Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems.
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …

Brain–computer interface (BCI) technology holds promise for individuals with profound
motor impairments, offering the potential for communication and control. Motor imagery (MI) …

An electroencephalography (EEG) based brain activity recognition is a fundamental field of
study for a number of significant applications such as intention prediction, appliance control …

Considering the entire BCI system, a big challenge is that information can be extracted from
brain signals in a meaningful way. Therefore, most BCI studies are focused on brain signal …

Previous studies have explored the use of deep neural networks for
electroencephalography (EEG)-based motor imagery (MI) recognition, but most of the …

Background Motor imagery (MI) related features are typically extracted from a fixed
frequency band and time window of EEG signal. Meanwhile, the time when the brain activity …

Deep neural network is a hotspot in the field of Machine Learning, which can realize deep
hierarchical representation of input data. In this paper, a simplified Shallow Convolutional …

In the field of Brain Computer Interface (BCI) technology, Motor Imagery (MI) plays an
important role as a paradigm. One of the primary focuses of this research area lies in …

Brain-computer interfaces (BCIs) enables direct communication between a brain and a
computer by recording and analyzing a subject's neural activity in real-time. Research in BCI …