Seizures

Multivariate pattern analysis reveals resting-state EEG biomarkers in fibromyalgia

Fibromyalgia (FM) involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. Resting-state (RS) electroencephalography (EEG) studies have revealed abnormal brain activity in chronic pain conditions, with anxiety and symptom duration potentially exacerbating these alterations. This study applied multivariate pattern analy...

Your Emotions, My Brain: Generalizable Neural Signatures of Emotional Memory Reactivation During Sleep

Reactivation in sleep alters the structure of memories and can potentially be used to restructure upsetting representations. Reactivation can be triggered with auditory cues and then detected using machine learning and electroencephalography (EEG), but can we also detect the emotionality of reactivated memories? We examined this by presenting auditory cues that had been associated with negative or...

Frequency bands EEG Biomarkers for Dementia using Graph Neural Networks

We introduce a simple and interpretable model for classification of electroencephalography (EEG) signals. Our focus essentially is on using deep learn...

Interpreting Sleep Activity Through Neural Contrastive Learning

Memories are spontaneously replayed during sleep, a process thought to support memory consolidation. However, capturing this replay in humans has been...

Cerebral Organoids Uncover Mechanisms of Neural Activity Changes in Epileptogenesis

Neurological disorders often originate from progressive brain network dysfunctions that start years before symptoms appear. How these changes emerge i...

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers

Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns—r...

Trustworthy Sleep Staging from EEG: Deep Ensembles, MC Dropout, and Predictive Calibration

Reliable sleep stage classification from EEG signals is critical for the development of clinical decision support systems. However, many deep learning...

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language mode...

Dynamic Graphs Analysis of EEG

In this study, we investigate the use of temporal dynamics in brain connectivity for the classification of electroencephalography (EEG) signals using ...

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentia...

The Use of Artificial Intelligence In Magnetic Resonance Imaging of Epilepsy: A Systematic Review and Meta-Analysis

The application of artificial intelligence (AI)/machine learning (ML) to MRI can be a powerful tool to streamline clinical decision-making, yet variab...

Design and Implementation of a Decision Making System for Controlling a Hand Exoskeleton Based on EEG/EMG Signals

This paper presents an approach of combining Electroencephalography (EEG) and Electromyography (EMG) signals to create a hybrid Brain Interface Comput...

A Shared Neural Marker Predicts Creative Performance Across Distinct Problem-Solving Tasks

Creativity is essential for innovation, yet the brain mechanisms supporting its moment-to-moment variability remain unclear. We hypothesize that creat...

Distinct brain mechanisms support trust violations, belief integration, and bias in human-AI teams

This study provides an integrated electrophysiological and behavioral account of the neuro-cognitive markers underlying trust evolution during human i...

A lightweight, physics-based, sensor-fusion filter for real-time EEG denoising and improved downstream AI classification

Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular, and muscle artifacts that hinder real-time infere...

Interpretable Machine Learning Identifies an Emergent Absence Seizure Mechanism

Absence epilepsy is a generalized seizure disorder marked by widespread spike-and-wave oscillations and sudden lapses in consciousness. Although no co...

Variational autoencoder for interpretable seizure onset phases detection

In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patients with fo...

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) are a leading computational framework for understanding neural visual processing. A standard approach for evaluating their...

High-level Prediction of Continuous Speech During Mind-Wandering

Abundant evidence shows that when listening to speech or reading text, we continuously make predictions about upcoming words. Does this process stop w...

Toward Unified Biomarkers for Focal Epilepsy

Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilepsy treatment, largely due to limited mechanistic u...