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Asynchronous gaze-independent event-related potential-based brain–computer interface
Open accessAbstract
Objective In this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state. Material and methods The proposed system was validated with the participation of 9 healthy subjects. A comparison was performed between asynchronous and synchronous classification technique outputs while users were controlling the same gaze independent BCI interface. The performance of both classification techniques were assessed both off-line and on-line by means of the efficiency metric introduced by Bianchi et al. (2007). This latter metric allows to set a different misclassification cost for wrong classifications and abstentions. Robustness was evaluated as the rate of false positives occurring during voluntary no-control states. Results The asynchronous classifier did not exhibited significantly higher accuracy or lower error rate with respect to the synchronous classifier (accuracy: 74.66% versus 87.96%, error rate: 7.11% versus 12.04% respectively). However, the on-line and off-line analysis revealed that the communication efficiency was significantly improved (p<.05) with the asynchronous classification modality as compared with the synchronous. Furthermore, the asynchronous classifier proved to be robust to false positives during intentional no-control state which occur during the ongoing visual stimulation (less than 1 false positive every 6min). Conclusion As such, the proposed ERP-BCI system which combines an asynchronous classifier with a gaze independent interface is a promising solution to be further explored in order to increase the general usability of ERP-based BCI systems designed for severely disabled people with an impairment of the voluntary control of eye movements. In fact, the asynchronous classifier can improve communication efficiency automatically adapting the number of stimulus repetitions to the current user's state and suspending the control if he/she does not intend to select an item.
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EEG features in the time domain (P300 ERPs) and frequency domain (EEG frequency bands) are modulated by difficulty of task variations.
“Through EEG measures, we extracted features (i) in the time domain (i.e., ERPs analysis, in particular P300 ERPs) and (ii) in the frequency domain (i.e., EEG frequency bands), both modulated the difficulty of the task variations32–34.”
From The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence (2021) · cites this paper as [34]
An increase in skin conductance is considered a reliable bioindicator of human arousal variations, and in virtual reality studies it is typically used to evaluate anxiety or stress related to a threat.
“An increase in skin conductance is considered a reliable bioindicator of human arousal variations34 and in virtual reality studies, it is typically used to evaluate the anxiety produced by a situation or the stress related to a threat26,27,30.”
From The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence (2021) · cites this paper as [34]
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Marucci, M., Di Flumeri, G., Borghini, G., Sciaraffa, N., Scandola, M., Pavone, E. F., Babiloni, F., Betti, V., & Aricò, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-84196-8 · Cited by 170
Self-citationPerformance Enhancement of P300 Detection by Multiscale-CNN
Wang, H., Pei, Z., Xu, L., Xu, T., Bezerianos, A., Sun, Y., & Li, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067943 · Cited by 35
Metadata
- Authors
- Aloise, F., Aricò, P., Schettini, F., Salinari, S., Mattia, D., & Cincotti, F.
- Affiliations
- Fondazione Santa Lucia (IT)
- Sapienza University of Rome (IT)
- Corresponding author
- Aloise, Fabio
- ORCID
- Aricò, Pietro
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 59
- Issue
- 2
- Pages
- 61-69
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2048836001
- Cited by
- 43
- References
- 1
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 5 · 2021: 4 · 2020: 4 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 3 · 2015: 5 · 2014: 2 · 2013: 1
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- EU (FP7-224631)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
- Keywords
- event-related potentials, brain-computer interface, communication efficiency, false positives, severely disabled users
- MeSH terms
- Adult, Evoked Potentials, Humans, Young Adult, Brain-Computer Interfaces
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