Advances in brain science and computer technology in the past decade have led to exciting
developments in brain–computer interface (BCI), thereby making BCI a top research area in …

People with severe neurological impairments face many challenges in sensorimotor
functions and communication with the environment; therefore they have increased demand …

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …

Over the past several decades, electroencephalogram (EEG)-based brain-computer
interfaces (BCIs) have attracted attention from researchers in the field of neuroscience …

This paper presents an important step forward towards increasing the independence of
people with severe motor disabilities, by using brain-computer interfaces to harness the …

Communication using brain-computer interfaces (BCIs) can be non-intuitive, often requiring
the performance of a conversation-irrelevant task such as hand motor imagery. In this paper …

The present study proposes a hybrid brain-computer interface (BCI) with 64 selectable items
based on the fusion of P300 and steady-state visually evoked potential (SSVEP) brain …

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability
of individuals with profound motor impairments to interact and communicate. While much …

Steady-state visual evoked potentials (SSVEP) based paradigm is a conventional BCI
method with the advantages of high information transfer rate, high tolerance to artifacts and …