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

TLDR

BCIs are framed as communication protocols where user intent is a symbolic string, enabling the problem to be reformulated as designing a reliable communication system using feedback information theory. The article introduces a BCI design approach that explicitly incorporates neural signal uncertainty and sensory feedback. This approach employs a posterior matching scheme to construct the communication protocol. The posterior matching scheme is provably optimal, user‑friendly, and experimentally validated with EEG‑based text entry and planar curve tracing.

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.