Seizures

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and physiological variability. Manual artifact detection is labor-intensive and subjective, underscoring the need for automated methods. This study evaluates the performance of three machine learning classifiers - Random Forest (RF), Support Vector Machine (...

Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

Pretrained generative models have opened new frontiers in brain decoding by enabling the synthesis of realistic texts and images from non-invasive brain recordings. However, the reliability of such outputs remains questionable--whether they truly reflect semantic activation in the brain, or are merely hallucinated by the powerful generative models. In this paper, we focus on EEG-to-text decoding...

Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs

We propose a novel framework for integrating fragmented multi-modal data in Alzheimer's disease (AD) research using large language models (LLMs) and...

QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems

A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assi...

A CNN-based approach for detecting eye blink episodes in EEG signals.

This study aims to develop and evaluate a convolutional neural network (CNN)-based architecture for detecting eye blink episodes in electroencephalogr...

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.

Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis ar...

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.

Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, a...

Commonality and individuality based graph learning network for EEG emotion recognition.

The interplay between individual differences and shared human characteristics significantly impacts electroencephalogram (EEG) emotion recognition mod...

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare.

Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on ...

Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net

High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistica...

Adaptive control for multi-scale stochastic dynamical systems with stochastic next generation reservoir computing

The rapid advancement of neuroscience and machine learning has established data-driven stochastic dynamical system modeling as a powerful tool for u...

Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.

Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognitio...

Bipartite Graph Adversarial Network for Subject-Independent Emotion Recognition.

Emotions play a vital role in connecting and sharing with others. However, individuals with emotional disorders face challenges in expressing their em...

Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.

Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associa...

Predicting antiseizure medication response in newly diagnosed epilepsy using quantitative EEG and machine learning.

BACKGROUND: Predicting long-term outcomes in newly diagnosed epilepsy remains limited by reliance on clinical features and visual EEG interpretation. ...

The contribution of the locus coeruleus - norepinephrine system to the coupling between pupil-linked arousal and cortical state.

Understanding how pupil-linked arousal couples with cortical state is crucial for uncovering the neural mechanisms underlying brain state-dependent co...

Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.

Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disor...

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models

Background and Objective. Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling ...

Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network.

Epilepsy is a neurological disorder characterized by recurrent seizures caused by excessive electrical discharges in brain cells, posing significant d...

A Computational Approach to Epilepsy Treatment: An AI-optimized Global Natural Product Prescription System

Epilepsy is a prevalent neurological disease with millions of patients worldwide. Many patients have turned to alternative medicine due to the limit...