Biased feedback in brain-computer interfaces
© Barbero and Grosse-Wentrup. 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.
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.
Eleven healthy subjects with a mean age of 26.18 ± 4.14 years, seven of them male and four female, participated in the study, all except one were naive to BCIs. Every subject initially performed one session. Four subjects attaining a good level of BCI-control were asked to perform two additional sessions each, as we expected effects to be most prominent in well-performing subjects. Each session consisted of nine runs, with each run being composed of 15 trials per condition in pseudo-randomized order. The first three runs of each session, during which no feedback was presented to the subject, were used to train the classification system. During the following six runs, biased feedback was presented as discussed above. For each session, this resulted in a total of 36 trials for each of the five feedback biases. Mean classification accuracy was then computed for each session and feedback bias, using the undistorted classifier output hidden from the subject. Subjects were not informed that the presented feedback was biased until they had completed their last session.
The BCI system employed in this study is described in detail in . Briey, classification was performed by logistic regression with l 1-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.
- Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano La, Robinson CJ, Vaughan TM: Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering 2000, 8:164–173.View ArticlePubMed
- Mason S, Bashashati A, Fatourechi M, Navarro K, Birch G: A comprehensive survey of brain interface technology designs. Annals of Biomedical Engineering 2007,35(2):137–169.View ArticlePubMed
- Pfurtscheller G, Neuper C: Motor Imagery and Direct Brain-Computer Communication. Proceedings of the IEEE 2001,89(7):1123–1134.View Article
- McFarland D, McCane L, Wolpaw J: EEG-based communication and control: short-term role of feedback. IEEE Transactions on Biomedical Engineering 1998, 6:7–11.
- Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G: Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clinical Neurophysiology 2009,120(2):239–247.View ArticlePubMed
- Curran EA, Stokes MJ: Learning to control brain activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain and Cognition 2003,51(3):326–336.View ArticlePubMed
- Grosse-Wentrup M, Liefhold C, Gramann K, Buss M: Beamforming in non-invasive Brain-Computer Interfaces. IEEE Transactions in Biomedical Engineering 2009,56(4):1209–1219.View Article
- Ramoser H, Mueller-Gerking J, Pfurtscheller G: Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement. IEEE Transactions on Rehabilitation Engineering 2000,8(4):441–446.View ArticlePubMed
- Blankertz B, Sannellia C, Halder S, Hammer E, Kübler A, Müller KR, Curio G, Dickhaus T: Neurophysiological predictor of SMR-based BCI performance. NeuroImage 2010,51(4):1303–1309.View ArticlePubMed
- Grosse-Wentrup M, Hill J, Schölkopf B: Causal Influence of Gamma Oscillations on the Sensorimotor-Rhythm. NeuroImage 2010, in press.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.