Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from
neurophysiology, computer science, and engineering in an effort to establish real-time …

How neural correlates of movements are represented in the human brain is of ongoing
interest and has been researched with invasive and non-invasive methods. In this study, we …

Brain computer interfacing (BCI) has enjoyed increasing interest not only from research
communities such as engineering and neuroscience but also from visionaries that predict it …

Studying the relationship between the brain and finger tap** motions can contribute
towards an improved understanding of neuromuscular impairment. Furthermore, acquiring …

To provide multidimensional control, we describe the first reported decoding of bilateral
hand movements by using single-trial magnetoencephalography signals as a new approach …

Objective. The ability to decode kinematics of imagined movement from neural activity is
essential for the development of prosthetic devices that can aid motor-disabled persons. To …

Background EEG and fMRI have contributed greatly to our understanding of brain activity
and its link to behaviors by hel** to identify both when and where the activity occurs. This …

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical
representation. A BMI first decodes hand movements using cortical signals and then …

Transfer Learning (TL) is a methodology that allows the re-train of a Machine Learning (ML)
algorithm (like Neural Networks or NN's) for a new task with the advantage of the previous …

Electroencephalography (EEG) study allows the recording of brain activity associated with
different mental tasks through electrodes placed on the scalp that amplifies the electricity …