Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely...

Full description

Bibliographic Details
Main Authors: Zied Tayeb, Juri Fedjaev, Nejla Ghaboosi, Christoph Richter, Lukas Everding, Xingwei Qu, Yingyu Wu, Gordon Cheng, Jörg Conradt
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Sensors
Subjects:
Brain-Computer Interfaces
spectrogram-based convolutional neural network model (pCNN)
Deep Learning
electroencephalography (EEG)
long short-term memory (LSTM)
recurrent convolutional neural network (RCNN)
Online Access:http://www.mdpi.com/1424-8220/19/1/210

Internet

http://www.mdpi.com/1424-8220/19/1/210

Similar Items