Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.

Neural correlates of user learning during long-term BCI training for the Cybathlon competition

Tortora, Stefano
;
Beraldo, Gloria;Bettella, Francesco;Formaggio, Emanuela;Rubega, Maria;Del Felice, Alessandra;Masiero, Stefano;Carli, Ruggero;Petrone, Nicola;Menegatti, Emanuele;Tonin, Luca
2022

Abstract

Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3452837
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