Movement prediction with sensory feedback and environmental interaction

In the last decade, numerous brain-computer interface (BCI) studies have been performed to help disabled people. However, previous BCI methods have several limitations. First, the BCI system has a time delay which makes user inconvenience. Second, user of the BCI only can select a choice among limited options. Last, accuracy of the BCI is low which is a barrier to practical use. Here, we suggested a novel BCI method to solve these problems. Our results demonstrated that BCI response time can be reduced by predicting motor intentions from the readiness potential (Bereitschaftspotential; RP or BP) signals. We also showed that movement trajectory could be predicted from the non-invasive MEG signals with considerably high accuracy. In addition, our results revealed that the BCI performance will be improved by combining feedback information. Furthermore, we showed possibility of a BCI with tactile feedback. Our results will promote the development of a practical BCI system.