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Updated: Jun 10, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
Published on: March 10, 2011
Biased feedback in brain-computer interfaces
Alvaro Barbero1, Moritz Grosse-Wentrup
1Universidad Autónoma de Madrid (Departamento de Ingeniería Informática) and Instituto de Ingeniería del Conocimiento, Francisco Tomás y Valiente 11, Madrid, Spain. alvaro.barbero@uam.es
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.
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