Neurology

Seizures

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

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Is Limited Participant Diversity Impeding EEG-based Machine Learning?

The application of machine learning (ML) to electroencephalography (EEG) has great potential to ad...

NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences

Generative AI is transforming education by enabling personalized, on-demand learning experiences. ...

Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges

Children with neurodevelopmental disorders require timely intervention to improve long-term outcom...

Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical p...

Mar 2025 39962835
Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation

Emotional Recognition in Conversation (ERC) is an important method for diagnosing health condition...

Are foundation models useful feature extractors for electroencephalography analysis?

The success of foundation models in natural language processing and computer vision has motivated ...

KNOWM Memristors in a Bridge Synapse delay-based Reservoir Computing system for detection of epileptic seizures

Nanodevices that show the potential for non-linear transformation of electrical signals and variou...

DreamNet: A Multimodal Framework for Semantic and Emotional Analysis of Sleep Narratives

Dream narratives provide a unique window into human cognition and emotion, yet their systematic an...

Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics

Neurological disorders represent significant global health challenges, driving the advancement of ...

SDA-DDA Semi-supervised Domain Adaptation with Dynamic Distribution Alignment Network For Emotion Recognition Using EEG Signals

In this paper, we focus on the challenge of individual variability in affective brain-computer int...

ZIA: A Theoretical Framework for Zero-Input AI

Zero-Input AI (ZIA) introduces a novel framework for human-computer interaction by enabling proact...

Graph-Based Deep Learning on Stereo EEG for Predicting Seizure Freedom in Epilepsy Patients

Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction ...

Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework

This study introduces a specialized pipeline designed to classify the concentration state of an in...

A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond

Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has...

Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand

We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked pot...

EEG Artifact Detection and Correction with Deep Autoencoders

EEG signals convey important information about brain activity both in healthy and pathological con...

Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding

Brain activity translation into human language delivers the capability to revolutionize machine-hu...

Large Cognition Model: Towards Pretrained EEG Foundation Model

Electroencephalography provides a non-invasive window into brain activity, offering valuable insig...

From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis

While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, i...

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG

All data modalities are not created equal, even when the signal they measure comes from the same s...

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