Brain diseases represent a major socio-economic burden worldwide, and great efforts are being made towards the development of brain restoration techniques. Specifically, stroke is a leading cause of adult long-term disability worldwide, resulting in loss of independence of patients suffering from severe cognitive and motor impairments.
Recent controlled trials have shown the potential benefit of Brain-Computer Interfaces (BCI)-based therapies for rehabilitation. Specifically, the use of multimodal interventions utilizing BCI-aided robotic, VR or FES training yielded increased potential compared to traditional rehabilitation training only.
The goal of this Research Topic is to create an understanding of the current capabilities of neurotechnology and brain–computer interaction for neurorehabilitation, highlight current and future applications, and identify and tackle the most relevant technical challenges.
We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles including but not limited to:
- Applications related to restorative BCIs for neurorehabilitation;
- Closed loop BCI training using VR/AR, Robotics, tDCES, FES, etc.;
- Hybrid BCIs or shared-control BCIs for rehabilitation;
- BCI or Neurofeedback training through EEG, MEG, fNIRS, fMRI modalities.
- Neuroergonomics and Improvement of usability or functionality of BCIs;
- Methodological and technical advancements related to restorative BCIs;
Brain diseases represent a major socio-economic burden worldwide, and great efforts are being made towards the development of brain restoration techniques. Specifically, stroke is a leading cause of adult long-term disability worldwide, resulting in loss of independence of patients suffering from severe cognitive and motor impairments.
Recent controlled trials have shown the potential benefit of Brain-Computer Interfaces (BCI)-based therapies for rehabilitation. Specifically, the use of multimodal interventions utilizing BCI-aided robotic, VR or FES training yielded increased potential compared to traditional rehabilitation training only.
The goal of this Research Topic is to create an understanding of the current capabilities of neurotechnology and brain–computer interaction for neurorehabilitation, highlight current and future applications, and identify and tackle the most relevant technical challenges.
We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles including but not limited to:
- Applications related to restorative BCIs for neurorehabilitation;
- Closed loop BCI training using VR/AR, Robotics, tDCES, FES, etc.;
- Hybrid BCIs or shared-control BCIs for rehabilitation;
- BCI or Neurofeedback training through EEG, MEG, fNIRS, fMRI modalities.
- Neuroergonomics and Improvement of usability or functionality of BCIs;
- Methodological and technical advancements related to restorative BCIs;
