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Related Experiment Video

Updated: Jul 13, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling.

Nathan Crone1, Daniel Candrea2, Samyak Shah1

  • 1Johns Hopkins Hospital.

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|October 16, 2023
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Summary
This summary is machine-generated.

Brain-computer interfaces (BCIs) enable neural control for communication. A single "click" decoder, trained with minimal data, provided sustained text-based communication for an individual with ALS over 90 days.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-computer interfaces (BCIs) offer communication restoration for individuals with severe motor and speech impairments.
  • Single-command "click" decoders represent a fundamental yet effective BCI capability.

Approach:

  • A high-density electrocorticographic (ECoG) BCI system was implanted to cover the sensorimotor cortex in a participant with amyotrophic lateral sclerosis (ALS).
  • A click decoder was trained using limited ECoG data (<44 minutes over 4 days) collected up to 21 days before use.
  • The decoder's performance and stability were evaluated over 90 days without retraining.

Key Points:

  • The participant achieved a median spelling rate of 10.2 characters per minute using the click decoder with a switch-scanning interface.
  • Despite a temporary signal interruption, a newly trained decoder, using even less data (<15 minutes), demonstrated comparable performance.
  • This highlights the robustness of ECoG-based click decoding.

Conclusions:

  • A click decoder can be trained effectively with a small ECoG dataset.
  • Robust performance is maintained over extended periods, enabling functional text-based communication for BCI users.
  • This technology holds significant promise for individuals with communication disabilities.