Page 167 - Handbook of Biomechatronics

164 Domen Novak

3.1 Improving User Friendliness and Resistance to
Environmental Conditions

BCIs have long been plagued by a perception of being unwieldy and overly
sensitive: in the minds of many researchers, they take a very long time to put
on, and their performance is then decimated by even the slightest noise.
While this may have been true in the past, BCIs have made enormous strides
with regard to user friendliness over the last few years. For example, dry and
water-based EEG electrodes have enabled reduced setup time and increased
comfort compared to “classic” gelled electrodes, and wireless EEG elec-
trodes have increased signal quality by making BCIs less susceptible to
movement artifacts. Furthermore, the use of techniques such as ERPs has
allowed BCIs to perform self-correction, increasing their accuracy. How-
ever, it is true that BCIs are still inconvenient and error-prone compared
to many other technologies (e.g., eye trackers). The situation will doubt-
lessly improve as some experimental BCI systems become more commonly
used, for example, though dry electrodes have achieved promising labora-
tory results (Guger et al., 2012), they are still relatively uncommon in real-
world situations that would benefit from them. Still, new advances in both
hardware and software have great potential to improve the robustness of
BCIs and could be invented by scientists and engineers in many fields,
not just BCI researchers.

3.2 Interindividual Differences
While many BCI studies treat their participant groups as largely homoge-
nous, BCI performance is affected by factors such as personality and cogni-
tive profile (Hammer et al., 2012; Jeunet et al., 2016), motivation (Sheets
et al., 2014) and level of experience with the system (Carlson and Milla ´n,
2013). Furthermore, participants with relatively poorly developed brain net-
works tend to have lower ability to perform motor imagery (Ahn and Jun,
2015), and participants with disabilities frequently exhibit worse BCI per-
formance than able-bodied participants. However, the effects of most of
these factors are unclear, and some studies have given conflicting results.
For example, a 2012 study by Hammer et al. (2012) found that the accuracy
of fine motor skills was a predictor of BCI performance, but a 2014 study by
the same research group (Hammer et al., 2014) found that the same variable
(measured in the same way) did not reach significance in a somewhat differ-
ent BCI. As another example, while some studies have found significantly
worse BCI performance in participants with disabilities than in able-bodied