A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling Article Swipe
Daniel N. Candrea
,
Samyak Shah
,
Shiyu Luo
,
Miguel Angrick
,
Qinwan Rabbani
,
Christopher Coogan
,
Griffin Milsap
,
Kevin Nathan
,
Brock A. Wester
,
William S. Anderson
,
Kathryn Rosenblatt
,
Alpa Uchil
,
Lora Clawson
,
Nicholas J. Maragakis
,
Mariska J. Vansteensel
,
Francesco V. Tenore
,
Nick F. Ramsey
,
Matthew S. Fifer
,
Nathan E. Crone
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3158792/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3158792/v1
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.
Related Topics
Concepts
Brain–computer interface
Computer science
Interface (matter)
Spelling
Speech recognition
Electrocorticography
Decoding methods
Artificial intelligence
Computer hardware
Electroencephalography
Neuroscience
Psychology
Philosophy
Maximum bubble pressure method
Linguistics
Parallel computing
Telecommunications
Bubble
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3158792/v1
- https://www.researchsquare.com/article/rs-3158792/latest.pdf
- OA Status
- green
- Cited By
- 2
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387025589
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387025589Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3158792/v1Digital Object Identifier
- Title
-
A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spellingWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-25Full publication date if available
- Authors
-
Daniel N. Candrea, Samyak Shah, Shiyu Luo, Miguel Angrick, Qinwan Rabbani, Christopher Coogan, Griffin Milsap, Kevin Nathan, Brock A. Wester, William S. Anderson, Kathryn Rosenblatt, Alpa Uchil, Lora Clawson, Nicholas J. Maragakis, Mariska J. Vansteensel, Francesco V. Tenore, Nick F. Ramsey, Matthew S. Fifer, Nathan E. CroneList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-3158792/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3158792/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-3158792/latest.pdfDirect OA link when available
- Concepts
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Brain–computer interface, Computer science, Interface (matter), Spelling, Speech recognition, Electrocorticography, Decoding methods, Artificial intelligence, Computer hardware, Electroencephalography, Neuroscience, Psychology, Philosophy, Maximum bubble pressure method, Linguistics, Parallel computing, Telecommunications, BubbleTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_institution_ids | https://openalex.org/I145311948, https://openalex.org/I2799853436, https://openalex.org/I2802946424, https://openalex.org/I3018483916 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.61285866 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |