A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface

Ferracuti, Francesco, Casadei, Valentina ORCID: 0000-0001-6391-8829, Marcantoni, Ilaria, Iarlori, Sabrina, Burattini, Laura, Monteriu, Andrea and Porcaro, Camillo (2020) A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface. Computer Methods and Programs in Biomedicine, 191. 105419-.

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Abstract

<h4>Background and objectives</h4>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 making 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 applied 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.<h4>Methods</h4>EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm 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.) RESULTS: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification.<h4>Conclusions</h4>The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.

Item Type:	Article
Uncontrolled Keywords:	Brain computer interface (BCI), Electroencephalography (EEG), Error-related potential (ErrP), Functional source separation (FSS), P300, Spatial filter
Depositing User:	Symplectic Admin
Date Deposited:	27 Apr 2020 10:19
Last Modified:	18 Jan 2023 23:55
DOI:	10.1016/j.cmpb.2020.105419
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3082659