Background and objectives: An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are mak-ing an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely ap-plied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials.
A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface
Elsevier, London ;, Paesi Bassi
Computer methods and programs in biomedicine (Print) 191 (2020). doi:10.1016/j.cmpb.2020.105419
info:cnr-pdr/source/autori:Ferracuti, Francesco; Casadei, Valentina; Marcantoni, Ilaria; Iarlori, Sabrina; Burattini, Laura; Monteriu, Andrea; Porcaro, Camillo/titolo:A functional source separation algorithm to enhance error-related potentials monitoring