For a brain-computer interface (BCI) system, a calibration procedure is required for each
individual user before he/she can use the BCI. This procedure requires approximately 20-30 …

Convolutional neural networks (CNNs) have recently been applied to electroencephalogram
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …

Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily
operate. Imagined speech and visual imagery are emerging paradigms that can directly …

Conventional brain‐computer interface (BCI) systems have been facing two fundamental
challenges: the lack of high detection performance and the control command problem. To …

Brain–computer interfaces (BCIs) have been widely employed to identify and estimate a
user's intention to trigger a robotic device by decoding motor imagery (MI) from an …

Transformers are groundbreaking architectures that have changed a flow of deep learning,
and many high-performance models are develo** based on transformer architectures …

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based
brain computer interface (BCI) to decode electroencephalography (EEG) signals. However …

The past twenty years have ignited a new spark in the research of Electroencephalogram
(EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order …

A new kind of sequence–to–sequence model called a transformer has been applied to
electroencephalogram (EEG) systems. However, the majority of EEG–based transformer …

Non-invasive brain-computer interface (BCI) has been developed for recognizing and
classifying human mental states with high performances. Specifically, classifying pilots' …