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Extracting robust single-trial somatosensory evoked potentials for non-invasive brain computer interfaces.

Disha Gupta1,2, Jodi Brangaccio1, Helia Mojtabavi1

  • 1National Center for Adaptive Neurotechnology, Stratton Veterans Affairs Medical Center, Albany, NY 12208, United States of America.

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|August 20, 2025
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Summary

This study optimized noninvasive single-trial somatosensory evoked potentials (SEPs) for brain-computer interfaces. Enhanced signal-to-noise ratio allows real-time SEP extraction for rehabilitation applications.

Keywords:
brain computer interfacingsingle-trial decodingsomatosensory evoked potentialstibial nerve stimulation

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Signal Processing
  • Rehabilitation Technology

Background:

  • Reliable single-trial somatosensory evoked potentials (SEPs) are crucial for brain-computer interface (BCI) applications in post-brain injury rehabilitation.
  • Current noninvasive SEP extraction methods often require extensive averaging or processing due to small and variable signals.
  • Optimizing stimulation parameters and signal processing is needed to enhance the signal-to-noise ratio (SNR) for real-time BCI feedback.

Purpose of the Study:

  • To describe and evaluate optimized electrical stimulation parameters for enhancing the SNR of noninvasive single-trial SEPs.
  • To enable reliable, real-time extraction of SEPs for BCI applications.
  • To assess the feasibility of real-time SEP detection in healthy individuals and those with central nervous system (CNS) injuries.

Main Methods:

  • Recorded SEPs using scalp electroencephalography (EEG) during tibial nerve stimulation in 13 healthy participants and 2 individuals with CNS injuries.
  • Evaluated three lower-than-recommended stimulation frequencies (0.2 Hz, 1 Hz, 2 Hz) with a longer pulse width (1 msec).
  • Assessed single-trial SEP detectability using offline, pseudo-online, and real-time analyses, employing Laplacian filtering.

Main Results:

  • The SEP N70 component was primarily observed in central scalp regions.
  • Online decoding performance significantly improved with Laplacian filtering, achieving Area Under the Curve (AUC) scores from 0.78-0.90.
  • Feasible SEP detection was demonstrated in individuals with incomplete spinal cord injury (AUC 0.86) and stroke (AUC 0.81), with real-time detection showing an AUC of 0.89.

Conclusions:

  • The study presents a system for real-time single-trial SEP extraction using optimized electrical stimulation parameters and signal processing.
  • This enhanced SNR approach is suitable for BCI-based operant conditioning for rehabilitation.
  • The optimized system demonstrates potential for effective BCI applications in individuals with neurological impairments.