Effects of Spatial Stimulus Overlap in a Visual P300-based Brain-computer Interface

Neuroscience ◽  
2020 ◽  
Vol 431 ◽  
pp. 134-142 ◽  
Author(s):  
Álvaro Fernández-Rodríguez ◽  
María Teresa Medina-Juliá ◽  
Francisco Velasco-Álvarez ◽  
Ricardo Ron-Angevin
PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e53513 ◽  
Author(s):  
Sebastian Halder ◽  
Eva Maria Hammer ◽  
Sonja Claudia Kleih ◽  
Martin Bogdan ◽  
Wolfgang Rosenstiel ◽  
...  

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Patrick Schembri ◽  
Maruisz Pelc ◽  
Jixin Ma

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.


2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
R. Ron-Angevin ◽  
L. Garcia ◽  
Á. Fernández-Rodríguez ◽  
J. Saracco ◽  
J. M. André ◽  
...  

The vast majority of P300-based brain-computer interface (BCI) systems are based on the well-known P300 speller presented by Farwell and Donchin for communication purposes and an alternative to people with neuromuscular disabilities, such as impaired eye movement. The purpose of the present work is to study the effect of speller size on P300-based BCI usability, measured in terms of effectiveness, efficiency, and satisfaction under overt and covert attention conditions. To this end, twelve participants used three speller sizes under both attentional conditions to spell 12 symbols. The results indicated that the speller size had, in both attentional conditions, a significant influence on performance. In both conditions (covert and overt), the best performances were obtained with the small and medium speller sizes, both being the most effective. The speller size did not significantly affect workload on the three speller sizes. In contrast, covert attention condition produced very high workload due to the increased resources expended to complete the task. Regarding users’ preferences, significant differences were obtained between speller sizes. The small speller size was considered as the most complex, the most stressful, the less comfortable, and the most tiring. The medium speller size was always considered in the medium rank, which is the speller size that was evaluated less frequently and, for each dimension, the worst one. In this sense, the medium and the large speller sizes were considered as the most satisfactory. Finally, the medium speller size was the one to which the three standard dimensions were collected: high effectiveness, high efficiency, and high satisfaction. This work demonstrates that the speller size is an important parameter to consider in improving the usability of P300 BCI for communication purposes. The obtained results showed that using the proposed medium speller size, performance and satisfaction could be improved.


2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Francesco Cavrini ◽  
Luigi Bianchi ◽  
Lucia Rita Quitadamo ◽  
Giovanni Saggio

We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.


2011 ◽  
Vol 71 ◽  
pp. e98
Author(s):  
Koichi Mori ◽  
Toshinori Maruoka ◽  
Minae Okada ◽  
Kazuyuki Itoh ◽  
Takenobu Inoue

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