Group-member selection for RSVP-based collaborative brain-computer interfaces.

Objective: The brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost. However, the key factors that enhance the collaboration capabilities of multiple users and how to further use these factors to optimize group mode remain unclear. Approach: This study proposed a group-member selection strategy to optimize the group mode and improve the system performance for RSVP-based cBCI. In contrast to the conventional grouping of collaborators at random, the group-member selection strategy enabled pairing each user with a better collaborator and allowed tasks to be done with fewer collaborators. Initially, we introduced the maximum individual capability and maximum collaborative capability (MIMC) to select optimal pairs, improving the system classification performance. The sequential forward floating selection (SFFS) combined with MIMC then selected a sub-group, aiming to reduce the hardware and labor expenses in the cBCI system. Moreover, the hierarchical discriminant component analysis (HDCA) was used as a classifier for within-session conditions, and the Euclidean space data alignment (EA) was used to overcome the problem of inter-trial variability for cross-session analysis. Main results: In this paper, we verified the effectiveness of the proposed group-member selection strategy on a public RSVP-based cBCI dataset. For the two-user matching task, the proposed MIMC had a significantly higher AUC and TPR and lower FPR than the common random grouping mode and the potential group-member selection method. Moreover, the SFFS with MIMC enabled a trade-off between maintaining performance and reducing the number of system users. Significance: The results showed that our proposed MIMC effectively optimized the group mode, enhanced the classification performance in the two-user matching task, and could reduce the redundant information by selecting the sub-group in the RSVP-based multi-user cBCI systems.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Nasal inhalation does not improve memory of visual repetitions.

Several studies suggest that breathing entrains neural oscillations and thereby improves visual detection and memory performance during nasal inhalation. However, the evidence for this association is mixed, with some studies finding no, minor, or opposite effects. Here, we tested whether nasal breathing phase influences memory of repeated images presented in a rapid serial visual presentation (RSVP) task. The RSVP task is ideal for studying the effects of respiratory-entrained oscillations on visual memory because it engages critical aspects of sensory encoding that depend on oscillatory activity, such as fast processing of natural images, repetition detection, memory encoding, and retrieval. It also enables the presentation of a large number of stimuli during each phase of the breathing cycle. In two separate experiments (n = 72 and n = 142, respectively) where participants were explicitly asked to breathe through their nose, we found that nasal breathing phase at target presentation did not significantly affect memory performance. An exploratory analysis in the first experiment suggested a potential benefit for targets appearing approximately 1 s after inhalation. However, this finding was not replicated in the pre-registered second experiment with a larger sample. Thus, in two large sample experiments, we found no measurable impact of breathing phase on memory performance in the RSVP task. These results suggest that the natural breathing cycle does not have a significant impact on memory for repeated images and raise doubts about the idea that visual memory is broadly affected by the breathing phase.

Exploring Binocular Visual Attention by Presenting Rapid Dichoptic and Dioptic Series.

This study addresses an issue in attentional distribution in a binocular visual system using RSVP tasks under Attentional Blink (AB) experimental protocols. In Experiment 1, we employed dichoptic RSVP to verify whether, under interocular competition, attention may be captured by a monocular channel. Experiment 2 was a control experiment, where a monoptic RSVP assessed by both or only one eye determines whether Experiment 1 monocular condition results were due to an allocation of attention to one eye. Experiment 3 was also a control experiment designed to determine whether Experiment 1 results were due to the effect of interocular competition or to a diminished visual contrast. Results from Experiment 1 revealed that dichoptic presentations caused a delay in the type stage of the Wyble's eSTST model, postponing the subsequent tokenization process. The delay in monocular conditions may be further explained by a visual attenuation, due to fusion of target and an empty frame. Experiment 2 evidenced the attentional allocation to monocular channels when forced by eye occlusion. Experiment 3 disclosed that monocular performance in Experiment 1 differs significantly from conditions with interocular competition. While both experiments revealed similar performance in monocular conditions, rivalry conditions exhibit lower detection rates, suggesting that competing stimuli was not responsible for Experiment 1 results. These findings highlight the differences between dichoptic and monoptic presentations of stimuli, particularly on the AB effect, which appears attenuated or absent in dichoptic settings. Furthermore, results suggest that monoptic presentation and binocular fusion stages were a necessary condition for the attentional allocation.

PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task.

Introduction: Accurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes. Methods: Given these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level. Results: Compared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05). Discussion: These results suggest that the proposed model effectively extracts features from both temporal and spatial dimensions of P300, leading to a more accurate classification of single-trial EEG during RSVP tasks. Therefore, this model has the potential to significantly enhance the performance of target recognition systems based on EEG, contributing to the advancement and practical implementation of target recognition in this field.

Multiband task related components enhance rapid cognition decoding for both small and similar objects.

The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain-Computer Interface.

The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

Action video games and posterior parietal cortex neuromodulation enhance both attention and reading in adults with developmental dyslexia.

The impact of action video games on reading performance has been already demonstrated in individuals with and without neurodevelopmental disorders. The combination of action video games and posterior parietal cortex neuromodulation by a transcranial random noise stimulation could enhance brain plasticity, improving attentional control and reading skills also in adults with developmental dyslexia. In a double blind randomized controlled trial, 20 young adult nonaction video game players with developmental dyslexia were trained for 15 h with action video games. Half of the participants were stimulated with bilateral transcranial random noise stimulation on the posterior parietal cortex during the action video game training, whereas the others were in the placebo (i.e. sham) condition. Word text reading, pseudowords decoding, and temporal attention (attentional blink), as well as electroencephalographic activity during the attentional blink, were measured before and after the training. The action video game + transcranial random noise stimulation group showed temporal attention, word text reading, and pseudoword decoding enhancements and P300 amplitude brain potential changes. The enhancement in temporal attention performance was related with the efficiency in pseudoword decoding improvement. Our results demonstrate that the combination of action video game training with parietal neuromodulation increases the efficiency of visual attention deployment, probably reshaping goal-directed and stimulus-driven fronto-parietal attentional networks interplay in young adults with neurodevelopmental conditions.

Attentional blink in infants under 7 months.

Attentional blink manifests in infants at 7 months of age, indicating that the working memory capacity of 7-month-olds is comparable to that of adults. However, attentional blink in infants under 7 months is not well understood. In this study, we conducted two experiments to investigate attentional blink in 5- and 6-month-old infants. The results of Experiment 1 demonstrated that attentional blinks were not observed with either a short lag (200 ms) or a long lag (800 ms). This suggests that 5- and 6-month-olds are unable to consolidate both targets regardless of the temporal distance between the two. We then split the infants into two groups by their age and conducted Experiment 2 with infants aged younger and older than 180 days to compare their consolidating ability to observe whether they could recognize a single item at 100-ms speed by presenting the same visual stream that was used in Experiment 1 except that one target was eliminated. The results showed that infants over 180 days of age could identify a single target in the visual stream at 100-ms presentation speed, whereas infants under 180 days could not. The findings of the current study indicate that the limitation of working memory capacity in infants under 7 months of age is a possible reason for the lack of attentional blink.

Development of a novel rodent rapid serial visual presentation task reveals dissociable effects of stimulant versus nonstimulant treatments on attentional processes.

The rapid serial visual presentation (RSVP) task and continuous performance tasks (CPT) are used to assess attentional impairments in patients with psychiatric and neurological conditions. This study developed a novel touchscreen task for rats based on the structure of a human RSVP task and used pharmacological manipulations to investigate their effects on different performance measures. Normal animals were trained to respond to a target image and withhold responding to distractor images presented within a continuous sequence. In a second version of the task, a false-alarm image was included, so performance could be assessed relative to two types of nontarget distractors. The effects of acute administration of stimulant and nonstimulant treatments for ADHD (amphetamine and atomoxetine) were tested in both tasks. Methylphenidate, ketamine, and nicotine were tested in the first task only. Amphetamine made animals more impulsive and decreased overall accuracy but increased accuracy when the target was presented early in the image sequence. Atomoxetine improved accuracy overall with a specific reduction in false-alarm responses and a shift in the attentional curve reflecting improved accuracy for targets later in the image sequence. However, atomoxetine also slowed responding and increased omissions. Ketamine, nicotine, and methylphenidate had no specific effects at the doses tested. These results suggest that stimulant versus nonstimulant treatments have different effects on attention and impulsive behaviour in this rat version of an RSVP task. These results also suggest that RSVP-like tasks have the potential to be used to study attention in rodents.

[Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions.

Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 µV versus 7.84 ± 1.40 µV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain-computer interfaces in EEG-based target detection areas.

Prioritized Identification of Fearful Eyes during the Attentional Blink Is Not Automatic.

The eye region conveys considerable information regarding an individual's emotions, motivations, and intentions during interpersonal communication. Evidence suggests that the eye regions of an individual expressing emotions can capture attention more rapidly than the eye regions of an individual in a neutral affective state. However, how attentional resources affect the processing of emotions conveyed by the eye regions remains unclear. Accordingly, the present study employed a dual-target rapid serial visual presentation task: happy, neutral, or fearful eye regions were presented as the second target, with a temporal lag between two targets of 232 or 696 ms. Participants completed two tasks successively: Task 1 was to identify which species the upright eye region they had seen belonged to, and Task 2 was to identify what emotion was conveyed in the upright eye region. The behavioral results showed that the accuracy for fearful eye regions was lower than that for neutral eye regions under the condition of limited attentional resources; however, accuracy differences across the three types of eye regions did not reach significance under the condition of adequate attentional resources. These findings indicate that preferential processing of fearful expressions is not automatic but is modulated by available attentional resources.

Differences in the duration of the attentional blink when viewing nature vs. urban scenes.

The current study examined how viewing nature vs. urban scenes impacts the duration of the attentional blink. Nature scenes produce a broader allocation of attention, allowing attention to spread and reduce the ability to disengage attention. Urban scenes produce a narrowed allocation of attention, allowing efficient encoding of relevant information, inhibition of irrelevant information and a speedier disengagement of attention. Participants viewed a rapid serial visual presentation (RSVP) of either nature or urban scenes. For both scene categories, an attentional blink was evident by reduced accuracy for reporting a second target that occurred two or three scenes after an accurately reported first target. However, the duration of the attentional blink was reduced for urban scenes compared with nature scenes. A peripheral target detection task confirmed a difference in the allocation of attention between scene categories. The peripheral targets were better detected for nature scenes, suggesting that participants have a broader spread of attention for nature scenes, even in an RSVP task. The shorter duration of the attentional blink for urban scenes was consistent across four experiments with small and large sets of urban and nature scenes. Therefore, urban scenes reliably reduce the attentional blink duration compared with nature scenes, and this could be attributed to a narrowed attention allocation that allows speedier disengagement of attention in an RSVP.

Perceptual load-independent modulation of the facilitated processing of emotional eye regions.

Although the eye region has been found to convey sufficient information for emotional recognition and interpersonal communication, little is known regarding the extent to which the prioritized processing of emotional eye regions relies on available attentional resources. To address this issue, the present study used a dual-target rapid serial visual presentation task in which the perceptual load levels of the first target (T1), as well as the valence of the second target (T2), were manipulated. In addition to the traditional event-related potential (ERP) analysis method, the mass univariate statistics approach was employed. Behaviorally, both happy and fearful eye regions were recognized more accurately than neutral eye regions, regardless of the T1 perceptual load. ERP findings revealed an enhanced N170 amplitude for fearful eye regions compared to neutral eye regions, confirming the preferential and automatic processing of fearful signals at the early sensory stage. The late positive potential component exhibited enhanced responses to fearful and happy eye regions, suggesting the amplified representation consolidation in working memory. Collectively, these findings indicate that isolated eye regions are processed automatically to a higher degree owing to their perceptual and motivational significance.

LDER: a classification framework based on ERP enhancement in RSVP task.

Objective.Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.Approach.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework.Main results.Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain-computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes.Significance.As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.

Measuring attentional blink magnitude: Reliability and validity of a novel single-target rapid serial visual presentation task index in a psychiatric sample.

Rapid serial visual presentation (RSVP) tasks have been frequently used to assess attentional control in psychiatric samples; however, it is unclear whether RSVP tasks exhibits the psychometric properties necessary to assess these individual differences. In the current study, we examined the reliability and validity of single-target computerized RSVP task outcomes in a sample of 63 participants with moderate to severe psychiatric illness. At the group level, we observed the classical attentional blink phenomenon. At the individual level, conventional indices of attentional blink magnitude exhibited poor internal consistency. We empirically evaluated a novel index for assessing attentional blink magnitude using a single-target RSVP task that involves collapsing across experimental trials in which the attentional blink phenomenon occurs and disregarding performance on control trials, which suffer from ceiling effects. We found that this new index resulted in much improved reliability estimates. Both novel and conventional indices provided evidence of convergent validity. Consequently, this novel index may be worth examining and adopting for researchers interested in assessing individual differences in attentional blink magnitude.

Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection.

Rapid Serial Visual Presentation (RSVP) based Brain-Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the RSVP-based BCI system limits its practical applications. This study introduces eye movements (gaze and pupil information), referred to as EYE modality, as another useful source of information to combine with EEG-based BCI and forms a novel target detection system to detect target images in RSVP tasks. We performed an RSVP experiment, recorded the EEG signals and eye movements simultaneously during a target detection task, and constructed a multi-modal dataset including 20 subjects. Also, we proposed a cross-modal guiding and fusion network to fully utilize EEG and EYE modalities and fuse them for better RSVP decoding performance. In this network, a two-branch backbone was built to extract features from these two modalities. A Cross-Modal Feature Guiding (CMFG) module was proposed to guide EYE modality features to complement the EEG modality for better feature extraction. A Multi-scale Multi-modal Reweighting (MMR) module was proposed to enhance the multi-modal features by exploring intra- and inter-modal interactions. And, a Dual Activation Fusion (DAF) was proposed to modulate the enhanced multi-modal features for effective fusion. Our proposed network achieved a balanced accuracy of 88.00% (±2.29) on the collected dataset. The ablation studies and visualizations revealed the effectiveness of the proposed modules. This work implies the effectiveness of introducing the EYE modality in RSVP tasks. And, our proposed network is a promising method for RSVP decoding and further improves the performance of RSVP-based target detection systems.

Research progress of brain-computer interface application paradigms based on rapid serial visual presentation / 生物医学工程学杂志

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP.

Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain-computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.