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

Nathan Crone, Daniel Candrea, Samyak Shah, Shiyu Luo, Miguel Angrick, Qinwan Rabbani, Christopher Coogan, Griffn Milsap, Kevin Nathan, Brock Wester, William Anderson, Kathryn Rosenblatt, Lora Clawson, Nicholas Maragakis, Mariska Vansteensel, Francesco Tenore, Nick Ramsey, Matthew Fifer, Alpa Uchil

Research output: Working paperPreprintAcademic

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.

Original languageEnglish
PublisherResearch Square
Pages1-26
Number of pages26
DOIs
Publication statusPublished - 25 Sept 2023

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