AI Medical Compendium Journal:
Frontiers in human neuroscience

Showing 1 to 10 of 18 articles

Your brain on art, nature, and meditation: a pilot neuroimaging study.

Frontiers in human neuroscience
OBJECTIVES: Exposure to art, nature, or meditation, all transcending human experiences, has beneficial effects on health and wellbeing. Focusing inward or watching art and nature videos elicits positive emotions that can help heal stress-related cond...

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence.

Frontiers in human neuroscience
Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in the United States and other developed countries, and is increasingly diagnosed in developing nations due to improved care and survival of children who are ...

Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.

Frontiers in human neuroscience
INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This...

Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.

Frontiers in human neuroscience
Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences i...

EEG channel and feature investigation in binary and multiple motor imagery task predictions.

Frontiers in human neuroscience
INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine ...

Development and validation of a two-dimensional pseudorandom balance perturbation test.

Frontiers in human neuroscience
INTRODUCTION: Pseudorandom balance perturbations use unpredictable disturbances of the support surface to quantify reactive postural control. The ability to quantify postural responses to a continuous multidirectional perturbation in two orthogonal d...

Predictive power of gait and gait-related cognitive measures in amnestic mild cognitive impairment: a machine learning analysis.

Frontiers in human neuroscience
INTRODUCTION: Gait disorders and gait-related cognitive tests were recently linked to future Alzheimer's Disease (AD) dementia diagnosis in amnestic Mild Cognitive Impairment (aMCI). This study aimed to evaluate the predictive power of gait disorders...

Understanding the role of emotion in decision making process: using machine learning to analyze physiological responses to visual, auditory, and combined stimulation.

Frontiers in human neuroscience
Emotions significantly shape decision-making, and targeted emotional elicitations represent an important factor in neuromarketing, where they impact advertising effectiveness by capturing potential customers' attention intricately associated with emo...

Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks.

Frontiers in human neuroscience
Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differenc...