Visualization and Workload with Implicit fNIRS-based BCI: Towards a Real-time Memory Prosthesis with fNIRS

Matthew Russell^1*Samuel Hincks¹Liang Wang¹Amin Babar²Zaiyi Chen¹Zachary White¹Robert J K Jacob¹

• ¹Tufts University, Medford, United States
• ²Hamilton College New York, Clinton, New York, United States

Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workloaddetection 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.