A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface. (July 2020)

• Record Type: Journal Article
• Authors: Ferracuti, Francesco; Casadei, Valentina; Marcantoni, Ilaria; Iarlori, Sabrina; Burattini, Laura; Monteriù, Andrea; Porcaro, Camillo
• Is Part Of: Computer methods and programs in biomedicine. Volume 191(2020)
• Publication Date: 2020-07
• Subjects: Brain computer interface (BCI) -- Electroencephalography (EEG) -- Error-related potential (ErrP) -- Functional source separation (FSS) -- P300, Spatial filter; AUC Area Under Curve -- BLDA Bayesian Linear Discriminant Analysis -- BCI Brain-Computer Interface -- C Correct trials -- CSP Common Spatial Pattern -- EEG electroencephalography -- ErrP Error-Related Potential -- EA Evoked Activity -- FDR False Discovery Rate -- FSS Functional Source Separation -- ICA Independent Component Analysis -- NC Non-Correct trials -- ROC Receiver Operating Characteristic -- SNR Signal-to-Noise Ratio -- SSNR Signal-to-Signal plus Noise Ratio; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105419
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: 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. 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 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. Methods: 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. Conclusions: 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.