Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potent...

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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering Jg. 26; H. 1; S. 26 - 36
Hauptverfasser:	Cruz, Aniana, Pires, Gabriel, Nunes, Urbano J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2018
Schlagworte:	Adult Algorithms automatic error correction brain-computer interface (BCI) Brain-Computer Interfaces - classification Calibration Communication Aids for Disabled Double error-related potentials (ErrP) Electric potential electroencephalogram (EEG) Electroencephalography Electroencephalography - classification Equipment Design Error correction Event-Related Potentials, P300 - physiology Feedback, Psychological Female Healthy Volunteers Humans Male Online Systems P300 ERP Reliability Reproducibility of Results speller Young Adult
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.
AbstractList	Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems. Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.
Author	Nunes, Urbano J. Cruz, Aniana Pires, Gabriel
Author_xml	– sequence: 1 givenname: Aniana surname: Cruz fullname: Cruz, Aniana email: anianabrito@isr.uc.pt organization: Institute for Systems and Robotics, University of Coimbra, Coimbra, Portugal – sequence: 2 givenname: Gabriel orcidid: 0000-0001-9967-845X surname: Pires fullname: Pires, Gabriel email: gpires@isr.uc.pt organization: Institute for Systems and Robotics, University of Coimbra, Coimbra, Portugal – sequence: 3 givenname: Urbano J. orcidid: 0000-0002-7750-5221 surname: Nunes fullname: Nunes, Urbano J. email: urbano@isr.uc.pt organization: Institute for Systems and Robotics, University of Coimbra, Coimbra, Portugal
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SubjectTerms	Adult Algorithms automatic error correction brain-computer interface (BCI) Brain-Computer Interfaces - classification Calibration Communication Aids for Disabled Double error-related potentials (ErrP) Electric potential electroencephalogram (EEG) Electroencephalography Electroencephalography - classification Equipment Design Error correction Event-Related Potentials, P300 - physiology Feedback, Psychological Female Healthy Volunteers Humans Male Online Systems P300 ERP Reliability Reproducibility of Results speller Young Adult
Title	Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller
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