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Conference proceeding
Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), v 2018, pp 360-363
Jul 2018
PMID: 30440411
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g., feature/channel selection), thus selecting fronto-central channels as discriminative features within-subject. In this paper, we propose the inclusion of error-related potential activity as a generalized two-dimensional feature set and a Convolutional Neural Network for classification of EEG-based human error detection. We evaluate this pipeline using the BNCI2020 - Monitoring Error-Related Potential dataset obtaining a maximum error detection accuracy of 79.8% in a within-session 10-fold cross-validation modality, and outperforming current state of the art.
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Details
- Title
- Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks
- Creators
- Juan M. Mayor Torres - University of TrentoTessa Clarkson - Stony Brook UniversityEvgeny A. Stepanov - University of TrentoChristian C. Luhmann - Stony Brook UniversityMatthew D. Lerner - Stony Brook UniversityGiuseppe Riccardi - University of Trento
- Publication Details
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), v 2018, pp 360-363
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- A.J. Drexel Autism Institute
- Web of Science ID
- WOS:000596231900082
- Scopus ID
- 2-s2.0-85056635681
- Other Identifier
- 991021861842704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Biomedical
- Engineering, Electrical & Electronic