scholarly journals Photorealistic Reconstruction of Visual Texture From EEG Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Suguru Wakita ◽  
Taiki Orima ◽  
Isamu Motoyoshi
Keyword(s):  
Visual Cortex ◽  
Spatial Resolution ◽  
Neural Activity ◽  
High Spatial Resolution ◽  
Brain Activity ◽  
Spatial Layout ◽  
Eeg Signals ◽  
Visual Texture ◽  
Sensory Stimuli ◽  
Neural Data

Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of “texture,” which is known to be represented as spatially global image statistics in the visual cortex. This property of “texture” enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. The present approach can be used as a method for decoding the highly detailed “impression” of sensory stimuli from brain activity.

scholarly journals Photorealistic reconstruction of visual texture from EEG signals

2021 ◽  
Author(s):  
Suguru Wakita ◽  
Taiki Orima ◽  
Isamu Motoyoshi
Keyword(s):  
Spatial Resolution ◽  
Neural Activity ◽  
High Spatial Resolution ◽  
Brain Activity ◽  
Spatial Layout ◽  
Eeg Signals ◽  
Sensory Stimuli ◽  
Neural Data ◽  
Texture Representation ◽  
Internal Texture

Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of 'texture', which is known to be represented as spatially global image statistics in the visual cortex. This property of 'texture' enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. A subsequent analysis of the dynamic development of the internal texture representation in the VGG network showed that the reproductivity of texture rapidly improves at 200 ms latency in the lower layers but improves more gradually in the higher layers. The present approach can be used as a method for decoding the highly detailed 'impression' of sensory stimuli from brain activity.

Reconstruction of Neural Activity from EEG Data Using Dynamic Spatiotemporal Constraints

2016 ◽  
Vol 26 (07) ◽  
pp. 1650026 ◽  
Author(s):  
E. Giraldo-Suarez ◽  
J. D. Martinez-Vargas ◽  
G. Castellanos-Dominguez
Keyword(s):  
Spatial Resolution ◽  
Neural Activity ◽  
Focal Epilepsy ◽  
Experimental Testing ◽  
Brain Activity ◽  
Brain Regions ◽  
Basis Set ◽  
Eeg Signals ◽  
Reconstruction Performance ◽  
Eeg Data

We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction.

Neuroimaging of the human visceral pain system–A methodological review

2011 ◽  
Vol 2 (3) ◽  
pp. 95-104 ◽  
Author(s):  
Jens Brøndum Frøkjær ◽  
Søren Schou Olesen ◽  
Carina Graversen ◽  
Trine Andresen ◽  
Dina Lelic ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Visceral Pain ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Pharmacological Intervention ◽  
Pain Processing ◽  
Pain Mechanisms ◽  
The Brain

AbstractDuring the last decades there has been a tremendous development of non-invasive methods for assessment of brain activity following visceral pain. Improved methods for neurophysiological and brain imaging techniques have vastly increased our understanding of the central processing of gastrointestinal sensation and pain in both healthy volunteers as well as in patients suffering from gastrointestinal disorders. The techniques used are functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG)/evoked brain potentials (EPs), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), and the multimodal combinations of these techniques. The use of these techniques has brought new insight into the complex brain processes underlying pain perception, including a number of subcortical and cortical regions, and paved new ways in our understanding of acute and chronic pain. The pathways are dynamic with a delicate balance between facilitatory and inhibitory pain mechanisms, and with modulation of the response to internal or external stressors with a high degree of plasticity. Hence, the ultimate goal in imaging of pain is to follow the stimulus response throughout the neuraxis.Brain activity measured by fMRI is based on subtracting regional changes in blood oxygenation during a resting condition from the signal during a stimulus condition, and has high spatial resolution but low temporal resolution. SPECT and PET are nuclear imaging techniques where radiolabeled molecules are injected with visualization of the distribution, density and activity of receptors in the brain allowing not only assessment of brain activity but also study of receptor sites. EEG is based on assessment of electrical activity in the brain, and recordings of the resting EEG and evoked potentials following an external stimulus are used to study normal visceral pain processing, alterations of pain processing in different patient groups and the effect of pharmacological intervention. EEG has high temporal resolution, but relative poor spatial resolution, which however to some extent can be overcome by applying inverse modelling algorithms and signal decomposition procedures. MEG is based on recording the magnetic fields produced by electrical currents in the brain, has high spatial resolution and is especially suitable for the study cortical activation.The treatment of chronic abdominal pain is often ineffective and dissapointing, which leads to search for optimized treatment achieved on the basis of a better understanding of underlying pain mechanisms. Application of the recent improvements in neuroimaging on the visceral pain system may likely in near future contribute substantially to our understanding of the functional and structural pathophysiology underlying chronic visceral pain disorders, and pave the road for optimized individual and mechanism based treatments.The purpose of this review is to give a state-of-the-art overview of these methods, with focus on EEG, and especially the advantages and limitations of the single methods in clinical gastrointestinal pain esearch including examples from relevant studies.

scholarly journals Contextual effects on binocular matching are evident in primary visual cortex

2019 ◽  
Author(s):  
Reuben Rideaux ◽  
Andrew E Welchman
Keyword(s):  
Visual Cortex ◽  
Visual Perception ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Contextual Effects ◽  
Local Processing ◽  
Neuronal Responses ◽  
Global Context ◽  
Binocular Matching

ABSTRACTGlobal context can dramatically influence local visual perception. This phenomenon is well-documented for monocular features, e.g., the Kanizsa triangle. It has been demonstrated for binocular matching: the disambiguation of the Wallpaper Illusion (Brewster, 1844) via the luminance of the background (Anderson & Nakayama, 1994). For monocular features, there is evidence that global context can influence neuronal responses as early as V1 (Muckli et al., 2015). However, for binocular matching, the activity in this area of the visual cortex is thought to represent local processing, suggesting that the influence of global context may occur at later stages of cortical processing. Here we sought to test if binocular matching is influenced by contextual effects in V1, using fMRI to measure brain activity while participants viewed perceptually ambiguous “wallpaper” stereograms whose depth was disambiguated by the luminance of the surrounding region. We localized voxels in V1 corresponding to the ambiguous region of the pattern, i.e., where the signal received from the eyes was not predictive of depth, and despite the ambiguity of the input signal, using multi-voxel pattern analysis we were able to reliably decode perceived (near/far) depth from the activity of these voxels. These findings indicate that stereoscopic related neural activity is influenced by global context as early as V1.

scholarly journals In vivo fluorescence imaging with a flat, lensless microscope

2020 ◽  
Author(s):  
Jesse K. Adams ◽  
Vivek Boominathan ◽  
Sibo Gao ◽  
Alex V. Rodriguez ◽  
Dong Yan ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Animal Movement ◽  
Brain Activity ◽  
Phase Mask ◽  
Reconstruction Algorithms ◽  
Lensless Imaging ◽  
Computational Reconstruction

AbstractFluorescence imaging over large areas of the brain in freely behaving animals would allow researchers to better understand the relationship between brain activity and behavior; however, traditional microscopes capable of high spatial resolution and large fields of view (FOVs) require large and heavy lenses that restrict animal movement. While lensless imaging has the potential to achieve both high spatial resolution and large FOV with a thin lightweight device, lensless imaging has yet to be achieved in vivo due to two principal challenges: (a) biological tissue typically has lower contrast than resolution targets, and (b) illumination and filtering must be integrated into this non-traditional device architecture. Here, we show that in vivo fluorescence imaging is possible with a thin lensless microscope by optimizing the phase mask and computational reconstruction algorithms, and integrating fiber optic illumination and thin-film color filters. The result is a flat, lensless imager that achieves better than 10 μm spatial resolution and a FOV that is 30× larger than other cellular resolution miniature microscopes.

High spatial resolution AEM observations of yttrium segregation in chromia scales grown on Co-45%Cr

1986 ◽  
Vol 44 ◽  
pp. 518-519
Author(s):  
K. Przybylski ◽  
A. J. Garratt-Reed ◽  
G. J. Yurek
Keyword(s):  
Spatial Resolution ◽  
Outer Surface ◽  
Maximum Concentration ◽  
High Spatial Resolution ◽  
Reactive Elements ◽  
Chromia Scales

The addition of so-called “reactive” elements such as yttrium to alloys is known to enhance the protective nature of Cr2O3 or Al2O3 scales. However, the mechanism by which this enhancement is achieved remains unclear. An A.E.M. study has been performed of scales grown at 1000°C for 25 hr. in pure O2 on Co-45%Cr implanted at 70 keV with 2x1016 atoms/cm2 of yttrium. In the unoxidized alloys it was calculated that the maximum concentration of Y was 13.9 wt% at a depth of about 17 nm. SIMS results showed that in the scale the yttrium remained near the outer surface.

Integration of Core-Edge Spectroscopy Methods for the Study of Polymers

1996 ◽  
Vol 54 ◽  
pp. 154-155
Author(s):  
E. G. Rightor
Keyword(s):  
Excited State ◽  
Polymer Blends ◽  
Gas Phase ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Electronic States ◽  
Core Electron ◽  
Bulk Solids ◽  
Development Application

Core edge spectroscopy methods are versatile tools for investigating a wide variety of materials. They can be used to probe the electronic states of materials in bulk solids, on surfaces, or in the gas phase. This family of methods involves promoting an inner shell (core) electron to an excited state and recording either the primary excitation or secondary decay of the excited state. The techniques are complimentary and have different strengths and limitations for studying challenging aspects of materials. The need to identify components in polymers or polymer blends at high spatial resolution has driven development, application, and integration of results from several of these methods.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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