Ioannis Xygonakis

TL;DR: This work develops a multiclass BCI decoding algorithm that uses electroencephalography source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG.

TL;DR: This paper presents the progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications, and proposes further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

TL;DR: Spinal cord injury patients showed signs of increased local processing as adaptive mechanism in functional connectivity, and Alpha networks were less dense, showing less integration and more segregation than beta networks.

TL;DR: Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology, and multi-DoF robotics control is possible by patients through commercial wireless BCI.

TL;DR: An experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms is described and shows promise in investigating differences in functional cortical networks associated with different motor tasks.