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SSVEP-based brain-computer interface enabling graded dyspnoea self-report: proof-of-concept study in healthy

Sébastien Campion1, Xavier Navarro-Suné1, Isabelle Rivals1,2

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Summary

A brain-computer interface (BCI) using steady-state visual evoked potentials (SSVEP) can detect and quantify breathing difficulties. This technology shows promise for assessing dyspnoea in non-communicative patients.

Keywords:
Brain–computer interfaceCritical careDyspnoeaMechanical ventilationNonverbal communicationRespiratory discomfortSelf-reportSteady-state visual evoked potentials (SSVEP)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Respiratory Medicine

Background:

  • Mechanically ventilated patients face challenges in reporting respiratory distress due to impaired communication.
  • Assessing dyspnoea (shortness of breath) in these patients is crucial for their well-being.
  • Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a potential solution for non-verbal communication.

Purpose of the Study:

  • To evaluate the performance of an SSVEP-based BCI in detecting and quantifying experimentally induced dyspnoea.
  • To assess the BCI's ability to enable self-reporting of respiratory discomfort in a controlled setting.
  • To compare the efficacy of different BCI models and visual stimulus frequencies.

Main Methods:

  • Forty-nine healthy volunteers underwent five respiratory conditions, including normal breathing and induced dyspnoea (inspiratory loading, CO₂ inhalation).
  • Two BCI models were tested: a detection BCI (D-BCI) and a quantification BCI (LED-based analogue scale - LAS).
  • Performance was measured using visual analogue scale (VAS) for dyspnoea and receiver operating characteristic (ROC) curves to determine the area under the curve (AUC).

Main Results:

  • Participants reported significantly higher dyspnoea VAS scores during induced respiratory discomfort compared to normal breathing.
  • The D-BCI achieved an AUC of 0.89 with 20-30 Hz stimuli, while the LAS achieved an AUC of 0.84 with low frequencies.
  • Both BCI models demonstrated effectiveness in detecting and quantifying experimentally induced respiratory discomfort.

Conclusions:

  • An SSVEP-based BCI can successfully detect and quantify experimentally induced dyspnoea in healthy individuals.
  • The findings suggest potential clinical applications for SSVEP-BCIs in assessing dyspnoea in non-communicative patients.
  • Further research is warranted to validate the clinical utility of this BCI technology in patient populations.