Brain-computer interface (BCI) is a popular and potential technology explored and applied in many different areas, such as rehabilitation medicine, biomedical engineering, and automation science. It mainly includes non-invasive and invasive BCI forms. At the same time, the former usually captures human biological signals like an electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), etc., and the latter usually collects neuron spikes signals. Up to now, progress appears in algorithm improvement and innovation, BCI prototype research and development, neurorehabilitation in diseases like stroke and other neurological dysfunctions, etc. However, there is still limited research regarding BCI algorithms and experimental paradigms. After BCI training, the clinical efficacy and brain change in people with nervous system diseases and dysfunction, like stroke people with motor dysfunction, needed to be clarified.
Based on the status of the non-invasive brain-computer interface technology, this Research Topic focuses on promoting the BCI algorithm, including signal preprocessing, feature engineering, classifying, etc., that advances the decoding accuracy of mental tasks, thus selecting the optimal BCI experimental paradigm and exploring brain plasticity during recovery in people with stroke or other nervous system diseases. Several typical medical scales can assess clinical efficacies. At the same time, the sub-clinical changes of those stroke patients can be detected in stroke survivors by EEG, fNIRS, magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI). By applying a non-invasive BCI system, we can treat and assess patients’ brain function and even predict disease prognosis. Besides, new-developed BCI prototypes and the associated sensory feedback loop are encouraged to show an advance in improving a BCI system.
We welcome clinical studies, algorithms, and paradigm research, including Original Research, Clinical Trials, Meta-Analysis, Visualized Analysis, Systematic Review, Review, Protocol, Methods, Hypothesis, and Theory.
Research areas of interest include, but are not limited to:
- Clinical efficacy in stroke after a BCI training
- Brain plasticity changes after stroke by EEG, fNIRS, MEG, and fMRI
- Relationship between EEG, fNIRS, MEG, fMRI, and behavioral scales
- EEG Biomarkers in predicting the prognosis of stroke patients (Event-related de-synchronization, ERD, Event-related synchronization, ERS, Event-related potential, ERP, Event-related spectral perturbation, ERSP, lateralization index, LI, brain symmetry index, BSI, brain networks, functional connectivity, FC, etc.)
- Time, spatial, and frequency domain change in the sensorimotor rhythm of stroke or other related nervous system diseases
- Exploration of the specific features and changes on different bands of EEG (delta, theta, alpha, beta, gamma) in stroke patients during different stages of the disease
- Optimization in non-invasive BCI algorithm in stroke for motor or cognitive function rehabilitation
- Non-invasive BCI experimental paradigm, feedback loop, command control, etc., may promote the development of a BCI in clinical neurorehabilitation
Brain-computer interface (BCI) is a popular and potential technology explored and applied in many different areas, such as rehabilitation medicine, biomedical engineering, and automation science. It mainly includes non-invasive and invasive BCI forms. At the same time, the former usually captures human biological signals like an electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), etc., and the latter usually collects neuron spikes signals. Up to now, progress appears in algorithm improvement and innovation, BCI prototype research and development, neurorehabilitation in diseases like stroke and other neurological dysfunctions, etc. However, there is still limited research regarding BCI algorithms and experimental paradigms. After BCI training, the clinical efficacy and brain change in people with nervous system diseases and dysfunction, like stroke people with motor dysfunction, needed to be clarified.
Based on the status of the non-invasive brain-computer interface technology, this Research Topic focuses on promoting the BCI algorithm, including signal preprocessing, feature engineering, classifying, etc., that advances the decoding accuracy of mental tasks, thus selecting the optimal BCI experimental paradigm and exploring brain plasticity during recovery in people with stroke or other nervous system diseases. Several typical medical scales can assess clinical efficacies. At the same time, the sub-clinical changes of those stroke patients can be detected in stroke survivors by EEG, fNIRS, magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI). By applying a non-invasive BCI system, we can treat and assess patients’ brain function and even predict disease prognosis. Besides, new-developed BCI prototypes and the associated sensory feedback loop are encouraged to show an advance in improving a BCI system.
We welcome clinical studies, algorithms, and paradigm research, including Original Research, Clinical Trials, Meta-Analysis, Visualized Analysis, Systematic Review, Review, Protocol, Methods, Hypothesis, and Theory.
Research areas of interest include, but are not limited to:
- Clinical efficacy in stroke after a BCI training
- Brain plasticity changes after stroke by EEG, fNIRS, MEG, and fMRI
- Relationship between EEG, fNIRS, MEG, fMRI, and behavioral scales
- EEG Biomarkers in predicting the prognosis of stroke patients (Event-related de-synchronization, ERD, Event-related synchronization, ERS, Event-related potential, ERP, Event-related spectral perturbation, ERSP, lateralization index, LI, brain symmetry index, BSI, brain networks, functional connectivity, FC, etc.)
- Time, spatial, and frequency domain change in the sensorimotor rhythm of stroke or other related nervous system diseases
- Exploration of the specific features and changes on different bands of EEG (delta, theta, alpha, beta, gamma) in stroke patients during different stages of the disease
- Optimization in non-invasive BCI algorithm in stroke for motor or cognitive function rehabilitation
- Non-invasive BCI experimental paradigm, feedback loop, command control, etc., may promote the development of a BCI in clinical neurorehabilitation
