Seizures

Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review

The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized responses. The attainment of such a level of emotiona...

Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data a...

EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.

BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer in...

Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health

Integrating physiological signals such as electroencephalogram (EEG), with other data such as interview audio, may offer valuable multimodal insight...

Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation

Electroencephalography (EEG) provides reliable indications of human cognition and mental states. Accurate emotion recognition from EEG remains chall...

Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep

We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving these altered s...

EEG-SSM: Leveraging State-Space Model for Dementia Detection

State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shor...

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of ...

Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning

Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices...

A Novel Approach to Image EEG Sleep Data for Improving Quality of Life in Patients Suffering From Brain Injuries Using DreamDiffusion

Those experiencing strokes, traumatic brain injuries, and drug complications can often end up hospitalized and diagnosed with coma or locked-in synd...

Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object Detection

Advanced cognition can be extracted from the human brain using brain-computer interfaces. Integrating these interfaces with computer vision techniqu...

EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More

Recently, electroencephalography (EEG) signals have been actively incorporated to decode brain activity to visual or textual stimuli and achieve obj...

EEG Emotion Recognition Based on 3D-CTransNet.

Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the ...

An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific mode...

Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.

In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing...

A Method of Cross-Subject Transfer Learning for Ultra Short Time SSVEP Classification.

The steady-state visual evoked potentials (SSVEP) based brain-computer interfaces (BCIs) require extensive training data for efficient classification,...

Bi-Stream Adaptation Network for Motor Imagery Decoding.

Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information ca...

EEG Tensorization Enhances CNN-Based Outcome Classification in Comatose Patients Following a Cardiac Arrest.

Standard diagnostic methods for evaluating the severity of brain injuries resulting from cardiac arrest, such as the Glasgow Coma Scale, exhibit subje...

Contrastive Self-supervised EEG Representation Learning for Emotion Classification.

Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that ap...

Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data.

EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recen...