Communications Medicine (Oct 2024)

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling

  • Daniel N. Candrea,
  • Samyak Shah,
  • Shiyu Luo,
  • Miguel Angrick,
  • Qinwan Rabbani,
  • Christopher Coogan,
  • Griffin W. Milsap,
  • Kevin C. Nathan,
  • Brock A. Wester,
  • William S. Anderson,
  • Kathryn R. Rosenblatt,
  • Alpa Uchil,
  • Lora Clawson,
  • Nicholas J. Maragakis,
  • Mariska J. Vansteensel,
  • Francesco V. Tenore,
  • Nicolas F. Ramsey,
  • Matthew S. Fifer,
  • Nathan E. Crone

DOI
https://doi.org/10.1038/s43856-024-00635-3
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 14

Abstract

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Abstract Background Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability. Methods We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant’s click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Results Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day). Conclusions These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.