A Feedback Information-Theoretic Approach to the Design of Brain–Computer Interfaces

Abstract

This article presents a new approach to designing brain–computer interfaces (BCIs) that explicitly accounts for both the uncertainty of neural signals and the important role of sensory feedback. This approach views a BCI as the means by which users communicate intent to an external device and models intent as a string in an ordered symbolic language. This abstraction allows the problem of designing a BCI to be reformulated as the problem of designing a reliable communication protocol using tools from feedback information theory. Here, this protocol is given by a posterior matching scheme. This scheme is not only provably optimal but also easily understood and implemented by a human user. Experimental validation is provided by an interface for text entry and an interface for tracing smooth planar curves, where input is taken in each case from an electroencephalograph during left- and right-hand motor imagery.

Acknowledgments

This research has been sponsored in part to Timothy Bretl and Todd P. Coleman by a Seed Grant from the Center for Healthy Minds, funded through NIH/NIA under Award No. P30-AG023101; to Timothy Bretl by awards NSF-CNS-0931871 and NSF-CMMI-0956362-EAGER; to Todd P. Coleman by the AFOSR Complex Networks Program via Award No. FA9550-08-1-0079; and to Cyrus Omar by the NSF Graduate Research Fellowship.

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