Seizures

Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroenceph...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and toge...

Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism.

Due to the complexity of human emotions, there are some similarities between different emotion featu...

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.

OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharg...

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals.

Emotion is interpreted as a psycho-physiological process, and it is associated with personality, beh...

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without dir...

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.

Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In th...

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We in...

Neuromagnetic high frequency spikes are a new and noninvasive biomarker for localization of epileptogenic zones.

OBJECTIVE: One barrier hindering high frequency brain signals (HFBS, >80 Hz) from wide clinical appl...

The Function of Color and Structure Based on EEG Features in Landscape Recognition.

Both color and structure make important contributions to human visual perception, as well as the eva...

Interpreting deep learning models for epileptic seizure detection on EEG signals.

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-ligence-based...

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition.

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases an...

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emo...

Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG.

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical ...

Edge deep learning for neural implants: a case study of seizure detection and prediction.

Implanted devices providing real-time neural activity classification and control are increasingly us...

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is k...

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzhei...

Machine learning for detection of interictal epileptiform discharges.

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy...

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals...

Bring the model to the data: The Deep Learning Epilepsy Detection Challenge.

Deep learning for robust detection of interictal epileptiform discharges.

Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epilepti...