View clinical trials related to Brain-computer Interface.
Filter by:About 50% of stroke patients are unable to live independently because of residual disability. Brain-computer interface (BCI) is based on closed-loop theory, which facilitates neurological remodeling by establishing a bridge between central and peripheral connections. Studies have confirmed that BCI real-time neurofeedback training system based on motor imagery alone can effectively improve patients' motor function. So, is the benefit greater if motor imagery is combined with motor execution? Current conclusions are mixed. In addition, previous studies and our preliminary study found that prefrontal Fp1 and Fp2 areas play an important role in motor recovery after stroke, and they are involved in motor imagery, motor execution, attention and other behavioral processes. Therefore, we designed a BCI training system based on motor imagery and motor execution with prefrontal electroencephalogram (EEG) signals as the modulatory target. This was a randomized placebo-controlled double-blinded clinical trial. Patients in the test group performed BCI-controlled upper extremity motor imagery + upper extremity pedaling training. The control group had the same equipment and training scenario, and patients were also asked to imagine the upper extremity pedaling movement with effort, and patients also wore EEG caps, but the EEG signals were only recorded without controlling the pedaling equipment. After 3 weeks of treatment, we observed the changes of motor and cognitive functions as well as fNIRS-related brain network characteristics in both groups.
Stroke has become the first cause of death and disability among Chinese adults. 70%-80% of patients cannot live independently due to disability, which has brought a heavy burden to families, medical institutions and society. How to better evaluate and improve post-stroke motor and cognitive dysfunction has always been a hot research topic. With the rapid development of brain-computer interface technology, rehabilitation assessment based on quantitative EEG analysis is gradually being applied in the medical field. So we designed a brain-computer interface based on hierarchical task induction-pedaling rehabilitation training system to investigate the effectiveness on the rehabilitation of stroke patients.