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- W2247649397 abstract "Even though the P300 based speller has proved to be usable by real patients, it is not a user-friendly system. The necesarry calibration session and slow spelling make the system tedious. We present a machine learning approach to P300 spelling that enables us to remove the calibration session. We achieve this by a combination of unsupervised training, transfer learning across subjects and language models. On top of that, we can increase the spelling speed by incorporating a dynamic stopping approach. This yields a P300 speller that works instantly and with high accuracy and spelling speed, even for unseen subjects." @default.
- W2247649397 created "2016-06-24" @default.
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- W2247649397 date "2013-01-01" @default.
- W2247649397 modified "2023-09-23" @default.
- W2247649397 title "Dynamic stopping in a calibration-less P300 speller" @default.
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- W2247649397 doi "https://doi.org/10.3217/978-3-85125-260-6-75" @default.
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