Seizures

Dynamic Difficulty Adjustment With Brain Waves as a Tool for Optimizing Engagement

This study explores the use of electroencephalography (EEG)-based brain wave monitoring to enable dynamic difficulty adjustment (DDA) in a virtual reality (VR) gaming environment. Using the Task Engagement Index (TEI) derived from frontal EEG electrodes, we adapt game challenge levels in real time to maintain optimal player engagement. In a within-subject design with six participants, we found t...

Mind2Matter: Creating 3D Models from EEG Signals

The reconstruction of 3D objects from brain signals has gained significant attention in brain-computer interface (BCI) research. Current research predominantly utilizes functional magnetic resonance imaging (fMRI) for 3D reconstruction tasks due to its excellent spatial resolution. Nevertheless, the clinical utility of fMRI is limited by its prohibitive costs and inability to support real-time o...

Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version

Early dementia diagnosis requires biomarkers sensitive to both structural and functional brain changes. While structural neuroimaging biomarkers hav...

Time-varying EEG spectral power predicts evoked and spontaneous fMRI motor brain activity

Simultaneous EEG-fMRI recordings are increasingly used to investigate brain activity by leveraging the complementary high spatial and high temporal ...

SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting

We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using interictal epileptiform activity (IEA) surrogate bi...

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal activity in the brain, which is often associated ...

A Systematic Literature Review of Unmanned Aerial Vehicles for Healthcare and Emergency Services

Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in various fields. As UAVs become more common across mu...

Brain Signatures of Time Perception in Virtual Reality

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extr...

Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis

Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, ...

Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children, characterized by difficulties in attention, hyp...

Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates

The performance of deep learning methods critically depends on the quality and quantity of the available training data. This is especially the case ...

EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding

Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for acc...

Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reason...

SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings

Epilepsy is a common neurological disorder that affects around 65 million people worldwide. Detecting seizures quickly and accurately is vital, give...

Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation

Emotion recognition is crucial for advancing mental health, healthcare, and technologies like brain-computer interfaces (BCIs). However, EEG-based e...

Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges

Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordin...

A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a c...

FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Ex...

Exploring Deep Learning Models for EEG Neural Decoding

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a mul...

EEG-CLIP : Learning EEG representations from natural language descriptions

Deep networks for electroencephalogram (EEG) decoding are currently often trained to only solve a specific task like pathology or gender decoding. A...