Silicone oil insulation effects on flash electroretinogram and visual evoked potential in patients with retinal detachment / Efectos del aislamiento del aceite de silicona sobre el electrorretinograma flash y el potencial evocado visual en pacientes con desprendimiento de retina

Background: Silicone oil is used as endotamponade following vitreoretinal surgery to maintain the retina reattached when indicated. This study investigates the hypothesis that silicone oil causes insulation effects on the retina by affecting its response to light. Methods: Electrophysiological responses to a flash stimulus were recorded using full-field electroretinography (ERG) and visual evoked potentials (VEP). Recordings were performed in 9 patients who underwent surgery for retinal detachment, before (1–2 days) and after (2–3 weeks) silicone oil removal (SOR) in both the study and the control eye. Flash ERG and VEP recordings were performed according to the ISCEV standard protocol. Results: Statistically significant differences were found in the study eye in the amplitudes of the ERG responses and their corresponding ratios, i.e. the amplitude after SOR over the amplitude before SOR, in all conditions tested. No differences were observed in the control eye. The mean ratio of photopic ERG response was 3.4 ± 2.4 for the study and 1.0 ± 0.3 for the control eye (p<0.001). The mean ratio of ERG flicker response was 3.1 ± 2.4 and 1.0 ± 0.3, respectively (p = 0.003). Scotopic flash ERG ratio was 5.0 ± 4.4 for the study and 1.3 ± 0.6 for the control eye (p = 0.012). No differences were observed for the amplitude and latency of flash VEP response after SOR. Conclusions: Silicone oil causes a reduction in flash ERG responses; no effect was found on flash VEP responses. ERGs in eyes filled with silicone oil should not be considered representative of retinal functionality, in contrast to VEPs, which are not affected by silicone oil presence.(AU)

Effect of desflurane anesthesia on flash visual evoked potential monitoring in patients undergoing spine surgery: study protocol for a randomized controlled trial.

BACKGROUND: Flash visual evoked potentials (FVEPs) are a reliable method for protecting visual function during spine surgery in prone position. However, the popularization and application of FVEPs remain limited due to the unclear influence of various anesthetics on FVEPs. Exploring the effects of anesthetic drugs on FVEP and establishing appropriate anesthesia maintenance methods are particularly important for promoting and applying FVEP. According to the conventional concept, inhaled narcotic drugs significantly affect the success of FVEP monitoring, FVEP extraction, and interpretation. Nonetheless, our previous study demonstrated that sevoflurane-propofol balanced anesthesia was a practicable regimen for FVEPs. Desflurane is widely used in general anesthesia for its rapid recovery properties. As the effect of desflurane on FVEP remains unclear, this trial will investigate the effect of different inhaled concentrations of desflurane anesthesia on amplitude of FVEPs during spine surgery, aiming to identify more feasible anesthesia schemes for the clinical application of FVEP. METHODS/ DESIGN: A total of 70 patients undergoing elective spinal surgery will be enrolled in this prospective, randomized controlled, open-label, patient-assessor-blinded, superiority trial and randomly assigned to the low inhaled concentration of desflurane group (LD group) maintained with desflurane-propofolremifentanil-balanced anesthesia or high inhaled concentration of desflurane group (HD group) maintained with desflurane-remifentanil anesthesia maintenance group at a ratio of 1:1. All patients will be monitored for intraoperative FVEPs, and the baseline will be measured half an hour after induction under total intravenous anesthesia (TIVA). After that, patients will receive 0.5 minimum alveolar concentration (MAC) of desflurane combined with propofol and remifentanil for anesthesia maintenance in the LD group, while 0.7-1.0 MAC of desflurane and remifentanil will be maintained in the HD group. The primary outcome is the N75-P100 amplitude 1 h after the induction of anesthesia. We intend to use the dual measure evaluation, dual data entry, and statistical analysis by double trained assessors to ensure the reliability and accuracy of the results. DISCUSSION: This randomized controlled trial aims to explore the superiority effect of low inhaled concentration of desflurane combined with propofolremifentanil-balanced anesthesia versus high inhaled concentration of desflurane combined with remifentanil anesthesia on amplitude of FVEPs. The study is meant to be published in a peer-reviewed journal and might guide the anesthetic regimen for FVEPs. The conclusion is expected to provide high-quality evidence for the effect of desflurane on FVEPs and aim to explore more feasible anesthesia schemes for the clinical application of FVEPs and visual function protection. TRIAL REGISTRATION: This study was registered on clinicaltrials.gov on July 15, 2022. CLINICALTRIALS: gov Identifier: NCT05465330.

A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.

Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.

Attention deployment in natural scenes: Higher-order scene statistics rather than semantics modulate the N2pc component.

Which properties of a natural scene affect visual search? We consider the alternative hypotheses that low-level statistics, higher-level statistics, semantics, or layout affect search difficulty in natural scenes. Across three experiments (n = 20 each), we used four different backgrounds that preserve distinct scene properties: (a) natural scenes (all experiments); (b) 1/f noise (pink noise, which preserves only low-level statistics and was used in Experiments 1 and 2); (c) textures that preserve low-level and higher-level statistics but not semantics or layout (Experiments 2 and 3); and (d) inverted (upside-down) scenes that preserve statistics and semantics but not layout (Experiment 2). We included "split scenes" that contained different backgrounds left and right of the midline (Experiment 1, natural/noise; Experiment 3, natural/texture). Participants searched for a Gabor patch that occurred at one of six locations (all experiments). Reaction times were faster for targets on noise and slower on inverted images, compared to natural scenes and textures. The N2pc component of the event-related potential, a marker of attentional selection, had a shorter latency and a higher amplitude for targets in noise than for all other backgrounds. The background contralateral to the target had an effect similar to that on the target side: noise led to faster reactions and shorter N2pc latencies than natural scenes, although we observed no difference in N2pc amplitude. There were no interactions between the target side and the non-target side. Together, this shows that-at least when searching simple targets without own semantic content-natural scenes are more effective distractors than noise and that this results from higher-order statistics rather than from semantics or layout.

Electrophysiological correlates of sustained conscious perception.

Previous research on the neural correlates of consciousness (NCC) in visual perception revealed an early event-related potential (ERP), the visual awareness negativity (VAN), to be associated with stimulus awareness. However, due to the use of brief stimulus presentations in previous studies, it remains unclear whether awareness-related negativities represent a transient onset-related response or correspond to the duration of a conscious percept. Studies are required that allow prolonged stimulus presentation under aware and unaware conditions. The present ERP study aimed to tackle this challenge by using a novel stimulation design. Male and female human participants (n = 62) performed a visual task while task-irrelevant line stimuli were presented in the background for either 500 or 1000 ms. The line stimuli sometimes contained a face, which needed so-called visual one-shot learning to be seen. Half of the participants were informed about the presence of the face, resulting in faces being perceived by the informed but not by the uninformed participants. Comparing ERPs between the informed and uninformed group revealed an enhanced negativity over occipitotemporal electrodes that persisted for the entire duration of stimulus presentation. Our results suggest that sustained visual awareness negativities (SVAN) are associated with the duration of stimulus presentation.

Melanopsin-mediated amplification of cone signals in the human visual cortex.

The ambient daylight variation is coded by melanopsin photoreceptors and their luxotonic activity increases towards midday when colour temperatures are cooler, and irradiances are higher. Although melanopsin and cone photoresponses can be mediated via separate pathways, the connectivity of melanopsin cells across all levels of the retina enables them to modify cone signals. The downstream effects of melanopsin-cone interactions on human vision are however, incompletely understood. Here, we determined how the change in daytime melanopsin activation affects the human cone pathway signals in the visual cortex. A 5-primary silent-substitution method was developed to evaluate the dependence of cone-mediated signals on melanopsin activation by spectrally tuning the lights and stabilizing the rhodopsin activation under a constant cone photometric luminance. The retinal (white noise electroretinogram) and cortical responses (visual evoked potential) were simultaneously recorded with the photoreceptor-directed lights in 10 observers. By increasing the melanopsin activation, a reverse response pattern was observed with cone signals being supressed in the retina by 27% (p = 0.03) and subsequently amplified by 16% (p = 0.01) as they reach the cortex. We infer that melanopsin activity can amplify cone signals at sites distal to retinal bipolar cells to cause a decrease in the psychophysical Weber fraction for cone vision.

A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.

Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min-1with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min-1.Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model.

BACKGROUND AND OBJECTIVE: Brain-Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity. METHODS: In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features. RESULTS: We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods. CONCLUSIONS: The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.

SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.

The execution of saccadic eye movements suppresses visual processing of both color and luminance in the early visual cortex of humans.

Our eyes execute rapid, directional movements known as saccades, occurring several times per second, to focus on objects of interest in our environment. During these movements, visual sensitivity is temporarily reduced. Despite numerous studies on this topic, the underlying mechanism remains elusive, including a lingering debate on whether saccadic suppression affects the parvocellular visual pathway. To address this issue, we conducted a study employing steady-state visual evoked potentials (SSVEPs) elicited by chromatic and luminance stimuli while observers performed saccadic eye movements. We also employed an innovative analysis pipeline to enhance the signal-to-noise ratio, yielding superior results compared to the previous method. Our findings revealed a clear suppression effect on SSVEP signals during saccades compared to fixation periods. Notably, this suppression effect was comparable for both chromatic and luminance stimuli. We went further to measure the suppression effect across various contrast levels, which enabled us to model SSVEP responses with contrast response functions. The results suggest that saccades primarily reduce response gain without significantly affecting contrast gain and that this reduction applies uniformly to both chromatic and luminance pathways. In summary, our study provides robust evidence that saccades similarly suppress visual processing in both the parvocellular and magnocellular pathways within the human early visual cortex, as indicated by SSVEP responses. The observation that saccadic eye movements impact response gain rather than contrast gain implies that they influence visual processing through a multiplicative mechanism.NEW & NOTEWORTHY The present study demonstrates that saccadic eye movements reduce the processing of both luminance and chromatic stimuli in the early visual cortex of humans. By modeling the contrast response function, the study further shows that saccades affect visual processing by reducing the response gain rather than altering the contrast gain, suggesting that a multiplicative mechanism of visual attenuation affects both parvocellular and magnocellular pathways.

Systematic review and meta-analysis of the visual mismatch negativity in schizophrenia.

Mismatch negativity (MMN) is an event-related potential component automatically elicited by events that violate predictions based on prior events. To elicit this component, researchers use stimulus repetition to induce predictions, and the MMN is obtained by subtracting the brain response to rare or unpredicted stimuli from that of frequent stimuli. Under the Predictive Processing framework, one increasingly popular interpretation of the mismatch response postulates that MMN represents a prediction error. In this context, the reduced MMN amplitude to auditory stimuli has been considered a potential biomarker of Schizophrenia, representing a reduced prediction error and the inability to update the mental model of the world based on the sensory signals. It is unclear, however, whether this amplitude reduction is specific for auditory events or if the visual MMN reveals a similar pattern in schizophrenia spectrum disorder. This review and meta-analysis aimed to summarise the available literature on the vMMN in schizophrenia. A systematic literature search resulted in 10 eligible studies that resulted in a combined effect size of g = -.63, CI [-.86, -.41], reflecting lower vMMN amplitudes in patients. These results are in line with the findings in the auditory domain. This component offers certain advantages, such as less susceptibility to overlap with components generated by attentional demands. Future studies should use vMMN to explore abnormalities in the Predictive Processing framework in different stages and groups of the SSD and increase the knowledge in the search for biomarkers in schizophrenia.

Visual evoked potential in generalized joint hypermobility: A case-control study.

INTRODUCTION: Generalized joint hypermobility (GJH) can be the result of several hereditary connective tissue disorders, especially Ehlers-Danlos syndrome. Cerebrovascular manifestations are among the most common complications in this disorder, and understanding their extent can help better diagnosis and prevention of hazardous events. We investigated visual evoked potential (VEP) changes in patients with GJH and compared them with healthy individuals. METHODS: Our case-control study included 90 patients who fulfilled the Beighton score (B score) for joint hypermobility and other 90 healthy participants. All of them went under VEP study, and the amplitude and latency of the evoked potential (P100) were compared to each other. RESULTS: The Case group had significantly higher B score (7.18 ± 0.967 vs. 1.18 ± 0.712), P100 latency (110.23 ± 6.64 ms vs. 100.18 ± 4.273 ms), and amplitude (6.54 ± 1.26 mv vs. 6.50 ± 1.29 mv) compared with the Control group, but the difference was only significant regarding B score, and P100 latency (p-value <.0001). Moreover, both latency and amplitude of P100 had significantly positive correlations with the B score in the Case group (p-value <.0001), but such correlations were not found in the Control group (p-value = .059). CONCLUSION: Our study could reveal VEP changes, especially significant P100 latency in GJH patients without previous neurologic or musculoskeletal disorders. Whether these changes are due to GJH itself or are predictive of inevitable neurologic disease or visual pathway involvement, particularly Multiple Sclerosis needs further investigation with longer follow-up periods.

Frequency-dependent dynamics of steady-state visual evoked potentials under sustained flicker stimulation.

Steady-state visual evoked potentials (SSVEP) are electroencephalographic signals elicited when the brain is exposed to a visual stimulus with a steady frequency. We analyzed the temporal dynamics of SSVEP during sustained flicker stimulation at 5, 10, 15, 20 and 40 Hz. We found that the amplitudes of the responses were not stable over time. For a 5 Hz stimulus, the responses progressively increased, while, for higher flicker frequencies, the amplitude increased during the first few seconds and often showed a continuous decline afterward. We hypothesize that these two distinct sets of frequency-dependent SSVEP signal properties reflect the contribution of parvocellular and magnocellular visual pathways generating sustained and transient responses, respectively. These results may have important applications for SSVEP signals used in research and brain-computer interface technology and may contribute to a better understanding of the frequency-dependent temporal mechanisms involved in the processing of prolonged periodic visual stimuli.

The time course of feature-selective attention inside and outside the focus of spatial attention.

Research on attentional selection of stimulus features has yielded seemingly contradictory results. On the one hand, many experiments in humans and animals have observed a "global" facilitation of attended features across the entire visual field, even when spatial attention is focused on a single location. On the other hand, several event-related potential studies in humans reported that attended features are enhanced at the attended location only. The present experiment demonstrates that these conflicting results can be explained by differences in the timing of attentional allocation inside and outside the spatial focus of attention. Participants attended to fields of either red or blue randomly moving dots on either the left or right side of fixation with the task of detecting brief coherent motion targets. Recordings of steady-state visual evoked potentials elicited by the flickering stimuli allowed concurrent measurement of the time course of feature-selective attention in visual cortex on both the attended and the unattended sides. The onset of feature-selective attentional modulation on the attended side occurred around 150 ms earlier than on the unattended side. This finding that feature-selective attention is not spatially global from the outset but extends to unattended locations after a temporal delay resolves previous contradictions between studies finding global versus hierarchical selection of features and provides insight into the fundamental relationship between feature-based and location-based (spatial) attention mechanisms.

A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.

Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.

Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA). METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial. RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively. CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.

Prior probability cues bias sensory encoding with increasing task exposure.

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and Electromyogram.

BACKGROUND: To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR. METHODS: In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects. RESULTS: The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects. CONCLUSIONS: Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.

Visual omitted stimulus potentials are not retinotopic.

Omitted stimulus potentials (OSPs) are elicited in response to the omission of expected stimuli and are thought to reflect prediction errors. If prediction errors are signaled in the sensory cortex, OSPs are expected to be generated in the sensory cortex. The present study investigated the involvement of the early visual cortex in the generation of OSPs by testing whether omitted visual stimuli elicit brain responses in a spatially specific manner. Checkerboard pattern stimuli were presented alternately in the upper and lower visual fields, and the stimuli were omitted in 10 % of the trials. Event-related potentials were recorded from 33 participants. While a retinotopic C1 component was evoked by real visual stimuli, omitted stimuli did not produce any response reflecting retinotopy but did elicit a visual mismatch negativity, which was larger for omitted stimuli expected in the lower visual field than for those in the upper visual field. These results suggest that omitted visual stimuli are processed in a different pathway than actual stimuli.