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

Matthew Russell1, Samuel Hincks1, Liang Wang1

  • 1Computer Science, Tufts University, Medford, MA, United States.

Frontiers in Neuroergonomics
|May 21, 2025
PubMed
Summary

This study demonstrates a novel memory prosthesis using Functional Near-Infrared Spectroscopy (fNIRS) Brain-Computer Interface (BCI) to adapt information delivery based on brain activity. The fNIRS-BCI system achieved 71% accuracy in differentiating cognitive states.

Keywords:
BCIHCIdefault mode networkfNIRSimplicit BCImemory prosthesistask positive network

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Functional Near-Infrared Spectroscopy (fNIRS) is established for real-time implicit Brain-Computer Interfaces (BCI), primarily for workload detection.
  • The application of prefrontal cortex neural measurements extends beyond workload assessment, offering potential for advanced BCI functionalities.

Purpose of the Study:

  • To develop and test a prototype memory prosthesis utilizing an fNIRS-based BCI.
  • To investigate the real-time adaptation of information delivery based on a user's current brain state.
  • To explore the potential of fNIRS for differentiating distinct cognitive tasks and brain networks.

Main Methods:

  • A prototype memory prosthesis was developed, employing a real-time implicit fNIRS-BCI.
  • Two distinct tasks were utilized: a creative visualization task engaging the Default Mode Network (DMN) and a knowledge-worker task engaging the Dorsolateral Prefrontal Cortex (DLPFC).
  • Leave-one-out cross-validation across participants was performed to assess classification performance.

Main Results:

  • The fNIRS-BCI system demonstrated a classification performance of 71% in differentiating between the two cognitive tasks.
  • Analyses within lateral and medial left prefrontal areas showed promising results for future classification algorithms.
  • The study confirms the differentiability of distinct cognitive tasks using fNIRS signals.

Conclusions:

  • The developed memory prosthesis shows promise for applied fNIRS-based BCI systems.
  • Real-time fNIRS-BCI can effectively differentiate between cognitive states associated with DMN and DLPFC engagement.
  • Further research into prefrontal cortex analyses can enhance future fNIRS-BCI classification capabilities.