AIMC Topic: Brain

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FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

PloS one
Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. Accurate classification of fetal MRI planes is essential for effective prenatal neurological assessme...

Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.

PloS one
Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in these black-box models raises concerns in sensitive domains such as healthcare, where understanding...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Biomedical physics & engineering express
Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learni...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

Biomedical physics & engineering express
End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency in...

A geometric shape regularity effect in the human brain.

eLife
The perception and production of regular geometric shapes, a characteristic trait of human cultures since prehistory, has unknown neural mechanisms. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and pa...

Primate-informed neural network for visual decision-making.

Proceedings of the National Academy of Sciences of the United States of America
The human brain excels at complex tasks with remarkable efficiency, adaptability, and resilience, making it a powerful source of inspiration for AI. Here, we present a neural dynamics model inspired by the primate dorsal visual pathway, a circuit cru...

Stress detection using the phase controlled Bi-channel adaptive features from the brain EEG signals.

Computers in biology and medicine
This work proposes a stress classification system from the electroencephalogram (EEG) signals collected from the stress subjects. The scheme extracts the phase-controlled Bi-channel adaptive features using a pair of EEG signals. The proposed adaptive...

Functional near-infrared spectroscopy for the detection of fear using parameterized quantum circuits.

Scientific reports
Excessive fear in response to certain stimuli may be a key indicator of anxiety disorders. Its detection makes it valuable for the diagnosis and treatment of such pathologies. Quantum computing has shown promising results in processing different type...

Application of Fully Convolutional Neural Networks in the Assessment of Cerebral White Matter Involvement in Primary Sjögren's Syndrome.

Neuroinformatics
Central nervous system (CNS) involvement in primary Sjögren's syndrome (pSS), although less frequent, can lead to serious complications. Our study aimed to assess white matter (WM) tract integrity, identify specific regions of disruption, quantify di...

Hybrid BCI-based instruction set for dual robotic arm control using EEG and eye movement signals.

Biomedical physics & engineering express
A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, a...