Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG)
signals to control external devices, such as Functional Electrical Stimulation (FES). Visual …

Objective. Most deep neural networks (DNNs) used as brain computer interfaces (BCI)
classifiers are rarely viable for more than one person and are relatively shallow compared to …

Abstract Rapid Serial Visual Presentation (RSVP) based Brain–Computer Interface (BCI)
facilities the high-throughput detection of rare target images by detecting evoked event …

An electroencephalogram (EEG)–based brain–computer interface (BCI) speller is a system
that conveys thought to enable communication between humans and computers using brain …

Objective A majority of BCI systems, enabling communication with patients with locked-in
syndrome, are based on electroencephalogram (EEG) frequency analysis (eg, linked to …

The brain–computer interface (BCI) P300 speller analyzes the P300 signals from the brain to
achieve direct communication between humans and machines, which can assist patients …

This study aimed to investigate the psychophysiological markers of imagery processes
through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic …

Convolutional neural networks (CNNs), which automatically learn features from raw data to
approximate functions, are being increasingly applied to the end-to-end analysis of …

Brain-computer interface (BCI) systems provide a direct connection between the human
brain and external devices. Visual evoked BCI systems including Event-related Potential …

Abstract A Brain-Computer Interface (BCI) relies on machine learning algorithms to decode
the brain signals. An accurate detection of P300 response in electroencephalography (EEG) …