architecures.