Visual short‐term memory‐related EEG components in a virtual reality setup

Virtual reality (VR) offers a powerful tool for investigating cognitive processes, as it allows researchers to gauge behaviors and mental states in complex, yet highly controlled, scenarios. The use of VR head-mounted displays in combination with physiological measures such as EEG presents new challenges and raises the question whether established findings also generalize to a VR setup. Here, we used a VR headset to assess the spatial constraints underlying two well-established EEG correlates of visual short-term memory: the amplitude of the contralateral delay activity (CDA) and the lateralization of induced alpha power during memory retention. We tested observers' visual memory in a change detection task with bilateral stimulus arrays of either two or four items while varying the horizontal eccentricity of the memory arrays (4, 9, or 14 degrees of visual angle). The CDA amplitude differed between high and low memory load at the two smaller eccentricities, but not at the largest eccentricity. Neither memory load nor eccentricity significantly influenced the observed alpha lateralization. We further fitted time-resolved spatial filters to decode memory load from the event-related potential as well as from its time-frequency decomposition. Classification performance during the retention interval was above-chance level for both approaches and did not vary significantly across eccentricities. We conclude that commercial VR hardware can be utilized to study the CDA and lateralized alpha power, and we provide caveats for future studies targeting these EEG markers of visual memory in a VR setup.Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347Cooperation between the Max Planck Society and the Fraunhofer GesellschaftDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

Decoding subjective emotional arousal from eeg during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience

Visual short-term memory related EEG components in a virtual reality setup

We used a VR headset to investigate two prominent EEG components that are related to the maintenance of representations in visual short-term memory: the contralateral delay activity (CDA) and the lateralization of alpha power. We manipulated both the memory load and the horizontal eccentricity of the stimuli to assess the influence of these factors on the EEG components in our VR-EEG setup

The Selhausen Minirhizotron Facilities: A Unique Set-Up to Investigate Subsoil Processes within the Soil-Plant Continuum

Climate change raises new challenges for agriculture. A comprehensive understanding of whole plant responses to a changing environment is the key to maintain yield and improve sustainable crop production. Although there are many projects approaching this challenge, most studies focus on the acquisition and analysis of above-ground field data. The subsoil processes involved in plant root growth and resource acquisition are rarely in focus, since very complex set-ups are required to obtain these data on field scale. Therefore, detailed measurement of the plant roots and the corresponding soil conditions are required. The minirhizotron facilities in Selhausen (Germany) are located within the TERENO-Selhausen test site in the lower Rhine valley. They enable non-invasive longer-term studies of the soil–plant continuum on two different soils in the same climate by offering a unique set-up to record above- and belowground information over entire crop growing seasons under various field conditions and agronomic treatments. Detailed information about soil water content, soil water potential, soil temperature and root development are collected with a high spatial and temporal resolution. Above-ground measurements, such as biomass, transpiration fluxes and assimilation rates are performed additionally. In recent years, continuous development and improvement of measurement technology and data analysis has facilitated the process, transfer and access to these data. Currently several dynamic and permanently installed sensors are used within the facilities. 7 m-long transparent tubes are horizontally located in several depths. An in-house developed RGB-camera system enables root imaging along the tubes in multiple directions. The images are analyzed with a deep neural network-based analysis pipeline that provides relevant root system traits, such as total root length and root length density. To obtain the spatial soil water content variations per depth, crosshole ground-penetrating radar (GPR) measurements are performed between the tubes. The derived permittivity and hence soil water content values show a clear spatial variation along the tubes and different behaviors for various plant and soil types. Recently, a novel analysis tool to derive the trend‑corrected spatial permittivity deviation was introduced, allowing an investigation of the GPR variability independently of static and dynamic influences.The ongoing measurements currently cover five years of wheat and maize trials, including water stress treatments, sowing density, planting time, and crop mixtures. Data collected in this study are available through the TERENO data portal and can be used to develop, calibrate, and validate models of the soil–plant continuum across different scales, including soil process, root development and root water uptake models, as well as model compilations, such as single-plant and multi-plant models. Further, the data can be of direct use for agronomists and ecologist

Estimating the effect of maize crops on time-lapse horizontal crosshole GPR data

Investigating soil, roots and their interaction is important to optimize agricultural practices like irrigation and fertilization and therefore increase the sustainability and productivity of crop production. In this study, we are combining two methods to examine non-invasively, characterize and monitor the soil-root zone throughout crop growing seasons: crosshole ground penetrating radar (GPR) and root-images within horizontal mini-rhizotrons. Over three maize crop growing seasons, we acquired in-situ time-lapse crosshole ground penetrating radar data and time-lapse root images, at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to horizontally measure data at six different depths, ranging between 0.1 m - 1.2 m and below three different plots with varying agricultural treatments, such as irrigation, sowing density, sowing date and cultivars. The GPR measurements result in the dielectric permittivity slices by applying standard ray-based analysis to zero-offset measurements along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro physical relationships. Additionally, the root images provide a root fraction per image, which is derived by using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The dielectric permittivity slices suggest a permittivity variation along the horizontal and vertical axes, depending on atmospheric conditions, soil properties, and root architecture. To quantify the influence of the roots on the spatial and temporal distribution of dielectric permittivity, we used statistical methods to reduce the impacting factors like soil heterogeneity, tube deviations and changing atmospheric conditions, which results in the spatial and temporal variability. For verification these permittivity variabilities are compared to the root fraction values. In general, using the spatial and temporal permittivity variations, we can detect the presence of roots and additionally recognize a varying influence of the roots over the duration of the crop growing season. Using these first results, we demonstrate that GPR can be applied to improve the characterization of the root-soil system related to maize plants. This could be the first step towards developing proxies e.g. for irrigation and fertilization applications using this non-invasive method

Using horizontal borehole GPR data to estimate the effect of maize plants on the spatial and temporal distribution of dielectric permittivity

Agro-ecosystems and their yield productivity are influenced by root water and nutrient uptake. This uptake depends on the crop root architecture and the soil water content distribution within the soil-root zone. Investigating this zone and its processes can help to optimize agricultural practices, like irrigation and fertilization and therefore helps to achieve the goal for sustainable crop production. Mini-rhizotrons have shown to be effective to non-invasively investigate the soil-root zone throughout crop growing seasons using horizontal rhizotubes installed at different depths in the subsurface. In this study, in-situ time-lapse crosshole ground penetrating radar measurements and root images were collected over three maize crop growing seasons at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to measure data at six different depths ranging between 0.1 m - 1.2 m and for three different plots with varying treatments. The dielectric permittivity was derived from the horizontal crosshole GPR measurements by using standard ray-based analysis along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro-physical relationships. The root architecture is expressed as root length density and is derived from the images, using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The results of the dielectric permittivity indicate horizontal and vertical variations, depending on weather conditions, soil properties, and root architecture. To quantify the impact of the roots on the spatial and temporal distribution of the dielectric permittivity, we used statistical methods to eliminate the effects of soil heterogeneity, tube deviations and daily evapotranspiration changes. Resulting in permittivity variation along the rhizotubes impacted by the presence of roots

Using horizontal borehole GPR data to estimate the effect of maize plants on the spatial and temporal distribution of dielectric permittivity

Agro-ecosystems and their yield productivity are influenced by root water and nutrient uptake. This uptake depends on the crop root architecture and the soil water content distribution within the soil-root zone. Investigating this zone and its processes can help to optimize agricultural practices, like irrigation and fertilization and therefore helps to achieve the goal for sustainable crop production. Mini-rhizotrons have shown to be effective to non-invasively investigate the soil-root zone throughout crop growing seasons using horizontal rhizotubes installed at different depths in the subsurface. In this study, in-situ time-lapse crosshole ground penetrating radar measurements and root images were collected over three maize crop growing seasons at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to measure data at six different depths ranging between 0.1 m - 1.2 m and for three different plots with varying treatments. The dielectric permittivity was derived from the horizontal crosshole GPR measurements by using standard ray-based analysis along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro-physical relationships. The root architecture is expressed as root length density and is derived from the images, using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The results of the dielectric permittivity indicate horizontal and vertical variations, depending on weather conditions, soil properties, and root architecture. To quantify the impact of the roots on the spatial and temporal distribution of the dielectric permittivity, we used statistical methods to eliminate the effects of soil heterogeneity, tube deviations and daily evapotranspiration changes. Resulting in permittivity variation along the rhizotubes impacted by the presence of roots

Linking horizontal crosshole GPR variability with root image information for maize crops

Abstract Non‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend‐corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination (R2) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability

OpenVirtualObjects: An Open Set of Standardized and Validated 3D Household Objects for Virtual Reality-Based Research, Assessment, and Therapy

Virtual reality (VR) technology provides clinicians, therapists, and researchers with new opportunities to observe, assess, and train behavior in realistic yet well-controlled environments. However, VR also comes with a number of challenges. For example, compared to more abstract experiments and tests on 2D computer screens, VR-based tasks are more complex to create, which can make it more expensive and time-consuming. One way to overcome these challenges is to create, standardize, and validate VR content and to make it openly available for researchers and clinicians. Here we introduce the OpenVirtualObjects (OVO), a set of 124 realistic 3D household objects that people encounter and use in their everyday lives. The objects were rated by 34 younger and 25 older adults for recognizability, familiarity, details (i.e., visual complexity), contact, and usage (i.e., frequency of usage in daily life). All participants also named and categorized the objects. We provide the data and the experiment- and analysis code online.With OVO, we hope to facilitate VR-based research and clinical applications. Easy and free availability of standardized and validated 3D objects can support systematic VR-based studies and the development of VR-based diagnostics and therapeutic tools