Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions
- PMID: 33339105
- PMCID: PMC7765532
- DOI: 10.3390/s20247198
Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions
Abstract
The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.
Keywords: P300 BCI; performance assessment; visual stimuli paradigm.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Figures









Similar articles
-
The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement.PLoS One. 2012;7(11):e49688. doi: 10.1371/journal.pone.0049688. Epub 2012 Nov 26. PLoS One. 2012. PMID: 23189154 Free PMC article.
-
A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm.J Neural Eng. 2013 Apr;10(2):026012. doi: 10.1088/1741-2560/10/2/026012. Epub 2013 Feb 21. J Neural Eng. 2013. PMID: 23429035
-
Beyond maximum speed--a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI).J Neural Eng. 2014 Oct;11(5):056004. doi: 10.1088/1741-2560/11/5/056004. Epub 2014 Jul 31. J Neural Eng. 2014. PMID: 25080406
-
Effects of Spatial Stimulus Overlap in a Visual P300-based Brain-computer Interface.Neuroscience. 2020 Apr 1;431:134-142. doi: 10.1016/j.neuroscience.2020.02.011. Epub 2020 Feb 18. Neuroscience. 2020. PMID: 32081721
-
Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms.Clin EEG Neurosci. 2020 Jan;51(1):19-33. doi: 10.1177/1550059419842753. Epub 2019 Apr 18. Clin EEG Neurosci. 2020. PMID: 30997842 Review.
Cited by
-
Brain Neuroplasticity Leveraging Virtual Reality and Brain-Computer Interface Technologies.Sensors (Basel). 2024 Sep 3;24(17):5725. doi: 10.3390/s24175725. Sensors (Basel). 2024. PMID: 39275636 Free PMC article.
-
Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.Front Neurosci. 2023 Feb 22;17:1132290. doi: 10.3389/fnins.2023.1132290. eCollection 2023. Front Neurosci. 2023. PMID: 36908799 Free PMC article.
-
Summary of over Fifty Years with Brain-Computer Interfaces-A Review.Brain Sci. 2021 Jan 3;11(1):43. doi: 10.3390/brainsci11010043. Brain Sci. 2021. PMID: 33401571 Free PMC article. Review.
-
Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.Sensors (Basel). 2021 Sep 29;21(19):6503. doi: 10.3390/s21196503. Sensors (Basel). 2021. PMID: 34640824 Free PMC article.
References
-
- Soekadar S., Birbaumer N., Cohen L. Brain–Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma. Springer; Tokyo, Japan: 2011. pp. 3–18. - DOI
-
- Karácsony T., Hansen J.P., Iversen H.K., Puthusserypady S. Brain Computer Interface for Neuro-Rehabilitation with Deep Learning Classification and Virtual Reality Feedback; Proceedings of the 10th Augmented Human International Conference; Reims, France. 11–12 March 2019; New York, NY, USA: Association for Computing Machinery; 2019. - DOI
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Miscellaneous