A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling
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 performanc...
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| Published in: | Communications medicine Vol. 4; no. 1; pp. 207 - 14 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
22-10-2024
Springer Nature B.V Nature Portfolio |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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.
Plain Language Summary
Amyotrophic lateral sclerosis (ALS) is a progressive disease of the nervous system that causes muscle weakness and leads to paralysis. People living with ALS therefore struggle to communicate with family and caregivers. We investigated whether the brain signals of a participant with ALS could be used to control a spelling application. Specifically, when the participant attempted a grasping movement, a computer method detected increased brain signals from electrodes implanted on the surface of his brain, and thereby generated a mouse-click. The participant clicked on letters or words from a spelling application to type sentences. Our method was trained using 44 min’ worth of brain signals and performed reliably for three months without any retraining. This approach can potentially be used to restore communication to other severely paralyzed individuals over an extended time period and after only a short training period.
Candrea et al. develop a brain-computer interface click detection algorithm using electrocorticographic signals. Using this click detector, a clinical trial participant with amyotrophic lateral sclerosis was able to control a switch-scanning spelling application and achieve high rates of spelling without model retraining. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2730-664X 2730-664X |
| DOI: | 10.1038/s43856-024-00635-3 |