Seizures

A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep learning is capable of automatically extracting the significant features and make classifications, the authors proposed a deep learning based model u...

Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from sca...

Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG.

. Auditory attention decoding (AAD) determines which speaker the listener is focusing on by analyzing his/her EEG. Convolutional neural network (CNN) ...

Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence.

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, t...

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective ...

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance impr...

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optima...

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for ...

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular prot...

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learni...

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, th...

Three simple steps to improve the interpretability of EEG-SVM studies.

Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variet...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings co...

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals w...

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and ...

ResNet-50 based technique for EEG image characterization due to varying environmental stimuli.

BACKGROUND AND OBJECTIVE: Emotion is an important factor affecting a person's physical and mental health, but there are few ways to detect a patient's...

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Co...

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events ...

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a sign...

On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.

Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep l...