Seizures

LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring

Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant role in understanding sleep patterns and disorders and underscore the need for ro...

CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

Electroencephalogram (EEG) signals are critical for detecting abnormal brain activity, but their high dimensionality and complexity pose significant challenges for effective analysis. In this paper, we propose CwA-T, a novel framework that combines a channelwise CNN-based autoencoder with a single-head transformer classifier for efficient EEG abnormality detection. The channelwise autoencoder co...

Identification of Epileptic Spasms (ESES) Phases Using EEG Signals: A Vision Transformer Approach

This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES ...

EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals...

CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information

Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitiv...

Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals

Time-series foundation models excel at tasks like forecasting across diverse data types by leveraging informative waveform representations. Wearable...

Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various bo...

LV-CadeNet: Long View Feature Convolution-Attention Fusion Encoder-Decoder Network for Clinical MEG Spike Detection

It is widely acknowledged that the epileptic foci can be pinpointed by source localizing interictal epileptic discharges (IEDs) via Magnetoencephalo...

A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis

Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or mis...

Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely d...

Investigation of in vitro neuronal activity processing using a CMOS-integrated ZrO2-based memristive crossbar

The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was stu...

CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare ...

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. D...

Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology ...

Hyperbolic embedding of brain networks can predict the surgery outcome in temporal lobe epilepsy

Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However...

Towards Brain Passage Retrieval -- An Investigation of EEG Query Representations

Information Retrieval (IR) systems primarily rely on users' ability to translate their internal information needs into (text) queries. However, this...

A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration

Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the devel...

STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs

Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorde...

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a ...

An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques

This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques...