- Short report
- Open Access
Biased feedback in brain-computer interfaces
© Barbero and Grosse-Wentrup; licensee BioMed Central Ltd. 2010
- Received: 10 December 2009
- Accepted: 27 July 2010
- Published: 27 July 2010
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.
- Classification Accuracy
- Motor Imagery
- Common Spatial Pattern
- Feedback Design
- Accurate Feedback
Brain-computer interfaces (BCIs) enable subjects to communicate without using the peripheral nervous system by recording brain signals and translating these into control commands . To operate a BCI, subjects need to learn how to intentionally modulate certain characteristics of their brain signals in order to express their intention. For example, in motor imagery, one of the most frequently used experimental paradigms in BCIs , subjects are instructed to haptically imagine movements of either the left or right hand, which typically induces a decrease in power of the electromagnetic field of the brain over contralateral sensorimotor cortex in the μ- and β-frequency ranges (roughly 10-14 Hz and 20-30 Hz, respectively) . The observed lateralization of this sensorimotor-rhythm (SMR) can then be used to infer a subject's intention.
As in any form of skill acquisition, subjects require feedback on their performance in order to learn how to optimally regulate their brain signals. While the importance of feedback in BCIs has long been recognized , surprisingly little is known on how feedback should be designed in BCIs in order to facilitate the skill acquisition process. In , the authors investigated whether instantaneous or delayed feedback proved to be more beneficial. While individual differences could be found, on average no significant effect was observed. Recently, the influence of realistic vs. abstract feedback on BCI performance was investigated . However, the authors again found no evidence for a significant influence of the type of feedback on BCI performance. As such, it appears that the specfic feedback design has little influence on BCI performance.
It should be noted, however, that in previous studies only accurate feedback was considered. While it is generally accepted that feedback in skill acquisition should be timely and precise, motivation is also known to play an important role in BCIs (cf. ). Accordingly, subjects may benefit from feedback that trades feedback accuracy for motivation, e.g., by artificially biasing the belief subjects have on their success in the skill acquisition process.
In this work, we investigate the influence of such a feedback bias on BCI performance. Subjects participated in a standard BCI experiment, in which they were asked to navigate a falling ball into a basket in either the left or right corner of the screen by performing haptic motor imagery of either the left or right hand. A depiction of the visual interface is shown in Figure 1. Each experimental trial lasted four seconds, and was considered successful if the ball ended up in the correct half-side of the screen. While usually the horizontal position of the ball on the screen reflects the belief of the BCI system on a subject's intention, we artificially distorted this feedback. Specifically, every two milliseconds we coded the classifier's belief on a subject's intention as a value in the range [0-1]. Then, we drew a sample from a Gaussian distribution, and added this to the classifier's belief. The mean of this sample was chosen as a function of the type of bias, and its variance was determined heuristically and identical for all type of feedback to prevent subjects' awareness of the feedback bias (σ2 = 3·10-4). If the resulting value was found to be larger/smaller than the current horizontal position of the ball (0/1 representing the left/right border of the screen), the ball was was moved one step (0.003 times the width of the screen) to the right/left. At the beginning of each trial, we pseudo-randomly chose one of five means for this random distortion, such that without any meaningful BCI control by the subject the falling ball would on average end up in 1.) the intended corner of the screen (strong positive bias), 2.) half-way between the center of the screen and the intended corner (weak positive bias), 3.) in the center of the screen (no bias), 4.) halfway between the center of the screen and the incorrect corner (weak negative bias), or 5.) in the incorrect corner (strong negative bias). As such, in 80% of the trials we biased the belief the subject had on her/his performance in either a positive or negative manner, while in the remaining 20% of trials subjects received accurate feedback.
The BCI system employed in this study is described in detail in . Briey, classification was performed by logistic regression with l1-regularization, using logarithmic bandpower in frequency bands ranging from 7 to 40 Hz. Before bandpower computation, the 128-channel EEG data was spatially filtered using beamforming  (subjects 1 to 7 and 11) or Common Spatial Patterns (CSP)  (subjects 8 to 10).
Mean classification results
Strong positive bias (++)
Weak positive bias (+)
Weak negative bias (-)
Strong negative bias (- -)
As the study design required trials with different types of feedback to be interleaved as well as subjects remaining ignorant of the feedback distortion, we could not ask subjects to report their experiences regarding different types of feedback. As such, any interpretation of the observed effects currently remains speculative. We hypothesize that subjects already capable of utilizing a BCI for means of communication are able to make use of instantaneous and accurate feedback in order to optimally regulate their SMR. In these subjects, any type of feedback bias appears to interfere with this feedback loop and hence leads to degraded performance. Accurate feedback in incapable subjects, on the other hand, may be perceived as random noise, as the horizontal movement of the falling ball is uncorrelated with the intended movement direction. We hypothesize that this perceived lack of control leads to frustration and demotivation, impeding an effective skill acquisition process. In these subjects, biased feedback may reduce the perceived randomness of the visual feedback. Specifically, our results indicate that a strong positive bias may be particularly helpful for focussing on the intended task.
In terms of feedback design for future BCI systems, our results suggest that a subject's current skill level should be taken into account. Subjects already capable of modulating their sensorimotor rhythm to some extent should receive accurate feedback. Subjects not yet capable of utilizing a BCI, on the other hand, may benefit by designs that aim to induce a beneficial state-of-mind. While further investigations into the behavioral and neural correlates of a beneficial state-of-mind for BCIs are required (cf. [9, 10] for two recent studies on this topic), the results presented here suggest that incapable subjects may particularly benefit if their belief on the level of control over the BCI-system is positively biased.
This work was developed at the Max Planck Institute for Biological Cybernetics, under partial support of Spain's TIN 2007-66862 and "Cátedra UAM-IIC en Modelado y Predicción". The first author is supported by the FPU-MEC grant reference AP2006-02285. We would like to acknowledge the support of Bernd Battes for participating in the preparation and execution of the BCI experiments.
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