Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement

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.

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The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

Computers ◽

10.3390/computers9030068 ◽

2020 ◽

Vol 9 (3) ◽

pp. 68

Author(s):

Patrick Schembri ◽

Maruisz Pelc ◽

Jixin Ma

Keyword(s):

Brain Computer Interface ◽

Low Cost ◽

User Preference ◽

Computer Interface ◽

Oddball Paradigm ◽

Active Listening ◽

P300 Speller ◽

Non Invasive ◽

Auditory Distractions ◽

Visual P300

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.

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The Study of Influence of Sound on Visual ERP-Based Brain Computer Interface

Sensors ◽

10.3390/s20041203 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1203 ◽

Cited By ~ 1

Author(s):

Guizhi Xu ◽

Yuwei Wu ◽

Mengfan Li

Keyword(s):

Information Transfer ◽

High Performance ◽

Brain Computer Interface ◽

Event Related Potential ◽

Training Data ◽

Computer Interface ◽

Information Transfer Rate ◽

High Workload ◽

The One ◽

Study Designs

The performance of the event-related potential (ERP)-based brain–computer interface (BCI) declines when applying it into the real environment, which limits the generality of the BCI. The sound is a common noise in daily life, and whether it has influence on this decline is unknown. This study designs a visual-auditory BCI task that requires the subject to focus on the visual interface to output commands and simultaneously count number according to an auditory story. The story is played at three speeds to cause different workloads. Data collected under the same or different workloads are used to train and test classifiers. The results show that when the speed of playing the story increases, the amplitudes of P300 and N200 potentials decrease by 0.86 μV (p = 0.0239) and 0.69 μV (p = 0.0158) in occipital-parietal area, leading to a 5.95% decline (p = 0.0101) of accuracy and 9.53 bits/min decline (p = 0.0416) of information transfer rate. The classifier that is trained by the high workload data achieves higher accuracy than the one trained by the low workload if using the high workload data to test the performance. The result indicates that the sound could affect the visual ERP-BCI by increasing the workload. The large similarity of the training data and testing data is as important as the amplitudes of the ERP on obtaining high performance, which gives us an insight on how make to the ERP-BCI generalized.

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Extending brain-computer interface access with a multilingual language model in the P300 speller

Brain-Computer Interfaces ◽

10.1080/2326263x.2021.1993426 ◽

2021 ◽

pp. 1-13

Author(s):

P Loizidou ◽

E Rios ◽

A Marttini ◽

O Keluo-Udeke ◽

J Soetedjo ◽

...

Keyword(s):

Brain Computer Interface ◽

Language Model ◽

Computer Interface ◽

P300 Speller

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Eye-Movement-Control-Independent Brain Computer Interface Using Modulation of Steady-State Responses in Visual Evoked Potentials

SICE Journal of Control Measurement and System Integration ◽

10.9746/jcmsi.11.438 ◽

2018 ◽

Vol 11 (6) ◽

pp. 438-445

Author(s):

Seiji NISHIFUJI ◽

Hirotaka NAKAMURA ◽

Atsushi MATSUBARA ◽

Akira IWATA ◽

Hitoshi HIRANO

Keyword(s):

Steady State ◽

Evoked Potentials ◽

Eye Movement ◽

Visual Evoked Potentials ◽

Brain Computer Interface ◽

Movement Control ◽

Computer Interface ◽

Eye Movement Control ◽

Steady State Responses ◽

State Responses

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A P300 brain-computer interface with a reduced visual field

10.1101/2020.09.07.285379 ◽

2020 ◽

Author(s):

Luiza Kirasirova ◽

Vladimir Bulanov ◽

Alexei Ossadtchi ◽

Alexander Kolsanov ◽

Vasily Pyatin ◽

...

Keyword(s):

Visual Field ◽

Visual Processing ◽

Peripheral Vision ◽

Brain Computer Interface ◽

Basic Research ◽

Computer Interface ◽

Eye Tracker ◽

Central Visual Field ◽

P300 Speller ◽

User Fatigue

AbstractA P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from visual evoked potentials (VEPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized by the strongest VEP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command. To tackle this approach further, we ran a pilot experiment where ten subjects first operated a traditional P300 speller and then wore a binocular aperture that confined their sight to the central visual field. Visual field restriction resulted in a reduction of non-target responses in all subjects. Moreover, in four subjects, target-related VEPs became more distinct. We suggest that this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

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Towards the detection of error-related potentials and its integration in the context of a P300 speller brain–computer interface

Neurocomputing ◽

10.1016/j.neucom.2011.09.013 ◽

2012 ◽

Vol 80 ◽

pp. 73-82 ◽

Cited By ~ 31

Author(s):

A. Combaz ◽

N. Chumerin ◽

N.V. Manyakov ◽

A. Robben ◽

J.A.K. Suykens ◽

...

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

P300 Speller ◽

Related Potentials

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A P300 Brain-Computer Interface With a Reduced Visual Field

Frontiers in Neuroscience ◽

10.3389/fnins.2020.604629 ◽

2020 ◽

Vol 14 ◽

Author(s):

Luiza Kirasirova ◽

Vladimir Bulanov ◽

Alexei Ossadtchi ◽

Alexander Kolsanov ◽

Vasily Pyatin ◽

...

Keyword(s):

Visual Field ◽

Visual Processing ◽

Classification Accuracy ◽

Peripheral Vision ◽

Brain Computer Interface ◽

Event Related Potentials ◽

Computer Interface ◽

Central Visual Field ◽

P300 Speller ◽

Related Potentials

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

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