AIMC Topic: Electroencephalography

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Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of ...

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Medical & biological engineering & computing
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG s...

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for pati...

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization.

Biomedical physics & engineering express
EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the...

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG.

Journal of neural engineering
. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.. EEG recordings of 17 healthy controls...

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Sensors (Basel, Switzerland)
Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is hig...

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.

Journal of medical Internet research
BACKGROUND: Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch.

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification d...

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

IEEE journal of biomedical and health informatics
Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the eff...

Cognitive Depression Detection Cyber-Medical System Based on EEG Analysis and Deep Learning Approaches.

IEEE journal of biomedical and health informatics
Long-term depression and negative emotional cycles affect life quality and work productivity. However, depression is not easy to detect, with current methods mostly relying on scales that make it impossible to quickly and directly measure the severit...