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

Author:

Candrea Daniel N.1,Shah Samyak2,Luo Shiyu1,Angrick Miguel2ORCID,Rabbani Qinwan3,Coogan Christopher2,Milsap Griffin W.4,Nathan Kevin C.2,Wester Brock A.4,Anderson William S.5ORCID,Rosenblatt Kathryn R.6,Uchil Alpa2,Clawson Lora2,Maragakis Nicholas J.2,Vansteensel Mariska J.7,Tenore Francesco V.4,Ramsey Nicolas F.7ORCID,Fifer Matthew S.4ORCID,Crone Nathan E.2ORCID

Affiliation:

1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD

2. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD

3. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD

4. Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD

5. Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD

6. Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD

7. Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, The Netherlands

Abstract

Abstract Background Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command “click” decoders 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 (ALS). We trained the participant’s click decoder using a small amount of training data (< 44 minutes across four 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 this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day).Conclusion These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Publisher

Research Square Platform LLC

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