Seizures

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 potential (SSVEP)-based EEG and facial EMG to improve multimodal control and mitigate fatigue in assistive applications. Traditional BMIs relying solely on EEG or EMG suffer from inherent limitations; EEG-based control requires sustained visual focus, leading to cognitive fatigue, while EMG-based contr...

EEG Artifact Detection and Correction with Deep Autoencoders

EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert intervention. This study presents LSTEEG, a novel LSTM-based autoencoder designed for th...

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-human interaction while providing communication supp...

Large Cognition Model: Towards Pretrained EEG Foundation Model

Electroencephalography provides a non-invasive window into brain activity, offering valuable insights for neurological research, brain-computer inte...

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, it is limited by high operational costs and signifi...

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 source. In the case of the brain, two of the most i...

FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model

Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applica...

Neurophysiological correlates to the human brain complexity through $q$-statistical analysis of electroencephalogram

The prospects of assessing neural complexity (NC) by $q$-statistics of the systemic organization of different types and levels of brain activity wer...

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking

EEG-based neural networks, pivotal in medical diagnosis and brain-computer interfaces, face significant intellectual property (IP) risks due to thei...

From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping

Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but ...

Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic character...

Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study

This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) de...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely...

Human fields and their impact on brain waves A pilot study

During brain function, groups of neurons fire synchronously. When these groups are large enough, the resulting electrical signals can be measured on...

Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task

As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroencephalogram (EEG) signals should be further invest...

Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

Emotions play a crucial role in human behavior and decision-making, making emotion recognition a key area of interest in human-computer interaction ...

SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (...

Vagus nerve stimulation as a modulator of feedforward and feedback neural transmission

Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various neurological and psychiatric conditions, including ...

Machine Learning Fairness for Depression Detection using EEG Data

This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We ...

On the challenges of detecting MCI using EEG in the wild

Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) d...