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

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

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 p...

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Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Communications Medicine
Online Access:https://doi.org/10.1038/s43856-024-00635-3
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Summary: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.
ISSN:2730-664X

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