An Iterative Framework for EEG-based Image Search: Robust Retrieval with Weak Classifiers

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The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardiogram(ECG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary obje ...

Traditionally, studies in schizophrenia research employ a single experimental paradigm. The results typically demonstrate a significant difference between patients and controls. Subsequent studies aim to describe the underlying abnormal mechanism at the ge ...

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, dep ...

Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects ongoing organi-zation of brain activity. Characterization of the spatial patterns is an indispensable step in numerous EEG processing pipelines. We present a novel me ...

The brain's functional networks can be assessed using imaging techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Recent studies have suggested a link between the dynamic functional connectivity (dFC) captured by ...

The discovery that human brain connectivity data can be used as a "fingerprint " to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have iden-tified the possibility to extract t ...

Spinal Cord Injury (SCI) affects almost 500,000 people every year, causing complete paralysis of both legs in severe cases, with no current treatment perspective. However, new neuroengineering technologies, such as the Brain Spine Interface (BSI), have eme ...

ABS T R A C T BACKGROUND: Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanism ...

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our ...

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varie ...

The terminology of neurological disorders encompasses a range of serious illnesses (e.g., epilepsy, Alzheimer's disease) leading to morbidity, disability, and stigma. Epilepsy alone affects over 50 million people worldwide, and these figures are rising as ...

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by ...

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

All functions we use in our everyday life depend on a complex interplay between both cortical and subcortical brain areas, communicating in between each others. When a region is affected by either an accident, aging or neurodegenerative diseases, the whole ...

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to r ...

Resting-state fMRI has proven to entail subject-specific signatures that can serve as a fingerprint to identify individuals. Conventional methods are based on building a connectivity matrix based on correlation between the average time course of pairs of b ...

An Iterative Framework for EEG-based Image Search: Robust Retrieval with Weak Classifiers

Chattez avec Graph Search

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Who says you are so sick? An investigation on individual susceptibility to cybersickness triggers using EEG, EGG and ECG

Variability is the rule: Neurophysiology and contextual visual processing in schizophrenia

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Modulation of Visually Induced Self-motion Illusions by α Transcranial Electric Stimulation over the Superior Parietal Cortex

Spectral representation of EEG data using learned graphs with application to motor imagery decoding

Brain's Dynamic Functional Organization with Simultaneous EEG-fMRI Networks

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

Brain-controlled spinal cord stimulation to restore mobility after spinal cord injury

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

Do we really measure what we think we are measuring?

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

Real-time Personalized Monitoring of Neurological Disorders on Wearable Systems

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Noninvasive deep brain stimulation to modulate human behavior by means of transcranial temporal interference electrical stimulation

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Brain Fingerprinting Using Fmri Spectral Signatures On High-Resolution Cortical Graphs