A Novel Three-Dimensional P300 Speller Based on Stereo Visual Stimuli

Qu, Jun; Wang, Fei; Xia, Zhenping; Yu, Tao; Xiao, Jing; Yu, Zhuliang; Gu, Zhenghui; Li, Yuanqing

doi:10.1109/thms.2018.2799525

Cited by 49 publications

(43 citation statements)

References 30 publications

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“…Therefore, it seems clear that the inability to move the eyes significantly affects the necessary cognitive resources used to control the interface. This disability should be considered in the case of several patients who cannot perform such action by using interfaces with an adequate size, since the medium size presented the best results despite the lack of significances, or new features that reduce the cognitive resources needed to use the speller (e.g., [44]).…”

Section: Discussionmentioning

confidence: 99%

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

Ron-Angevín

García

Fernández-Rodríguez

et al. 2019

Computational Intelligence and Neuroscience

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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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Section: Discussionmentioning

confidence: 99%

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

Ron-Angevín

García

Fernández-Rodríguez

et al. 2019

Computational Intelligence and Neuroscience

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“…They compared their proposed three-dimensional speller with two-dimensional speller. Higher classification accuracy was observed in three-dimensional P300 speller [21]. In recent studies such as Zhao et al contributed body of knowledge with their proposed audio-visual based row column P300 speller.…”

Section: Introductionmentioning

confidence: 94%

“…The user counts him/herself intensifying of target stimuli. Thus, oddball paradigm is occurred [21]. The target character is selected according to in which row and column whether P300 potential is elicited or not.…”

Section: Introductionmentioning

confidence: 99%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants ''A'' and ''B'' for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset. INDEX TERMS Brain computer interface, deep Learning, human machine systems, P300 detection.

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“…There were several studies with the aim of improving the performance of P300-BCI systems. For instance, Qu et al [13] focused on a new P300 speller paradigm based on three-dimensional (3-D) stereo visual stimuli, while Gu et al's [14] primary focus was within the single character P300 paradigm, i.e., they evaluated the posterior probability of each character in the stimuli set to be the target. Even though improving the performance and execution of the application is an important aspect, this should not come at the expense of the environment in which it is used, such as that of a real-world context.…”

Section: Research Backgroundmentioning

confidence: 99%

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

Schembri

Pelc

2020

Computers

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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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A Novel Three-Dimensional P300 Speller Based on Stereo Visual Stimuli

Cited by 49 publications

References 30 publications

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

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

3D Input Convolutional Neural Networks for P300 Signal Detection

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

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