Methods: EEG data recorded on six subjects were used to evaluate the proposed method based on FFS al-gorithm in comparison with the xDAWN algorithm. FSS-and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.)

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

Porcaro Camillo
2020

Abstract

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.
2020
Istituto di Scienze e Tecnologie della Cognizione - ISTC
Methods: EEG data recorded on six subjects were used to evaluate the proposed method based on FFS al-gorithm in comparison with the xDAWN algorithm. FSS-and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.)
Brain computer interface (BCI)
Electroencephalography (EEG)
Error-related potential (ErrP)
Functional source separation (FSS)
P300, Spatial filter
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/389359
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