A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface
Copyright © 2020. Published by Elsevier B.V..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:191 |
|---|---|
| Enthalten in: |
Computer methods and programs in biomedicine - 191(2020) vom: 30. Juli, Seite 105419 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ferracuti, Francesco [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Brain computer interface (BCI) |
|---|
| Anmerkungen: |
Date Completed 12.04.2021 Date Revised 12.04.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.cmpb.2020.105419 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM307402355 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM307402355 | ||
| 003 | DE-627 | ||
| 005 | 20231225125502.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.cmpb.2020.105419 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1024.xml |
| 035 | |a (DE-627)NLM307402355 | ||
| 035 | |a (NLM)32151908 | ||
| 035 | |a (PII)S0169-2607(19)31635-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ferracuti, Francesco |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 12.04.2021 | ||
| 500 | |a Date Revised 12.04.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020. Published by Elsevier B.V. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Brain computer interface (BCI) | |
| 650 | 4 | |a Electroencephalography (EEG) | |
| 650 | 4 | |a Error-related potential (ErrP) | |
| 650 | 4 | |a Functional source separation (FSS) | |
| 650 | 4 | |a P300, Spatial filter | |
| 700 | 1 | |a Casadei, Valentina |e verfasserin |4 aut | |
| 700 | 1 | |a Marcantoni, Ilaria |e verfasserin |4 aut | |
| 700 | 1 | |a Iarlori, Sabrina |e verfasserin |4 aut | |
| 700 | 1 | |a Burattini, Laura |e verfasserin |4 aut | |
| 700 | 1 | |a Monteriù, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Porcaro, Camillo |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computer methods and programs in biomedicine |d 1993 |g 191(2020) vom: 30. Juli, Seite 105419 |w (DE-627)NLM012836133 |x 1872-7565 |7 nnns |
| 773 | 1 | 8 | |g volume:191 |g year:2020 |g day:30 |g month:07 |g pages:105419 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.cmpb.2020.105419 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 191 |j 2020 |b 30 |c 07 |h 105419 | ||