Biased feedback in brain-computer interfaces

Abstract

Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.

Figure 1

Setup of visual feedback. Arrangement of the elements present in the visual feedback interface of the used BCI system. The subject is told to look at the fixation cross, which is always present on the screen. During each trial an arrow showing the objective basket appears on screen. The position of the baskets is fixed, and the falling ball always starts at the shown position at the beginning of each trial.

Figure 2

Unbiased classification accuracy vs. deviation in accuracy due to feedback bias. Unbiased classification accuracy vs. deviation from this accuracy due to feedback bias. A +10% value in the y-axis represents a 10% improvement in absolute mean accuracy. Each dot corresponds to one session, the numbers identificating the subjects. Least squares regression lines for each type of feedback bias are shown in grey along with their correlation coefficient. The x2 maker denotes overlapping datapoints corresponding to the same subject.