Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS

TL;DR

This paper explores using fNIRS-based BCI to detect brain states and present tailored information in real time, aiming to develop a memory prosthesis.

Contribution

The paper introduces a prototype memory prosthesis using real-time fNIRS-based BCI for adaptive information delivery.

Findings

A 71% accuracy in differentiating brain states using leave-one-out cross-validation suggests feasibility.

Analyses in lateral and medial prefrontal areas show potential for improved classification in future systems.

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.

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