[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

[Visual object detection system based on augmented reality and steady-state visual evoked potential].

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

Retinal Damage and Visual Network Reconfiguration Defines Visual Function Recovery in Optic Neuritis.

BACKGROUND AND OBJECTIVES: Recovery of vision after acute optic neuritis (AON) is critical to improving the quality of life of people with demyelinating diseases. The objective of the study was to prospectively assess the changes in visual acuity, retinal layer thickness, and cortical visual network in patients with AON to identify the predictors of permanent visual disability. METHODS: We studied a prospective cohort of 88 consecutive patients with AON with 6-month follow-up using high and low-contrast (2.5%) visual acuity, color vision, retinal thickness from optical coherence tomography, latencies and amplitudes of multifocal visual evoked potentials, mean deviation of visual fields, and diffusion-based structural (n = 53) and functional (n = 19) brain MRI to analyze the cortical visual network. The primary outcome was 2.5% low-contrast vision, and data were analyzed with mixed-effects and multivariate regression models. RESULTS: We found that after 6 months, low-contrast vision and quality of vision remained moderately impaired. The thickness of the ganglion cell layer at baseline was a predictor of low-contrast vision 6 months later (ß = 0.49 [CI 0.11-0.88], p = 0.012). The structural cortical visual network at baseline predicted low-contrast vision, the best predictors being the betweenness of the right parahippocampal cortex (ß = -036 [CI -0.66 to 0.06], p = 0.021), the node strength of the right V3 (ß = 1.72 [CI 0.29-3.15], p = 0.02), and the clustering coefficient of the left intraparietal sulcus (ß = 57.8 [CI 12.3-103.4], p = 0.015). The functional cortical visual network at baseline also predicted low-contrast vision, the best predictors being the betweenness of the left ventral occipital cortex (ß = 8.6 [CI: 4.03-13.3], p = 0.009), the node strength of the right intraparietal sulcus (ß = -2.79 [CI: -5.1-0.4], p = 0.03), and the clustering coefficient of the left superior parietal lobule (ß = 501.5 [CI 50.8-952.2], p = 0.03). DISCUSSION: The assessment of the visual pathway at baseline predicts permanent vision disability after AON, indicating that damage is produced early after disease onset and that it can be used for defining vision impairment and guiding therapy.

Neuro-ophthalmic and Neuro-otologic Evaluation in Individuals with Motion Sickness Susceptibility.

Since the physiological background of motion sickness is not entirely clear, it was aimed to examine the physiological differences in groups consisting of individuals susceptible and non-susceptible to motion sickness. Sixty subjects [motion sickness (MS) group: 33 female, 3 male; 28.8 ± 8.1 years; control group: 19 female, 5 male; 24.5 ± 4.3 years] were included in the study. Near visual acuity test on the treadmill in the presence of visual stimulation, pattern visual-evoked potentials, oculomotor tests, and computerized dynamic posturography were applied. Receiver operating characteristic analysis was performed to determine the parameter that provides the excellent discrimination between the groups. The most effective parameter in differentiating the study groups was determined as dynamic visual acuity with 77.8% sensitivity and 95.8% specificity. Significant differences were found in the vestibular (mean ± standard deviation: 0.63 ± 0.17), visual (0.77 ± 0.18), and composite scores (73.11 ± 11.89) of the patients (P=.000) in posturographic evaluation. In the visual-evoked potential examination, a significant decrease was found in the amplitude values between the P100-N145 waves in the binocular (5.0 ± 2.8, P=.002), right eye (7.6 ± 3.2, P=.009) and left eye (7.9 ± 2.9, P=.016) in the symptomatic patients. In binocular oculomotor evaluation, directional asymmetric findings were obtained. It has been shown that the most effective test parameter that distinguishes the MS susceptible and non-susceptible individuals is the dynamic visual acuity value. Based on the results of neuro-physiological tests, it was suggested that a possible visual-vestibular integration disorder in individuals susceptible to motion sickness may affect visual and vestibular performance.

Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling.

Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.

Pattern-reversal visual evoked potentials in prosthetic vision and simulated visual reduction.

OBJECTIVE: To quantitatively evaluate visual evoked potentials (VEPs) in prosthetic vision and simulated visual reduction. METHODS AND ANALYSIS: Four blind patients implanted with the Argus II retinal prosthesis and seven sighted controls participated. VEPs were recorded with pattern-reversal stimuli (2 cycles of a horizontal square wave grating, 0.1 cycle/degree) at 1.07 reversals per second (rps) for Argus II subjects and 3.37 rps for controls. Argus II patients had both eyes patched, viewing the pattern solely through their implant. Controls viewed the pattern monocularly, either with their best-corrected vision or with simulated visual reduction (field restriction, added blur or reduced display contrast). RESULTS: VEPs recorded in Argus II patients displayed a similar shape to normal VEPs when controls viewed the pattern without simulated visual reduction. In sighted controls, adding blur significantly delayed the P100 peak time by 8.7 ms, 95% CI (0.9, 16.6). Reducing stimulus contrast to 32% and 6% of full display contrast significantly decreased P100 amplitude to 55% (37%, 82%) and 20% (13%, 31%), respectively. Restriction on the field of view had no impact on either the amplitude or the peak latency of P100. CONCLUSION: The early visual cortex in retinal prosthesis users remains responsive to retinal input, showing a similar response profile to that of sighted controls. Pattern-reversal VEP offers valuable insights for objectively evaluating artificial vision therapy systems (AVTSs) when selecting, fitting and training implant users, but the uncertainties in the exact timing and location of electrode stimulation must be considered when interpreting the results.

Processing of visual hapaxes in picture naming task: An event-related potential study.

Object recognition and visual categorization are typically swift and seemingly effortless tasks that involve numerous underlying processes. In our investigation, we utilized a picture naming task to explore the processing of rarely encountered objects (visual hapaxes) in comparison to common objects. Our aim was to determine the stage at which these rare objects are classified as unnamable. Contrary to our expectations and in contrast to some prior research on event-related potentials (ERPs) with novel and atypical objects, no differences between conditions were observed in the late time windows corresponding to the P300 or N400 components. However, distinctive patterns between hapaxes and common objects surfaced in three early time windows, corresponding to the posterior N1 and P2 waves, as well as a widespread N2 wave. According to the ERP data, the differentiation between hapaxes and common objects occurs within the first 380 ms of the processing line, involving only limited and indirect top-down influence.

N1 and LNC reveal the differential electrophysiological signatures of functional and hedonic images processing.

In the process of creative sentence or phrase utilization, novel and appropriate evaluations cause the different brain responses observed in event-related potentials: the N400 reflects the novelty evaluation, whereas a late negative component marks appropriate processing. Do we have similar brain reactions in image perception when we rapidly browse pictures of objects with different novelty, functional/appropriate, and hedonic value? To explore this question, participants were presented with four novel object images with high or low functional and hedonic properties, as well as the ordinary product images, with the instruction to attentively observe and understand each image. We found a clear dissociation between processing of novelty and functional value: novelty objects produced negative deflections in the N2-N400 time window relative to the ordinary object images, whereas images with high functional value elicited a larger N2 and late negative waves (LNC) resembling the late component found for the appropriate phrases. Object images with high hedonic value, however, were associated with earlier aesthetic preference reflected in smaller N1 amplitudes, but failed to elicit a LNC effect. We therefore conclude that the processing of novelty, functional, and hedonic value are dissociation, and the perception of hedonic value is earlier (N1) than the novelty processing (N400) and the verification of functional value (LNC).

An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification.

Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

Stabilizing axin leads to optic nerve hypoplasia in a mouse model of autism.

Autism spectrum disorder (ASD) is a group of neurodevelopment disorders characterized by deficits in social interaction and communication, and repetitive or stereotyped behavior. Autistic children are more likely to have vision problems, and ASD is unusually common among blind people. However, the mechanisms behind the vision disorders in autism are unclear. Stabilizing WNT-targeted scaffold protein Axin2 by XAV939 during embryonic development causes overproduction of cortical neurons and leads to autistic-like behaviors in mice. In this study, we investigated the relationship between vision abnormality and autism using an XAV939-induced mouse model of autism. We found that the mice receiving XAV939 had decreased amplitude of bright light-adaptive ERG. The amplitudes and latency of flash visual evoked potential recorded from XAV939-treated mice were lower and longer, respectively than in the control mice, suggesting that XAV939 inhibits visual signal processing and conductance. Anatomically, the diameters of RGC axons were reduced when Axin2 was stabilized during the development, and the optic fibers had defective myelin sheaths and reduced oligodendrocytes. The results suggest that the WNT signaling pathway is crucial for optic nerve development. This study provides experimental evidence that conditions interfering with brain development may also lead to visual problems, which in turn might exaggerate the autistic features in humans.

An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients.

Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77 % and 79.38 ± 6.26 %, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.

Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.

Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.

Human-centred physical neuromorphics with visual brain-computer interfaces.

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.

Investigating the impact of early deafness on learned action-effect contingency for action linked to peripheral sensory effects.

Investigating peripheral visual processing in individuals with early auditory deprivation is a critical research area in the field of neuroscience, since it helps understanding the phenomenon of sensory adaptation and brain plasticity after sensory loss. Prior research has already demonstrated that the absence of auditory input, which is crucial to detect events occurring out of the central egocentric visual space, leads to an improved processing of visual and tactile stimuli occurring in peripheral regions of the sensory space. Nevertheless, no prior studies have explored whether such enhanced processing also takes place within the domain of action, particularly when individuals are required to perform actions that produce peripheral sensory outcomes. To test this hypothesis, we recruited 15 hearing (31 ± 3.3 years) and 15 early deaf adults (42 ± 2.6 years) for a neuro-behavioral experiment involving: 1) a behavioral task where participants executed a simple motor action (i.e., a button press) and received a visual feedback either in the center or in a peripheral region of the visual field, and 2) the electrophysiological recording of brain electrical potentials (EEG). We measured and compared neural activity preceding the motor action (the readiness potentials) and visual evoked responses (the N1 and P2 ERP components) and found that deaf individuals did not exhibit more pronounced modulation of neural responses when their motor actions resulted in peripheral visual stimuli compared to their hearing counterparts. Instead they showed a reduced modulation when visual stimuli were presented in the center. Our results suggest a redistribution of attentional resources from center to periphery in deaf individuals during sensorimotor coupling.

Utility of Visual Evoked Potentials (VEPs) study in the evaluation of visual pathway dysfunction in diabetics without retinopathy: correlations with diabetic peripheral neuropathy and other clinical findings.

Aim and objectives: Visual dysfunction in diabetes mellitus (DM) is multifactorial and can be due to vascular disease, and metabolic abnormalities that can affect the retina, optic nerve, and visual pathways. Visual evoked potential (VEP) is an electrophysiological test that can quantify the functional integrity of the visual pathways from the retina via the optic nerves, and optic tracts to the visual cortices. In this study, we aimed to investigate the visual pathway dysfunction among diabetics without retinopathy compared with healthy controls and to look for any correlation with diabetic neuropathy, duration of diabetes, or HbA1c level. Methods: The study included 75 diabetic patients and 75 age and sex-matched controls. VEPs were recorded using the pattern reversal stimulation method on the Medtronic EMG EP machine, and P100 latency and N75-P100 amplitude were recorded in both diabetic patients and healthy controls. Results: Mean P100 latency was significantly prolonged and N75-P100 amplitude significantly reduced among diabetic cases compared to healthy controls (p < 0.001). Among diabetics with peripheral neuropathy, P100 latency was significantly prolonged and N75-P100 amplitude was significantly reduced compared to diabetics without peripheral neuropathy. A significant positive correlation of VEP P100 latency (p < 0.001) and a negative correlation with N75-P100 amplitude (p < 0.001) with duration of disease were also found. Conclusion: VEP changes are observed in diabetics before the development of retinopathy or peripheral neuropathy indicating optic pathway dysfunction, which precedes the development of these complications. Early preclinical visual pathway dysfunction can warrant taking the necessary measures to reduce diabetic complications. Abbreviations: DM = Diabetes Mellitus, VEP = Visual Evoked Potential, HbA1c = Hemoglobin A1 c, MRI = Magnetic Resonance Imaging, EEG = Electroencephalography, P100 = Positive wave peak at latency 100 ms (millisecond), N75 = Negative wave peak at latency 75 ms (millisecond), N145 = Negative wave peak at latency 145 ms (millisecond), OCT = Optical coherence tomography, PRVEP = Pattern Reversal Visual Evoked Potential, NCS = Nerve Conduction Study, SSR = Sympathetic Skin Response, IL1 = Interleukin-1, LIF = Leukemia inhibitory factor, CNTF = Ciliary neurotrophic factor, TNF alpha = Tumor necrosis factor-alpha, TGF-beta = Transforming growth factor-beta.

Portable multi-focal visual evoked potential diagnostics for multiple sclerosis/optic neuritis patients.

PURPOSE: Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We've developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes. METHODS: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification. RESULTS: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings. CONCLUSION: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.

BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

Measuring attentional selection of object categories using hierarchical frequency tagging.

In the present study, we used Hierarchical Frequency Tagging (Gordon et al., 2017) to investigate in electroencephalography how different levels of the neural processing hierarchy interact with category-selective attention during visual object recognition. We constructed stimulus sequences of cyclic wavelet scrambled face and house stimuli at two different frequencies (f1 = 0.8 Hz and f2 = 1 Hz). For each trial, two stimulus sequences of different frequencies were superimposed and additionally augmented by a sinusoidal contrast modulation with f3 = 12.5 Hz. This allowed us to simultaneously assess higher level processing using semantic wavelet-induced frequency-tagging (SWIFT) and processing in earlier visual levels using steady-state visually evoked potentials (SSVEPs), along with their intermodulation (IM) components. To investigate the category specificity of the SWIFT signal, we manipulated the category congruence between target and distractor by superimposing two sequences containing stimuli from the same or different object categories. Participants attended to one stimulus (target) and ignored the other (distractor). Our results showed successful tagging of different levels of the cortical hierarchy. Using linear mixed-effects modeling, we detected different attentional modulation effects on lower versus higher processing levels. SWIFT and IM components were substantially increased for target versus distractor stimuli, reflecting attentional selection of the target stimuli. In addition, distractor stimuli from the same category as targets elicited stronger SWIFT signals than distractor stimuli from a different category indicating category-selective attention. In contrast, for IM components, this category-selective attention effect was largely absent, indicating that IM components probably reflect more stimulus-specific processing.

Correcting for ERP latency jitter improves gaze-independent BCI decoding.

Objective.Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze.Approach.ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment.Main results.WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session.Significance. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.