Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.

Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to th...

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted to...

A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts.

While disgust originates in the hard-wired mammalian distaste response, the conscious experience of ...

ATST-Net: A method to identify early symptoms in the upper and lower extremities of PD.

Bradykinesia, a core symptom of motor disorders in Parkinson's disease (PD), is a major criterion fo...

Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial.

Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is n...

Imagined speech classification exploiting EEG power spectrum features.

Imagined speech recognition has developed as a significant topic of research in the field of brain-c...

Thrombosed Persistent Median Artery with Coexisting Bifid Median Nerve in a Robotic Arthroplasty Surgeon: A Case Report.

CASE: A 47-year-old orthopaedic surgeon presented with acute volar left wrist pain. He performed ove...

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variabi...

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy di...

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients...

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional ...

Neuromorphic one-shot learning utilizing a phase-transition material.

Design of hardware based on biological principles of neuronal computation and plasticity in the brai...

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connect...

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and pr...

MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition.

. The instability of the EEG acquisition devices may lead to information loss in the channels or fre...

Identifying Bladder Phenotypes After Spinal Cord Injury With Unsupervised Machine Learning: A New Way to Examine Urinary Symptoms and Quality of Life.

PURPOSE: Patients with spinal cord injuries (SCIs) experience variable urinary symptoms and quality ...

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presen...

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