Francesco Ferracuti1, Valentina Casadei2, Ilaria Marcantoni3, Sabrina Iarlori4, Laura Burattini5, Andrea Monteriù6, Camillo Porcaro7. 1. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: f.ferracuti@univpm.it. 2. Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, United Kingdom. Electronic address: valentina.casadei@liverpool.ac.uk. 3. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: i.marcantoni@pm.univpm.it. 4. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: s.iarlori@univpm.it. 5. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: l.burattini@univpm.it. 6. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: a.monteriu@staff.univpm.it. 7. Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy; Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium; S. Anna Institute and Research in Advanced Neurorehabilitation (RAN) Crotone, Italy; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom. Electronic address: camillo.porcaro@istc.cnr.it.
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.
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.
Authors: Matthew Moore; Edward L Maclin; Alexandru D Iordan; Yuta Katsumi; Ryan J Larsen; Andrew P Bagshaw; Stephen Mayhew; Andrea T Shafer; Bradley P Sutton; Monica Fabiani; Gabriele Gratton; Florin Dolcos Journal: Hum Brain Mapp Date: 2021-06-23 Impact factor: 5.038