Speech imagery-based Brain-computer interface (BCI) using Electroencephalogram (EEG)
signal is a promising area of research for individuals with severe speech production …

Advances in deep learning have shown great promise towards the application of performing
high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks …

This paper presents BELT, a novel model and learning framework for the pivotal topic of
brain-to-language translation research. The translation from noninvasive brain signals into …

This systematic review examines EEG-based imagined speech classification, emphasizing
directional words essential for development in the brain–computer interface (BCI). This study …

Objective. In recent years, imagined speech brain–computer (machine) interface
applications have been an important field of study that can improve the lives of patients with …

This study focuses on the automatic decoding of inner speech using noninvasive methods,
such as Electroencephalography (eeg). While inner speech has been a research topic in …

This work focuses on inner speech recognition starting from EEG signals. Inner speech
recognition is defined as the internalized process in which the person thinks in pure …

Decoding natural language from noninvasive brain signals has been an exciting topic with
the potential to expand the applications of brain-computer interface (BCI) systems. However …

The recognition of inner speech, which could give a 'voice'to patients that have no ability to
speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the …

Objective. In recent years, electroencephalogram (EEG)-based brain–computer interfaces
(BCIs) applied to inner speech classification have gathered attention for their potential to …