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.

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.

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.

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.

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.

A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.

The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.

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.

Mowat-Wilson syndrome: Case report.

BACKGROUND: Mowat-Wilson syndrome (MWS) is a rare genetic condition resulting in multiple congenital anomalies, including facial dysmorphism, structural anomalies of the internal organs, functional disorders, and, although less commonly, ocular abnormalities. To present a child with MWS and eye abnormalities. METHODS: A 3-year-old boy was born at 37 weeks of pregnancy with dysmorphic features, neurodevelopmental disorders, genetically confirmed MWS, nystagmus, strabismus, and suspicion of congenital glaucoma. Ophthalmic examination was carried out under general anesthesia; eyeball ultrasound and electrophysiological examination (flash visual evoked potentials) were also performed. RESULTS: The examinations revealed nystagmus, a normal response of pupils to light in both eyes, and normal intraocular pressure, that is, 17 and 18 mm Hg in the right and left eye, respectively. Corneal thickness was 606 µm in the right eye and 588 µm in the left eye. Gonioscopy revealed displacement of Schwalbe line anterior to the limbus of the cornea (posterior embryotoxon). Fundus examination revealed a pink optic disk with a cup-to-disc ratio of 0.5, macular pigment regrouping, and normal blood vessels. Flash visual evoked potentials: P2 latency was normal. P2 amplitude from the left hemisphere was reduced to 50%, and P2 amplitude over the right hemisphere was normal. CONCLUSION: Children with genetically determined congenital anomalies need regular ophthalmic checkups to accurately assess the eye and determine the prospects of vision function development.

The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70 %, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18 % for the online experiment. All the experimental results demonstrated the effectiveness of the brain-computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.

Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.

[Morphological features of an optic nerve in premature infants according to the optical coherence tomography data]. / Morfologicheskie osobennosti zritel'nogo nerva u nedonoshennykh detei po dannym opticheskoi kogerentnoi tomografii.

OBJECTIVE: To assess the morphological state of the visual analyzer in premature infants in long-term. MATERIAL AND METHODS: We examined 40 premature children (74 eyes) aged 10.3±2.92 years (gestational age (GA) 25-34 weeks, birth weight (BW) 690-2700 g). Twenty mature children (40 eyes), aged 10.8±3.05 years, were examined as a control group. The children underwent standard ophthalmologic examination, optical coherence tomography (OCT) and recording of visual evoked potentials (VEP). RESULTS: The thickness of retinal nerve fiber layer (RNFL) is less in preterm infants than in term infants, regardless of retinopathy of prematurity (ROP) and refraction (p<0.05). Thickness loss has an inverse proportion with the degree of hypoxic-ischemic encephalopathy (HIE) and intraventricular hemorrhage (IVH) (p<0.05). Retinal thickness in fovea is significantly greater in preterm infants and has a direct proportionality with the degree of IVH and the number of days on artificial lung ventilation (p<0.05). Moderate organic changes were detected in conduction pathways in 43.08% of premature infants according to VEP data. CONCLUSION: The use of OCT and recording of VEP may improve the quality of comprehensive neuro-ophthalmologic diagnosis in preterm infants. The thickness loss of RNFL can be expected in premature infants with HIE and IVH.

An extremely fast neural mechanism to detect emotional visual stimuli: A two-experiment study.

Defining the brain mechanisms underlying initial emotional evaluation is a key but unexplored clue to understanding affective processing. Event-related potentials (ERPs), especially suited for investigating this issue, were recorded in two experiments (n = 36 and n = 35). We presented emotionally negative (spiders) and neutral (wheels) silhouettes homogenized regarding their visual parameters. In Experiment 1, stimuli appeared at fixation or in the periphery (200 trials per condition and location), the former eliciting a N40 (39 milliseconds) and a P80 (or C1: 80 milliseconds) component, and the latter only a P80. In Experiment 2, stimuli were presented only at fixation (500 trials per condition). Again, an N40 (45 milliseconds) was observed, followed by a P100 (or P1: 105 milliseconds). Analyses revealed significantly greater N40-C1P1 peak-to-peak amplitudes for spiders in both experiments, and ANCOVAs showed that these effects were not explained by C1P1 alone, but that processes underlying N40 significantly contributed. Source analyses pointed to V1 as an N40 focus (more clearly in Experiment 2). Sources for C1P1 included V1 (P80) and V2/LOC (P80 and P100). These results and their timing point to low-order structures (such as visual thalamic nuclei or superior colliculi) or the visual cortex itself, as candidates for initial evaluation structures.

Visual evoked potentials in patients with congenital color vision deficiency.

BACKGROUND/AIM: Congenital color vision deficiency (CCVD) is an eye disease characterized by abnormalities in the cone cells in the photoreceptor layer. Visual evoked potentials (VEPs) are electrophysiological tests that physiologically examine the optic nerve, other visual pathways, and the visual cortex. The aim of this research was to determine whether there are VEP abnormalities in CCVD patients. METHODS: Patients with CCVD and healthy individuals were included in this prospective case-control study. Participants with eye disease or neurodegenerative disease were excluded from the study. Pattern reversal VEP (PVEP), flash VEP (FVEP), and optical coherence tomography were performed on all participants. RESULTS: Twenty healthy individuals (15 male) and 21 patients with CCVD (18 male) were included in the study. The mean ages of healthy individuals and patients with CCVD were 29.8 ± 9.6 and 31.1 ± 10.9 years (p = 0.804). Retinal nerve fiber layer thickness and central macular thickness values did not differ between the two groups. In PVEP, Right P100, Left N75, P100, N135 values were delayed in CCVD patients compared to healthy individuals (p = 0.001, p = 0.032, p = 0.003, p = 0.032). At least one PVEP and FVEP abnormality was present in nine (42.9%) and six (28.6%) of the patients, respectively. PVEP or FVEP abnormalities were found in 13 (61.9%) of the patients. CONCLUSION: This study indicated that there may be PVEP and FVEP abnormalities in patients with CCVD.

High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces.

In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.

Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP.

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.

Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals.

Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.

A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection.

Visual-based brain-computer interface (BCI) enables people to communicate with others by spelling words from the brain and helps professionals recognize targets in large numbers of images. P300 signals evoked by different types of stimuli, such as words or images, may vary significantly in terms of both amplitude and latency. A unified approach is required to detect variable P300 signals, which facilitates BCI applications, as well as deepens the understanding of the P300 generation mechanism. In this study, our proposed approach involves a cascade network structure that combines xDAWN and classical EEGNet techniques. This network is designed to classify target and non-target stimuli in both P300 speller and rapid serial visual presentation (RSVP) paradigms. The proposed approach is capable of recognizing more symbols with fewer repetitions (up to 5 rounds) compared to other models while possessing a better information transfer rate (ITR) as demonstrated on Dataset II (17.22 bits/min in the second repetition round) of BCI Competition III. Additionally, our approach has the highest unweighted average recall (UAR) performance for both 5 Hz ( 0.8134±0.0259 ) and 20 Hz ( 0.6527±0.0321 ) RSVP. The results show that the cascade network structure has better performance between both the P300 Speller and RSVP paradigms, manifesting that such a cascade structure is robust enough for dealing with P300-related signals (source code is available at https://github.com/embneural/Cascade-xDAWN-EEGNet-for-ERP-Detection).

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.

Division and spreading of attention across color.

Biological systems must allocate limited perceptual resources to relevant elements in their environment. This often requires simultaneous selection of multiple elements from the same feature dimension (e.g. color). To establish the determinants of divided attentional selection of color, we conducted an experiment that used multicolored displays with four overlapping random dot kinematograms that differed only in hue. We manipulated (i) requirement to focus attention to a single color or divide it between two colors; (ii) distances of distractor hues from target hues in a perceptual color space. We conducted a behavioral and an electroencephalographic experiment, in which each color was tagged by a specific flicker frequency and driving its own steady-state visual evoked potential. Behavioral and neural indices of attention showed several major consistencies. Concurrent selection halved the neural signature of target enhancement observed for single targets, consistent with an approximately equal division of limited resources between two hue-selective foci. Distractors interfered with behavioral performance in a context-dependent fashion but their effects were asymmetric, indicating that perceptual distance did not adequately capture attentional distance. These asymmetries point towards an important role of higher-level mechanisms such as categorization and grouping-by-color in determining the efficiency of attentional allocation in complex, multicolored scenes.