Movements of the same upper limb can be classified from low-frequency time-domain EEG signals

Brain-computer interfaces (BCIs) can be used to control neuroprostheses of spinal cord injured (SCI) persons. A neuroprosthesis can restore different movement functions (e.g., hand open/close, supination/pronation etc.), and requires a BCI with a sufficiently high number of classes. However, sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. Since a couple of years, a new EEG feature has evolved: low-frequency time-domain signals. For example movement trajectories [1] and movement directions [2] were decoded using this feature. In the present study, we investigated whether low-frequency time-domain signals can also be used to classify several (executed) hand/arm movements of the same limb. A BCI relying on the imagination of such movements may be used to control a neuroprosthesis more naturally and provide a higher number of classes

Time domain classification of grasp and hold tasks

Brain-Computer Interfaces (BCIs) enable its users to interact with their environment only by thought. Earlier studies indicated [1, 2] that BCI might be a suitable method for controlling a neuroprostheses, which could assist people with spinal cord injuries (SCI) in their daily life. One drawback for the end user is that only simple motor imaginations (MI) are available for control e.g. MI of both feet to control ones arm is abstract and in contradiction to an associated natural movement. Therefore we are looking for means to design a more natural control modality. One promising scenario would be to use MI of different grasps to actually control different grasps of the neuroprosthesis. In this study we attempt to classify the execution of different grasp types in low-frequency time-domain EEG signals

Discriminating goal-directed from nongoal-directed movements and its potential impact for BCI control

Differences in the electroencephalographic (EEG) recordings between the execution of goal-directed and nongoal-directed movements have been recently shown in [1]. Such differences can be of interest for brain-computer interfaces (BCIs) control, when combined with information on the kinematic level (e.g. velocity decoding), since this combination mirrors the hierarchic way one plans a movement. In this study, we show that the time-domain differences between these movements are discriminable in a single-trial classification

Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the results from several motor decoding studies including our study (Ofner et al. 2017). While we endorse their warning about artefacts in general, we do have doubts whether their work supports such a statement with respect to our study. We provide in this commentary a more nuanced contextualization of our work presented in Ofner et al. and the type of artefacts investigated in Bibian et al

A New Advanced Backcross Tomato Population Enables High Resolution Leaf QTL Mapping and Gene Identification.

Quantitative Trait Loci (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the S. pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here, we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennellii This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC2 and BC3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs, we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous hidden Markov model for genotyping the lines, and by using state-of-the-art sparse regression techniques for QTL mapping

Moregrasp: Restoration of Upper Limb Function in Individuals with High Spinal Cord Injury by Multimodal Neuroprostheses for Interaction in Daily Activities

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a brain-computer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation of a user-centered design

Absence of antibodies against Brucella sp. in different species of semi free-ranging Bovidae in the Emirate of Dubai, United Arab Emirates

Summary Durin

Care Trajectories of Veterans in the Twelve Months following Hospitalization for Acute Ischemic Stroke

Background—Recovery after a stroke varies greatly between individuals and is reflected by wide variation in the use of institutional and home care services. This study sought to classify veterans according to their care trajectories in the 12 months after hospitalization for ischemic stroke. Methods and Results—The sample consisted of 3811 veterans hospitalized for ischemic stroke in Veterans Health Administration facilities in 2007. Three outcomes—nursing home care, home care, and mortality—were modeled jointly >12 months using latent class growth analysis. Data on Veterans’ care use and cost came from the Veterans Administration and Medicare. Covariates included stroke severity (National Institutes of Health Stroke Scale), functional status (functional independence measure score), age, marital status, chronic conditions, and prestroke ambulation. Five care trajectories were identified: 49% of Veterans had Rapid Recovery with little or no use of care; 15% had a Steady Recovery with initially high nursing home or home care that tapered off; 9% had Long-Term Home Care; 13% had Long-Term Nursing Home Care; and 14% had an Unstable trajectory with multiple transitions between long-term and acute care settings. Care use was greatest for individuals with more severe strokes, lower functioning at hospital discharge, and older age. Average annual costs were highest for individuals with the Long-Term Nursing Home trajectory ($63 082), closely followed by individuals with the Unstable trajectory ($58 720). Individual with the Rapid Recovery trajectory had the lowest costs ($9271). Conclusions—Care trajectories after stroke were associated with stroke severity and functional dependency and they had a dramatic impact on subsequent costs