Brain-Computer Interfaces (BCIs) represent groundbreaking technology, offering substantial improvements in the quality of life for individuals with severe motor disabilities. This study focuses on the identification of Error-Related Potentials (ErrPs) and their key characteristics during the continuous control of a powered wheelchair. Participants were instructed to navigate the powered wheelchair using a joystick along a predetermined path with obstacles, while EEG data were collected. ErrP was successfully identified when the wheelchair executed an unexpected command not initiated by the user. The analysis, including artifact removal and alignment of ErrP peaks, was performed offline. The obtained results are comparable to studies in the literature for discrete control. This groundbreaking finding holds significant implications for future advancements in continuous BCI control, aiming to enhance the user experience.
Le Brain-Computer Interfaces (BCI) rappresentano una tecnologia rivoluzionaria, offrendo miglioramenti sostanziali nella qualità della vita per individui con gravi disabilità motorie. Questo studio si concentra sull’identificazione dei Potenziali di Errore (ErrP) e delle loro caratteristiche chiave durante il controllo continuo di una sedia a rotelle motorizzata. Ai partecipanti è stato chiesto di guidare la sedia a rotelle motorizzata utilizzando un joystick lungo un percorso prestabilito con ostacoli, mentre venivano registrati dati EEG. Gli ErrP sono stati identificati con successo quando la sedia a rotelle ha eseguito un comando inaspettato non generato dall’utente. L’analisi, comprensiva della rimozione degli artefatti e dell’allineamento dei picchi ErrP, è stata eseguita offline. I risultati ottenuti sono confrontabili con gli studi presenti in letteratura per il controllo discreto. Questa scoperta rivoluzionaria ha significative implicazioni per futuri progressi nel controllo continuo BCI, con l’obiettivo di migliorare l’esperienza dell’utente.
Error-Related Potential identification during continuous control of a powered wheelchair
BERGAMINI, LORENZO
2022/2023
Abstract
Brain-Computer Interfaces (BCIs) represent groundbreaking technology, offering substantial improvements in the quality of life for individuals with severe motor disabilities. This study focuses on the identification of Error-Related Potentials (ErrPs) and their key characteristics during the continuous control of a powered wheelchair. Participants were instructed to navigate the powered wheelchair using a joystick along a predetermined path with obstacles, while EEG data were collected. ErrP was successfully identified when the wheelchair executed an unexpected command not initiated by the user. The analysis, including artifact removal and alignment of ErrP peaks, was performed offline. The obtained results are comparable to studies in the literature for discrete control. This groundbreaking finding holds significant implications for future advancements in continuous BCI control, aiming to enhance the user experience.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/60577