期刊
PATTERN RECOGNITION LETTERS
卷 32, 期 8, 页码 1145-1153出版社
ELSEVIER
DOI: 10.1016/j.patrec.2011.02.022
关键词
Neural network; Convolution; Fourier transform; Spatial filters; Steady-state visual evoked potential (SSVEP) Electroencephalogram (EEG)
资金
- European Community [MTKD-CT-2004-014211]
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 1 s. The proposed strategy outperforms other classical neural network architecures. (C) 2011 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据