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Sequential hypothesis testing for automatic detection of task-related changes in cerebral perfusion in a

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Summary

This study introduces a faster brain-computer interface (BCI) using transcranial Doppler ultrasound (TCD) and sequential hypothesis testing. It achieves 72% accuracy in differentiating cognitive tasks in just 23 seconds, improving communication for locked-in patients.

Keywords:
Brain–computer interfaceFunctional transcranial DopplerVerbal fluency task

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Cognitive activity in locked-in patients can be monitored via cerebral blood flow patterns.
  • Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and is a potential brain-computer interface (BCI) modality.
  • Previous TCD-BCI studies often used lengthy offline analyses, limiting practical application.

Purpose of the Study:

  • To develop and evaluate an online TCD-BCI system for rapid differentiation of cognitive tasks.
  • To implement sequential hypothesis testing for efficient decision-making in TCD-BCIs.
  • To assess the performance of the TCD-BCI in terms of classification accuracy and decision time.

Main Methods:

  • Designed a BCI system utilizing bilateral TCD to record blood flow velocities in middle cerebral arteries.
  • Employed sequential hypothesis testing to analyze TCD signals and classify between silent counting and verbal fluency tasks.
  • Simulated online analysis updating class probability estimates every 250 ms, terminating classification upon reaching a certainty threshold.

Main Results:

  • Achieved a mean classification accuracy of 72% after an average of 23 seconds across ten participants.
  • Offline analysis yielded 80% accuracy in 45 seconds, demonstrating a significant gain in data transmission rate for the online system.
  • Observed decision times ranging from 19 to 28 seconds, highlighting the adaptability of sequential hypothesis testing to individual participants.

Conclusions:

  • Sequential hypothesis testing is a promising method for online TCD-BCIs, enabling faster and efficient communication.
  • The developed TCD-BCI system maintains over 70% classification accuracy with substantially reduced task durations.
  • Adaptive decision times are crucial for consistent performance in TCD-BCI applications.