Neurology

Deep learning and feature based medication classifications from EEG in a large clinical data set.

The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type of particular interest is electroencephalographical (EEG) data, collected noninvasively from humans in various behavioral contexts. The Temple Univers...

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is cruci...

The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image.

This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by c...

Artificial intelligence for decision support in acute stroke - current roles and potential.

The identification and treatment of patients with stroke is becoming increasingly complex as more treatment options become available and new relations...

A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington's Disease.

The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington's disease ...

Neurocognitive robot-assisted rehabilitation of hand function: a randomized control trial on motor recovery in subacute stroke.

BACKGROUND: Hand function is often impaired after stroke, strongly affecting the ability to perform daily activities. Upper limb robotic devices have ...

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity.

The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last...

Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

OBJECTIVE: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-codi...

Machine learning-based automated classification of headache disorders using patient-reported questionnaires.

Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply ob...

Emotional EEG classification using connectivity features and convolutional neural networks.

Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studi...

A simple approach to the determination of three curcuminoids with similar chemical structures in different substrates.

The determination of curcuminoids in mixtures is more difficult due to their similar chemical structures as well as serious interferences, thus the co...

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, el...

Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.

Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event...

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech...

Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between...

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alp...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervent...

CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG)...

The role of neuroinflammation in the pathogenesis of glaucoma neurodegeneration.

The chapter is a review enclosed in the volume "Glaucoma: A pancitopatia of the retina and beyond." No cure exists for glaucoma. Knowledge on the mole...

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurolo...