In this paper we propose a framework using multi-channel convolutional neural network (MC–
CNN) for recognizing the grammatical class (verb or noun) of covertly-spoken words from …

The completely paralyzed and quadriplegic patients cannot communicate with others.
However, the imagined thoughts of these patients can be used to drive assistive devices by …

Imagined speech is a process where a person imagines the sound of words without moving
any of his or her muscles to actually say the word. If the brain signals of a person imagining …

Objective. Directly decoding imagined speech from electroencephalogram (EEG) signals
has attracted much interest in brain–computer interface applications, because it provides a …

The recent advances in the field of deep learning have not been fully utilised for decoding
imagined speech primarily because of the unavailability of sufficient training samples to train …

Electroencephalography (EEG)-based brain computer interface (BCI) enables people to
interact directly with computing devices through their brain signals. A BCI typically interprets …

The use of imagined speech with electroencephalographic (EEG) signals is a promising
field of brain-computer interfaces (BCI) that seeks communication between areas of the …

This paper proposes a novel approach that uses deep neural networks for classifying
imagined speech, significantly increasing the classification accuracy. The proposed …

Imagined speech is gaining traction as a communicative paradigm for brain-computer-
interfaces (BCI), as a growing body of research indicates the potential for decoding speech …

We propose a mixed deep neural network strategy, incorporating parallel combination of
Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep …