Network neuroscience for optimizing brain-computer interfaces

TL;DR

This paper reviews how network neuroscience can address key challenges in brain-computer interfaces by modeling brain architecture and function to improve human-machine interactions.

Contribution

It introduces the application of network science to BCI challenges, highlighting its potential to enhance decoding and learning strategies.

Findings

Network neuroscience offers a promising framework for BCI development.

Modeling brain interconnectivity can improve decoding of neural signals.

Network approaches can address neural plasticity challenges in BCIs.

Abstract

Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.