Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied for several decades since the 1970s. Current BCI research mainly aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. The BCI technology can also benefit normal healthy users; however, little progress has been made in real-world practices due to low BCI performance caused by technical limits of EEG. To overcome this bottleneck, this study uses a collaborative BCI to improve overall performance through integrating information from multiple users. A dataset involving 15 subjects participating in a Go/NoGo decision-making experiment was used to evaluate the collaborative method. Using collaborative computing techniques, the classification accuracy for predicting a Go/NoGo decision was enhanced substantially from 75.8% to 91.4%, 97.6%, and 99.1% as the number of subjects increased from 1 to 5, 10, and 15, respectively. These results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve human behavior.