EEG-based action anticipation in human-robot interaction: a comparative pilot study.

As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques.

Alterations of white matter microstructure in migraine patients vary in the peri-ictal phases.

Alterations in white matter (WM) microstructure are commonly found in migraine patients. Here, we employ a longitudinal study of episodic migraine without aura using diffusion MRI (dMRI) to investigate whether such WM microstructure alterations vary through the different phases of the pain cycle. Fourteen patients with episodic migraine without aura related with menstruation were scanned through four phases of their (spontaneous) migraine cycle (interictal, preictal, ictal and postictal).

[The role of EEG in tomorrow's medicine].

There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.

A custom-built single-channel in-ear electroencephalography sensor for sleep phase detection: an interdependent solution for at-home sleep studies.

Sleep is vital for health. It has regenerative and protective functions. Its disruption reduces the quality of life and increases susceptibility to disease.

Involvement of the cerebellum in structural connectivity enhancement in episodic migraine.

Background: The pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls.

Interactive mirrOring Games wIth sOCial rObot (IOGIOCO): a pilot study on the use of intransitive gestures in a sample of Italian preschool children with autism spectrum disorder.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication, social interaction, and restricted behaviors. The importance of early intervention has been widely demonstrated, and developmental trajectories in ASD emphasize the importance of nonverbal communication, such as intransitive gesture production, as a possible positive prognostic factor for language development. The use of technological tools in the therapy of individuals with ASD has also become increasingly important due to their higher engagement and responsiveness to technological objects, such as robots.

Long-term Eulerian Sea surface temperature records along the Portuguese Coast.

Monitoring ocean surface temperature is critical to infer the variability of the upper layers of the ocean, from short temporal scales to climatic change scales. Analysis of the climatological trends and anomalies is fundamental to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The original data for the dataset presented was collected by the Portuguese Hydrographic Institute () using seven Ondograph and Meteo-oceanography buoys anchored offshore along the Portuguese coast to acquire ocean surface temperatures.

Realistic 3D human saccades generated by a 6-DOF biomimetic robotic eye under optimal control.

We recently developed a biomimetic robotic eye with six independent tendons, each controlled by their own rotatory motor, and with insertions on the eye ball that faithfully mimic the biomechanics of the human eye. We constructed an accurate physical computational model of this system, and learned to control its nonlinear dynamics by optimising a cost that penalised saccade inaccuracy, movement duration, and total energy expenditure of the motors. To speed up the calculations, the physical simulator was approximated by a recurrent neural network (NARX).

Stereo reconstruction from microscopic images for computer-assisted ophthalmic surgery.

Purpose: This work presents a novel platform for stereo reconstruction in anterior segment ophthalmic surgery to enable enhanced scene understanding, especially depth perception, for advanced computer-assisted eye surgery by effectively addressing the lack of texture and corneal distortions artifacts in the surgical scene.

Methods: The proposed platform for stereo reconstruction uses a two-step approach: generating a sparse 3D point cloud from microscopic images, deriving a dense 3D representation by fitting surfaces onto the point cloud, and considering geometrical priors of the eye anatomy. We incorporate a pre-processing step to rectify distortion artifacts induced by the cornea's high refractive power, achieved by aligning a 3D phenotypical cornea geometry model to the images and computing a distortion map using ray tracing.

Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images.

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction.

Fisheries Inspection in Portuguese Waters from 2015 to 2023.

As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas.

Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain.

There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

Purpose: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model.

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death.

Wrist-Based Fall Detection: Towards Generalization across Datasets.

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors.

Effects of motor imagery-based neurofeedback training after bilateral repetitive transcranial magnetic stimulation on post-stroke upper limb motor function: an exploratory crossover clinical trial.

Objective: To examine the clinical effects of combining motor imagery-based neurofeedback training with bilateral repetitive transcranial magnetic stimulation for upper limb motor function in subacute and chronic stroke.

Design: Clinical trial following an AB/BA crossover design with counterbalanced assignment.

Subjects: Twenty individuals with subacute (n = 4) or chronic stroke (n = 16).

Impact of truncating diffusion MRI scans on diffusional kurtosis imaging.

Objective: Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (Opt); 2) spherical codes (Opt); 3) random (Random).

Side-scan sonar imaging data of underwater vehicles for mine detection.

Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned.