Seizures

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorde...

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard database for emotion analysis using physiological EEG signals. Once raw signals are pre-processed in an EEGLAB, we perform feature extraction using Matrix L...

Statistical algorithms for emotion classification via functional connectivity.

Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input t...

Percept-related EEG classification using machine learning approach and features of functional brain connectivity.

Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature ...

Epileptic seizure detection using EEG signals and extreme gradient boosting.

The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple Univers...

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empi...

Predicting task-general mind-wandering with EEG.

Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive becaus...

Characterization of SSMVEP-based EEG signals using multiplex limited penetrable horizontal visibility graph.

The steady state motion visual evoked potential (SSMVEP)-based brain computer interface (BCI), which incorporates the motion perception capabilities o...

Estimation of brain connectivity through Artificial Neural Networks.

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved...

Signal2Image Modules in Deep Neural Networks for EEG Classification.

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such a...

EEG-Based Emotion Recognition with Similarity Learning Network.

Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evalua...

Classification of Perceived Human Stress using Physiological Signals.

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These inclu...

2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep.

We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable progn...

Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction.

Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal ...

Epileptic States Recognition Using Transfer Learning.

Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy...

A convolutional neural network based framework for classification of seizure types.

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures ba...

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an important research direction. A novel automatic epilep...

Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.

Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully r...

Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks.

The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton reh...

Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks.

In consideration of the complexity of recording electroencephalography(EEG), some researchers are trying to find new features of emotion recognition. ...