Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …

In this paper, hybrid brain-computer interface (hBCI) technologies for improving
classification accuracy and increasing the number of commands are reviewed. Hybridization …

Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …

Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …

All people experience aging, and the related physical and health changes, including
changes in memory and brain function. These changes may become debilitating leading to …

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

Background Common spatial pattern (CSP) has been most popularly applied to motor-
imagery (MI) feature extraction for classification in brain–computer interface (BCI) …