Evaluating the performance of a self-paced BCI with a new movement and using a more engaging environment

In previous studies, we proposed a self-paced brain computer interface (SBCI) system that employed three neurological phenomena to identify intentional control (IC) commands from the no control (NC) states of EEG signals. We showed that this SBCI system achieved a good performance that was better than those of other EEG-based SBCI systems. In this paper, we carry out a new study to show that this system can be generalized. Specifically, we show that it can also achieve good performance when 1) a new type of movement is used (hand extension vs. the finger flexion this system was designed for), and 2) NC data are recorded in an engaging environment. A more reliable artifact monitoring system is also added to the system to rule out not only the effects of eye blinks but also the frontalis muscles when controlling the system. Using the data from five participants it is shown that the system obtains good performance compared to other EEG-based SBCI systems.