Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 26, Issue 1, Pages 26-36Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2017.2755018
Keywords
Double error-related potentials (ErrP); automatic error correction; brain-computer interface (BCI); P300 ERP; electroencephalogram (EEG); speller
Categories
Funding
- Ph.D. Scholarship of Aniana Cruz - Portuguese foundation for science and technology [SFRH/BD/111473/2015]
- FEDER through COMPETE
- Portugal 2020 program [UID/EEA/00048/2013]
- [AMS-HMI12: RECI/EEI-AUT/0181/2012]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/111473/2015] Funding Source: FCT
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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.
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