… of published articles on motor imagery between 1995 and 2011. Lower graph: number of
published articles on motor imagery which used functional magnetic resonance imaging (fMRI) …

… Electroencephalogram (EEG) signal recorded during motor imagery (MI) has been widely
… In this paper, we propose a new method based on the deep convolutional neural network (…

… This work aims to build a robust neural network for motor imagery in a realistic scenario
that implies small size datasets and session-to-session transfer. Those constraints lead us to …

… The main contribution of this paper is design and analysis of a parallel convolutional-linear
neural network for 4-class motor imagery classification. In the following sections, we describe …

This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …

… recognize patterns generated by motor imagery. Currently, … methodologies for multiple
motor imagery classification. Both … many Convolutional Neural Networks previously optimized …

… This work focuses on four-class motor imagery problem where the recorded EEG signal
is … neural network (CNN) is a novel animal visual cortex inspired method for image based …

… neural network technology and the characteristics and difficulties of recognition and analysis
of motor imagery … of motor imagery EEG signals based on BP neural network algorithm, and …

… neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand
motor imagery (… 98 % ) for two-class motor imagery, while the best accuracy on this dataset is …

… Deep neural network: classifiers other than deep neural networks are not within the … Motor
imagery classification: the scope of our review is the classification of different motor imagery …