توزیع دقت طبقه بندی در سرتاسر مغز رایانه ای مبتنی بر پدیده بصری بر پایه مغز و کامپیوتر

Brain–computer interfaces (BCIs) system designed using the steady-state visual evoked potential (SSVEP) signal have been widely studied because of their high accuracy of classification and high rates of the information transfer. However, the SSVEP is typically measured over the occipital scalp region (channels O1, O2, and Oz), which makes this type of BCI unsuitable for some patients. We investigated the classification accuracy of SSVEP over the whole scalp, to evaluate the feasibility of building SSVEP-based BCIs that use additional channels. The classification accuracy distribution of the whole scalp increased with the electrode positions closer to the occipital region, and the classification accuracy increased with an increasing number of electroencephalogram data channels.