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

•Semi Blind functional source separation (FSS) identify optimal spatial filter for BCI.•FSS algorithm is able to enhance error-related potential (ErrPs) monitoring in non-invasive BCI.•Bayesian linear classification shows higher accuracy for FSS respect to single EEG electrode.•Bayesian linear class...

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Vydáno v:Computer methods and programs in biomedicine Ročník 191; s. 105419
Hlavní autoři: Ferracuti, Francesco, Casadei, Valentina, Marcantoni, Ilaria, Iarlori, Sabrina, Burattini, Laura, Monteriù, Andrea, Porcaro, Camillo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Ireland Elsevier B.V 01.07.2020
Témata:
Algorithms
Bayes Theorem
Brain computer interface (BCI)
Brain-Computer Interfaces
Electroencephalography (EEG)
Electroencephalography - methods
Error-related potential (ErrP)
Event-Related Potentials, P300
Evoked Potentials
Functional source separation (FSS)
Humans
P300, Spatial filter
Signal Processing, Computer-Assisted
CSP
SSNR
Functional source separation (FSS)
C
EEG
ICA
FSS
ROC
BCI
ErrP
Error-related potential (ErrP)
P300, Spatial filter
Brain computer interface (BCI)
AUC
Electroencephalography (EEG)
FDR
NC
SNR
BLDA
EA
ISSN:0169-2607, 1872-7565, 1872-7565
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Shrnutí:•Semi Blind functional source separation (FSS) identify optimal spatial filter for BCI.•FSS algorithm is able to enhance error-related potential (ErrPs) monitoring in non-invasive BCI.•Bayesian linear classification shows higher accuracy for FSS respect to single EEG electrode.•Bayesian linear classification shows higher accuracy for FSS respect to xDAWN spatial filter. 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. 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.) 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. 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2020.105419