Seizures

Insights of 3D Input CNN in EEG-based Emotion Recognition.

Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applications. In this paper, a reproducible framework is proposed for classifying human emotions based on EEG signals. The framework consists of extracting frequency-dependent features from raw EEG signals to form a three-dimensional EEG image which is clas...

Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning.

Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae that emerge days to years later, including cognitive decline and seizures. Some TBI patients develop posttraumatic epilepsy (PTE), or recurrent and u...

Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity.

Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorith...

A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection.

In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalogr...

Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data.

Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are charact...

Towards Deeper Neural Networks for Neonatal Seizure Detection.

Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work prop...

Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns.

Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetrically weighted local binary patterns (SLBP)-based...

Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features.

The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscienc...

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals.

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification...

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) pr...

Classification of Epileptic Seizure From EEG Signal Based on Hilbert Vibration Decomposition and Deep Learning.

A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from...

Seizure Type Classification Using EEG Based on Gramian Angular Field Transformation and Deep Learning.

classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while mos...

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This ca...

Acoustic Based Footstep Detection in Pervasive Healthcare.

Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older ad...

EEG-Based Emotion Recognition for Modulating Social-Aware Robot Navigation.

Companion robots play an important role to accompany humans and provide emotional support, such as reducing human social isolation and loneliness. Bas...

EEG-based Emotion Recognition Using Graph Convolutional Network with Learnable Electrode Relations.

Emotion recognition based on electroencephalography (EEG) plays a pivotal role in the field of affective computing, and graph convolutional neural net...

Reduction of the ERP Measurement Time by a Weighted Averaging Using Deep Learning.

In clinical examination, event-related potentials (ERPs) are estimated by averaging across multiple responses, which suppresses background EEG. Howeve...

Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.

Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wear...

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may d...

[An anesthesia depth computing method study based on wavelet transform and artificial neural network].

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia dep...