Brain-computer interfacing is an area of research that is currently in flux as researchers try to understand not only what but who BCIs will work best for. One study by a group of researchers at Bremen University, Germany has recently attempted to determine who, exactly, is the key demographic for BCI use. More specifically, they looked at a group of subjects using a certain flavor of EEG-based BCI called steady state visually evoked potential (SSVEP), a technique where visual stimuli are flashed on a screen at certain frequencies. These flashes have very nice EEG signals for increasing classification accuracy during a certain visual task.

The results showed that an SSVEP BCI system was an effective form of communication for most of their 106 subjects. Who performed best? (drumroll please) Young people! Shocked? No, me neither. The authors note, however, that the result was not statistically significant since older subjects are known to have smaller evoked potentials for visual attention tasks. Another confounding issue stems from the fact that a majority of these types of studies are performed with young male populations from that particular study’s university.

Another problem is that many subjects find the use of SSVEP BCIs to be highly annoying. One could safely predict that having a white light flashing in your eyes for an hour is not enjoyable. This particular study was only 20 minutes in length, so most subjects had no problem with the visual stimuli presented. What made this particular study interesting was the use of a questionnaire given to each subject that addressed attentional strategies, mood, and personality factors. Understanding how users interact with BCI tasks is an important element in refining future BCI software and paradigms, since factors such as personality and mental strategy often correlate with higher (or lower) performance. There are numerous methods for improving BCI performance, many of which are user-dependent and cannot be generalized to the entire population of BCI users. However, knowing the key demographics for certain BCI applications, and continuing to garner feedback from users can assist researchers and software/hardware developers to find which factors can be either ignored or bolstered.