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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Nature Portfolio
2024-10-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00635-3 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Daniel N. Candrea |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5c5b61fc56574dd6b4b5cc7132cd8687 |
| institution | OA Journals |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-5c5b61fc56574dd6b4b5cc7132cd86872025-08-20T02:11:47ZengNature PortfolioCommunications Medicine2730-664X2024-10-014111410.1038/s43856-024-00635-3A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spellingDaniel N. Candrea0Samyak Shah1Shiyu Luo2Miguel Angrick3Qinwan Rabbani4Christopher Coogan5Griffin W. Milsap6Kevin C. Nathan7Brock A. Wester8William S. Anderson9Kathryn R. Rosenblatt10Alpa Uchil11Lora Clawson12Nicholas J. Maragakis13Mariska J. Vansteensel14Francesco V. Tenore15Nicolas F. Ramsey16Matthew S. Fifer17Nathan E. Crone18Department of Biomedical Engineering, Johns Hopkins University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Biomedical Engineering, Johns Hopkins University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Electrical and Computer Engineering, Johns Hopkins UniversityDepartment of Neurology, Johns Hopkins University School of MedicineResearch and Exploratory Development Department, Johns Hopkins University Applied Physics LaboratoryDepartment of Neurology, Johns Hopkins University School of MedicineResearch and Exploratory Development Department, Johns Hopkins University Applied Physics LaboratoryDepartment of Neurosurgery, Johns Hopkins University School of MedicineDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Neurology and Neurosurgery, UMC Utrecht Brain CenterResearch and Exploratory Development Department, Johns Hopkins University Applied Physics LaboratoryDepartment of Neurology and Neurosurgery, UMC Utrecht Brain CenterResearch and Exploratory Development Department, Johns Hopkins University Applied Physics LaboratoryDepartment of Neurology, Johns Hopkins University School of MedicineAbstract 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.https://doi.org/10.1038/s43856-024-00635-3 |
| spellingShingle | 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 A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling Communications Medicine |
| title | A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling |
| title_full | A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling |
| title_fullStr | A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling |
| title_full_unstemmed | A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling |
| title_short | A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling |
| title_sort | click based electrocorticographic brain computer interface enables long term high performance switch scan spelling |
| url | https://doi.org/10.1038/s43856-024-00635-3 |
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