Stimulus and Response-Locked P3 Activity in a Dynamic Rapid Serial Visual Presentation (RSVP) Task

Objective. Brain–computer interface (BCI) systemsdirectly translate human intentions to instructions for machines by decoding the neural signals. The rapid serial visual presentation (RSVP) task is a typical paradigm of BCIs, in which subjects can detect the targets in the high-speed serial images. There are still two main challenges in electroencephalography (EEG) classification for RSVP tasks: inter-trial variability of event-related potentials (ERPs) and limited trial number of EEG training data.Approach.This study proposed an algorithm of discriminant analysis and classification for interval ERPs (DACIE) in RSVP tasks. Firstly, an interval model of ERPs was exploited to solve the inter-trial variability problem. Secondly, a spatial structured sparsity regularization was utilized to reinforce the important channels, which provided a spatial region of interest (sROI). Meanwhile, a temporal auto-weighting technique was conducted to emphasize the important discriminant components, which obtained a temporal regions of interest (tROIs). Thirdly, classification features were obtained by the discriminant eigenvalue analysis to avoid the ill-conditioned estimation of covariance matrix caused by fewer training trials.Main results.EEG datasets of 12 subjects in RSVP tasks were analyzed to evaluate the classification performance of proposed algorithm. The average accuracy rate, true positive rate, false positive rate and AUC value are 96.9%, 81.6%, 2.8% and 0.938, respectively. Compared with several state-of-the-art algorithms, the proposed algorithm can provide significantly better classification performance.Significance.The interval model of ERPs was exploited in a spatial linear discriminant framework to overcome the inter-trial variability. The sROIs and tROIs were explored to reinforce the pivotal channels and temporal components. And the proposed algorithm can provide good performance with fewer training trials.

Electroencephalography (EEG) datasets are characterized by low signal-to-noise signals and unquantifiable noisy labels, which hinder the classification performance in rapid serial visual presentation (RSVP) tasks. Previous approaches primarily relied on supervised learning (SL), which may result in overfitting and reduced generalization performance. In this paper, we propose a novel multi-task collaborative network (MTCN) that integrates both SL and self-supervised learning (SSL) to extract more generalized EEG representations. The original SL task, i.e., the RSVP EEG classification task, is used to capture initial representations and establish classification thresholds for targets and non-targets. Two SSL tasks, including the masked temporal/spatial recognition task, are designed to enhance temporal dynamics extraction and capture the inherent spatial relationships among brain regions, respectively. The MTCN simultaneously learns from multiple tasks to derive a comprehensive representation that captures the essence of all tasks, thus mitigating the risk of overfitting and enhancing generalization performance. Moreover, to facilitate collaboration between SL and SSL, MTCN explicitly decomposes features into task-specific features and task-shared features, leveraging both label information with SL and feature information with SSL. Experiments conducted on THU, CAS, and GIST datasets illustrate the significant advantages of learning more generalized features in RSVP tasks. Our code is publicly accessible at https://github.com/Tammie-Li/MTCN.

Fatigue has an impact on the target recognition accuracy in small target detection task based on the rapid serial visual presentation (RSVP) paradigm. It is necessary to evaluate brain fatigue objectively to adjust RSVP parameters, so as to prolong the effective task execution time. This paper proposes a method to evaluate the degree of brain fatigue based on reaction time and Oxyhemoglobin (HBO) index of functional near-infrared spectroscopy (fNIRS). A 100-min RSVP task was designed to induce the fatigue state. The fNIRS data of 17 channels in the prefrontal cortex (PFC) region and 440 groups of auditory key reaction time data of 11 subjects were collected and analyzed. The degree of brain fatigue can be divided into conscious state and fatigue state by combining the subjective evaluation scale, probability density distribution and cumulative distribution of reaction time, and the concentration change of HBO. The results show that, in the RSVP task, with the increase of brain fatigue, the reaction time of human gradually increases and the overall distribution is lognormal, the reaction time of HBO reduces and the concentration increases in PFC at the same time. Therefore, the reaction time index and fNIRS index can be used as potential indicators to evaluate the brain fatigue model in RSVP small target detection task, and provide the basis for the later research on the monitoring and regulation of brain fatigue.

Evidence for adaptive event integration has previously been provided using the Rapid Serial Visual Presentation (RSVP) task. However, it is not straightforward to generalize this finding to other types of tasks that measure temporal integration, because integration in such tasks is known to vary, depending on the method that is used. This variability has been seen as an indication that integration may result from more than a single type of perceptual persistence, and that different integration tasks may not tap into same type of persistence. Therefore, we investigated whether adaptive control of integration in the RSVP task can be replicated using another technique for measuring temporal integration, which may rely more on low-level mechanisms, namely the dot-array integration or Missing Element Task (MET). As in the RSVP studies, stimulus speed expectancy was presently manipulated. The results indicated that integration performance in the MET was not subject to adaptive control. We argue that this discrepancy with previous RSVP studies can most likely be attributed to a specific difference in the type of persistence underlying task performance. Temporal integration in the MET might rely mostly on visible persistence, while for the RSVP task integration relies more on informational persistence. The present findings suggest that, contrary to informational persistence, visible persistence may not be susceptible to adaptive control.

Neuroscience studies have demonstrated the phase-locked characteristics of some early event-related potential (ERP) components evoked by stimuli. In this study, we propose a phase preservation neural network (PPNN) to learn phase information to improve the Electroencephalography (EEG) classification in a rapid serial visual presentation (RSVP) task. The PPNN consists of three major modules that can produce spatial and temporal representations with the high discriminative ability of the EEG features for classification. We first adopt a stack of dilated temporal convolution layers to extract temporal dynamics while avoiding the loss of phase information. Considering the intrinsic channel dependence of the EEG data, a spatial convolution layer is then applied to obtain the spatial-temporal representation of the input EEG signal. Finally, a fully connected layer is adopted to extract higher-level features for the final classification. The experiments are conducted on two public and one collected EEG datasets from the RSVP task, in which we evaluated the performance and explored the capability of phase preservation of our PPNN model and visualized the extracted features. The experimental results indicate the superiority of the proposed PPNN when compared with previous methods, suggesting the PPNN is a robust model for EEG classification in RSVP task.

In non-invasive brain-computer interface (BCI), the analysis of event-related potentials (ERP) has typically focused on averaged trials, a current trend is to analyze single-trial evoked response individually with new approaches in pattern recognition and signal processing. Such single trial detection requires a robust response that can be detected in a variety task conditions. Here, we investigated the influence of target probability, a key factor known to influence the amplitude of the evoked response, on single trial target classification in a difficult rapid serial visual presentation (RSVP) task. Our classification approach for detecting target vs. non target responses, considers spatial filters obtained through the maximization of the signal to signal-plus-noise ratio, and then uses the resulting information as inputs to a Bayesian discriminant analysis. The method is evaluated across eight healthy subjects, on four probability conditions (P=0.05, 0.10, 0.25, 0.50). We show that the target probability has a statistically significant effect on both the behavioral performance and the target detection. The best mean area under the ROC curve is achieved with P=0.10, AUC=0.82. These results suggest that optimal performance of ERP detection in RSVP tasks is critically dependent on target probability.

Objective. Single-trial electroencephalography (EEG) classification is of great importance in the rapid serial visual presentation (RSVP) task. Convolutional neural networks (CNNs), as one of the mainstream deep learning methods, have been proven to be effective in extracting RSVP EEG features. However, most existing CNN models for EEG classification do not consider the phase-locked characteristic of event-related potential (ERP) components very well in the architecture design. Here, we propose a novel CNN model to make better use of the phase-locked characteristic to extract spatiotemporal features for single-trial RSVP EEG classification. Based on the phase-locked characteristic, the spatial distributions of the main ERP component in different periods can be learned separately. Approach. In this work, we propose a novel CNN model to achieve superior performance on single-trial RSVP EEG classification. We introduce the combination of the standard convolutional layer, the permute layer and the depthwise convolutional layer to separately operate the spatial convolution in different periods, which more fully utilizes the phase-locked characteristic of ERPs for classification. We compare our model with several traditional and deep-learning methods in the classification performance. Moreover, we use spatial topography and saliency map to visually analyze the ERP features extracted by our model. Main results. The results show that our model obtains better classification performance than those of reference methods. The spatial topographies of each subject exhibit the typical ERP spatial distribution in different time periods. And the saliency map of each subject illustrates the discriminant electrodes and the meaningful temporal features. Significance. Our model is designed with better consideration of the phase-locked ERP characteristic and reaches excellent performance on single-trial RSVP EEG classification.

Objective: The aim of this study was to investigate changes in P3 amplitude and latency to emotional stimuli with different arousal and valence levels after a mindfulness-based intervention in oncological patients. Methods: P3 amplitude and latency at Fz, Cz, and Pz to emotional salient stimuli differing in valence (positive vs. negative) and arousal (low vs. high) levels were measured in 17 oncological patients (Mage = 51 years, SD = 9.17, females = 11) before and after a mindfulness-based intervention in a waitlist-controlled clinical trial. Group sessions were held 2x/week over a period of 5 weeks. Results: Arousal and valence levels of emotional salient stimuli were significant modulators of P3 amplitudes (P = .050; P = .006, respectively). P3 amplitudes at Fz decreased significantly from pre-waitlist to post-intervention (P = .002). P3 latency for negatively valenced stimuli decreased from pre- to post-intervention with trending significance (P = .080). P3 latency at Pz showed a trend of being shorter than P3 latency at Cz (P = .066). Depressive symptoms showed a decreasing trend from pre- to post-treatment (P = .075). Conclusions: We could show that arousal and valence levels of emotional salient stimuli are important modulators of P3 amplitudes and latencies in oncological patients. P3 amplitudes at Fz decreased significantly from pre-waitlist until after mindfulness-based intervention, decreases from pre-to post-intervention were linear, but not significant. In oncological patients the mindfulness-based intervention seems to have its strongest effect on the frontal electrode Fz, not the electrode Pz where its amplitude was largest. Depressive symptoms could be reduced.

Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is an efficient information detection technology by detecting event related brain response evoked by target stimuli. In the protocol design of the RSVP-BCI a range of parameters could influence the task difficulty, which may result in the changes of mental workload for subjects. This paper focused on the presentation rate in the RSVP paradigm aiming to investigate its influence on mental workload, and the separability of brain states during RSVP tasks with different setup of presentation rate. 64-channel Electroencephalographic (EEG) data were recorded during RSVP tasks with three levels of presentation rate in ten healthy subjects. The results show that different presentation rates indeed contribute to significant differences on mental workload revealed by one-way repeated measures analysis of variance (ANOVA) on z-scored RSME. Higher presentation rate results in the significant decrease on both behavioral and single-trial recognition performance of target images. Classification results on three levels of mental workload show that the mean accuracy reaches 65.5% and the highest accuracy reaches 88.3%. This work implies that mental workload induced by different presentation rates during RSVP tasks could be accurately recognized, and provides a possible method to monitor the mental workload in the application areas of RSVP-BCI.

We describe a method, using linear discrimination, for detecting single-trial EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (87 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Subjects were instructed to release a button when they recognized a target image (an image with a person/people). Trials consisted of 100 images each, with a 50% chance of a single target being in a trial. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Linear discrimination enables the estimation of a forward model and thus allows for an approximate localization of the discriminating activity. Results show multiple loci for discriminating activity (e.g. motor and visual). Using these detected EEG signatures, we show that in many cases we can detect targets more accurately than the overt response (button release) and that such signatures can be used to prioritize images for high-throughput search.

The extent to which letter-by-letter reading results from a specific orthographic deficit, as compared with a nonspecific disturbance in basic visuoperceptual mechanisms, is unclear. The current study directly compared processing of letters and digits in a letter-by-letter reader, G.M., using a rapid serial visual presentation (RSVP) task and a speeded matching task. Comparisons were made to a group of six brain-damaged individuals without reading deficits. In the RSVP task, G.M. had increased difficulty reporting the target identities when they were letters, as compared with digits. Although this general pattern was also evident in the control group, the magnitude of the letter-digit accuracy difference was greater in G.M. Similarly, in the matching task, G.M. was slower to match letters than digits, relative to the control group, although his response times to both item types were increased. These data suggest that letter-by-letter reading, at least in this case, results from a visuoperceptual encoding deficit that particularly affects letters, but also extends to processing of digits to a lesser extent. Results are consistent with the notion that a left occipitotemporal area is specialized for letter processing with greater bilaterality in the visual processing of digits.

Collaborative brain-computer interfaces (cBCIs) have demonstrated significant improvements in single-trial electroencephalogram (EEG) classification performance in rapid serial visual presentation (RSVP) tasks. However, it remains unclear how to effectively organize multiple collaborators into sub-groups to optimize system performance. This study introduces a novel sub-group partition strategy for RSVP-based cBCI systems. We first developed intra-individual and inter-individual neural response reproducibility (IINRR) as a metric to estimate subgroup capability in RSVP tasks. Based on this metric, we propose an IINRR-based partition strategy to optimize sub-group composition. Additionally, we introduce a metric called collaborative information processing rate (CIPR) to evaluate overall system performance. Our experiments verified the effectiveness of the proposed strategy on a public RSVP-based cBCI dataset. The results showed that our strategy consistently outperformed random partitioning in both within-session and cross-session scenarios, achieving higher classification performance and system efficiency. These findings suggest the strategy's potential for optimizing group mode in practical RSVP-based cBCI applications.

To examine the differences in auditory evoked cortical responses that may underlie the tendency of some people to perceive tinnitus. The study hypothesis is that the mean ALR and P300 amplitudes in normal hearing adults who perceive temporary tinnitus after exposure to sustained silence will be larger than the mean ALR and P300 amplitudes in normal hearing adults who do not perceive temporary tinnitus after exposure to sustained silence. This was a prospective cross-sectional study. The approval for the study was obtained from the IRB and COVID ramp up committee of University of North Carolina Greensboro (UNCG). Participants completed comprehensive hearing screening and preand postsilence ALR and P300 recordings were obtained. After the first ALR/P300 recording participants were exposed to ten minutes of silence. Participants completed a Qualtrics questionnaire to report any tinnitus perception that emerged during silence exposure. Absolute N1, P2 and P300 waveform amplitudes and latencies were identified and were entered into an SPSS spreadsheet for data analysis. Thirty adult females with normal pure tone hearing thresholds and no history of persistent tinnitus were included in the study. The mean age of the participants was 22.5 ± 3.9 years. When exposed to silence, eight (26.7%) participants perceived temporary tinnitus. N1 and P300 waveforms were smaller in amplitude and faster in latency in the tinnitus perception group; however, the ALR and P300 waveform latencies and amplitudes did not statistically differ significantly between the participants who perceived temporary tinnitus in silence and those who did not (p>0.05). The difference in waveform morphology between the tinnitus perception group and the non-tinnitus perception group revealed a greater difference in P300 amplitude after exposure to silence. Differences in ALR and P300 latencies and amplitudes were observed between the tinnitus perception group and non-tinnitus perception group, with smaller P300 amplitudes appearing in the group perceiving tinnitus. While the results did not statistically significant, this pattern may reflect a mismatch between the neuronal response in the auditory cortex (N1 and P2 amplitudes and latencies) and the neuronal activity in the modulatory network regions (P300).

Introduction In order to provide the theoretical basis and practical guidance of eye protection during the prone spinal surgery under the general anesthesia, visual evoked potential (VEP) was monitored before and after surgery. Patients and Methods A total of 82 patients were involved in our study and grouped into three groups, S, M, and L groups, respectively, according to the operation time. The P100's latency and amplitude changes and the visual function of preoperative, postanesthetic, and postoperative spots were analyzed to understand their relationship. Results (1) The waveforms of VEP in 78 cases were monitored stably and repeatedly and the other four cases failed. (2) Difference of P100's latency and amplitude at preoperative, postanesthetic time was not statistically significant in all groups. (3) Postoperatively, difference of P100's latency was statistically significant in M and L groups, and no statistically significant difference was observed in S and M groups. Difference in P100's amplitude was statistically significant in all groups. (4) Recovery time of P100's latency has statistically significant differences in all three groups. (5) Two patients in the L group had conjunctival edema, pupillary light reflex obstacles, and visual acuity descending. After receiving positive symptomatic treatment, symptoms disappeared and vision recovered. Conclusion VEP can provide the stable and on-time electrophysiological conductive evidence needed to judge the optical pathway function. The operator should pay close attention to the change in VEP and visual performance if prone spinal surgery continues for more than 4 hours.

Auditory P300 event related potentials and serotonin reuptake inhibitor treatment in patients with fibromyalgia