Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.

Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved.

Binocularly incongruent, multifrequency-coded SSVEP in VR: feasibility and characteristics.

Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing but their implementation in virtual reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).

Correcting for ERP latency jitter improves gaze-independent BCI decoding.

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.

The role of distinct ECoG frequency features in decoding finger movement.

To identify the electrocorticography (ECoG) frequency features that encode distinct finger movement states during repeated finger flexions.We used the publicly available Stanford ECoG dataset of cue-based, repeated single finger flexions. Using linear regression, we identified the spectral features that contributed most to the encoding of movement dynamics and discriminating movement events from rest, and combined them to predict finger movement trajectories.

Predicting phenotypes of elderly from resting state fMRI.

Machine learning techniques are increasingly embraced in neuroimaging studies of healthy and diseased human brains. They have been used successfully in predicting phenotypes, or even clinical outcomes, and in turning functional connectome metrics into phenotype biomarkers of both healthy individuals and patients. In this study, we used functional connectivity characteristics based on resting state functional magnetic resonance imaging data to accurately classify healthy elderly in terms of their phenotype status.

Effects of risk factors on longitudinal changes in brain structure and function in the progression of AD.

Introduction: Past research on Alzheimer's disease (AD) has focused on biomarkers, cognition, and neuroimaging as primary predictors of its progression, albeit additional ones have recently gained attention. When turning to the prediction of the progression from one stage to another, one could benefit from the joint assessment of imaging-based biomarkers and risk/protective factors.

Methods: We included 86 studies that fulfilled our inclusion criteria.

Real-Time Navigation in Google Street View Using a Motor Imagery-Based BCI.

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands.

Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.

Multiway- or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector- or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression).

Host plants and insecticides shape the evolution of genetic and clonal diversity in a major aphid crop pest.

Understanding the spatiotemporal dynamics of pesticide resistance at the landscape scale is essential to anticipate the evolution and spread of new resistance phenotypes. In crop mosaics, host plant specialization in pest populations is likely to dampen the spread of pesticide resistance between different crops even in mobile pests such as aphids. Here, we assessed the contribution of host-based genetic differentiation to the dynamics of resistance alleles in , a major aphid pest which displays several insecticide resistance mechanisms.

Cognitive tasks propagate the neural entrainment in response to a visual 40 Hz stimulation in humans.

Introduction: Alzheimer's disease is one of the great challenges in the coming decades, and despite great efforts, a widely effective disease-modifying therapy in humans remains elusive. One particular promising non-pharmacological therapy that has received increased attention in recent years is based on the Gamma ENtrainment Using Sensory stimulation (GENUS), a high-frequency neural response elicited by a visual and/or auditory stimulus at 40 Hz. While this has shown to be effective in animal models, studies on human participants have reported varying success.

Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression.

We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.

Towards predicting ECoG-BCI performance: assessing the potential of scalp-EEG.

. Implanted brain-computer interfaces (BCIs) employ neural signals to control a computer and may offer an alternative communication channel for people with locked-in syndrome (LIS). Promising results have been obtained using signals from the sensorimotor (SM) area.

Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding.

While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation.We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof.

Phase-Spatial Beamforming Renders a Visual Brain Computer Interface Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target Responses.

Objective: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets.

Global Warming Could Magnify Insect-Driven Apparent Competition Between Native and Introduced Host Plants in Sub-Antarctic Islands.

Pristine sub-Antarctic islands terrestrial ecosystems, including many endemic species, are highly threatened by human-induced cosmopolitan plant invasion. We propose that native plant suppression could be further facilitated by the subsequent invasion by generalist pest species that could exacerbate their competitive exclusion through the process of apparent competition. By comparing the biological parameters of an invasive aphid species, Myzus ascalonicus, on one native (Acaena magellanica) and one invasive (Senecio vulgaris) plant species, we showed that survival and fecundity were higher and development time lower on the native plant species than on the invasive one.

Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach.

With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate.

The Effect of Mental Fatigue and Gender on Working Memory Performance during Repeated Practice by Young and Older Adults.

Working memory (WM) is one of the most investigated cognitive functions albeit the extent to which individual characteristics impact on performance is still unclear, especially when older adults are involved. The present study considers repeated practice of a visual -Back task with three difficulty levels (1-, 2-, and 3-Back) in healthy young and older individuals. Our results reveal that, for both age groups, the expected mental fatigue was countered by a learning effect, in terms of accuracies and reaction times, which turned out to benefit females more than males, for all three -Back levels.

Source space reduction for eLORETA.

We introduce Sparse exact low resolution electromagnetic tomography (eLORETA), a novel method for estimating a nonparametric solution to the source localization problem. Its goal is to generate a sparser solution compared to other source localization methods including eLORETA while benefitting from the latter's superior source localization accuracy.Sparse eLORETA starts by reducing the source space of the Lead Field Matrix using structured sparse Bayesian learning from which a Reduced Lead Field Matrix is constructed, which is used as input to eLORETA.

Practical real-time MEG-based neural interfacing with optically pumped magnetometers.

Background: Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware.

Electrophysiological Proxy of Cognitive Reserve Index.

Cognitive reserve (CR) postulates that individual differences in task performance can be attributed to differences in the brain's ability to recruit additional networks or adopt alternative cognitive strategies. Variables that are descriptive of lifetime experience such as socioeconomic status, educational attainment, and leisure activity are common proxies of CR. CR is mostly studied using neuroimaging techniques such as functional MRI (fMRI) in which case individuals with a higher CR were observed to activate a smaller brain network compared to individuals with a lower CR, when performing a task equally effectively (higher efficiency), and electroencephalography (EEG) where a particular EEG component (P300) that reflects the attention and working memory load, has been targeted.

Fifty million years of beetle evolution along the Antarctic Polar Front.

Global cooling and glacial-interglacial cycles since Antarctica's isolation have been responsible for the diversification of the region's marine fauna. By contrast, these same Earth system processes are thought to have played little role terrestrially, other than driving widespread extinctions. Here, we show that on islands along the Antarctic Polar Front, paleoclimatic processes have been key to diversification of one of the world's most geographically isolated and unique groups of herbivorous beetles-Ectemnorhinini weevils.

N-Back Related ERPs Depend on Stimulus Type, Task Structure, Pre-processing, and Lab Factors.

The N-Back, a common working memory (WM) updating task, is increasingly used in basic and applied psychological research. As such, an increasing number of electroencephalogram (EEG) studies have sought to identify the electrophysiological signatures of N-Back task performance. However, , , pre-processing methods, and differences in the laboratory environment, including the EEG recording setup employed, greatly vary across studies, which in turn may introduce inconsistencies in the obtained results.

Accurate Offline Asynchronous Detection of Individual Finger Movement From Intracranial Brain Signals Using a Novel Multiway Approach.

Asynchronous motor Brain Computer Interfacing (BCI) is characterized by the continuous decoding of intended muscular activity from brain signals. Such applications have gained widespread interest for enabling users to issue commands volitionally. In conventional motor BCIs features extracted from brain signals are concatenated into vector- or matrix-based (or one-/two-way) representations.