Learning joint space–time–frequency features for EEG decoding on small labeled data-Reference-Cited by-同舟云学术

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Learning joint space–time–frequency features for EEG decoding on small labeled data

Published:2019-06 Issue: Volume:114 Page:67-77
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Zhao Dongye,Tang Fengzhen^ORCID,Si Bailu,Feng Xisheng

Funder

National Key Research and Development Program of China

Chinese Academy of Sciences

CAS Pioneer Hundred Talents Program, China

State Key Laboratory of Robotics, China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference30 articles.

1. Analysis of EEG records in an epileptic patient using wavelet transform;Adeli;Journal of Neuroscience Methods,2003

2. Separable common spatio-spectral patterns for motor imagery BCI systems;Aghaei;IEEE Transactions on Biomedical Engineering,2016

3. Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs;Ang;Pattern Recognition,2012

4. Multiclass brain-computer interface classification by Riemannian geometry;Barachant;IEEE Transactions on Biomedical Engineering,2012

5. Optimizing spatial filters for robust EEG single-trial analysis;Blankertz;IEEE Signal Processing Magazine,2007

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