Convolutional neural networks and genetic algorithm for visual imagery classification

被引:13
作者
Llorella, Fabio R. [1 ]
Patow, Gustavo [1 ]
Azorin, Jose M. [2 ]
机构
[1] Univ Girona, ViRVIG UdG, Girona, Spain
[2] Brain Machine Interface Syst Lab, Avda Univ S-N Ed Innova, Alicante 03202, Spain
关键词
Brain-Computer Interface; Visual imagery; Deep learning; Genetic algorithms; Keras; BRAIN-COMPUTER-INTERFACE; MENTAL-IMAGERY; COLOR; PERCEPTION; ATTENTION;
D O I
10.1007/s13246-020-00894-z
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the present work we present a technique that employs visual imagery. Our technique uses neural networks to classify the signals produced in visual imagery. To this end, we have used densely connected neural and convolutional networks, together with a genetic algorithm to find the best parameters for these networks. The results we obtained are a 60% success rate in the classification of four imagined objects (a tree, a dog, an airplane and a house) plus a state of relaxation, thus outperforming the state of the art in visual imagery classification.
引用
收藏
页码:973 / 983
页数:11
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