Seizures

Deep Learning of Motor Imagery EEG Classification for Brain-Computer Interface Illiterate Subject.

BCI illiterate subject is defined as the subject who cannot achieve accuracy higher than 70%. BCI illiterate subject cannot produce stronger contralateral ERD/ERS activity, thus most of the frequency band-based algorithms cannot obtain higher accuracy. Deep learning with convolutional neural networks (CNN) has revolutionized in many recent studies to learn features and classify different types of ...

Classification of Movement Direction From Electroencephalogram During Working Memory Time.

When humans perform cognitive tasks, it is necessary to hold information temporarily. This is done by a brain function called working memory (WM). Since WM is active during the whole time range from stimulus presentation to task execution, onset detection is unnecessary, in contrast to readiness potentials for movement. Therefore, it is possible to realize application in a brain-computer interface...

Probabilistic prediction of Epileptic Seizures using SVM.

In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) ...

Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning.

Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods...

3D Convolutional Neural Networks for Event-Related Potential detection.

Deep learning techniques have recently been successful in the classification of brain evoked responses for multiple applications, including brain-mach...

Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children.

Dyslexia is a specific learning difficulty associated with brain capability in processing numbers and letters. Analysis of Electroencephalogram (EEG) ...

EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia.

This paper presents the design of a machine learning-based classifier for the differentiation between Schizophrenia patients and healthy controls usin...

Automatic detection of High Frequency Oscillations (80-500Hz) based on Convolutional Neural Network in Human Intracerebral Electroencephalogram.

Recently, high-frequency oscillations (HFOs) of range 80-500 Hz in electroencephalogram (EEG) recordings of epilepsy patients are considered as a reli...

EEG-Based Classification of Olfactory Response to Pleasant Stimuli.

Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies ha...

Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System.

Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is ...

Tensor Discriminant Analysis for MI-EEG Signal Classification Using Convolutional Neural Network.

Motor Imagery (MI) is a typical paradigm for Brain-Computer Interface (BCI) system. In this paper, we propose a new framework by introducing a tensor-...

Movement related activity in the μ band of the human EEG during a robot-based proprioceptive task.

Innovative research in the fields of prosthetic, neurorehabilitation, motor control and human physiology has been focusing on the study of propriocept...

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks.

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordi...

Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.

Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and l...

A Multi-View Deep Learning Framework for EEG Seizure Detection.

The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of e...

Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG.

BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy an...

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chron...

A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG.

Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interacti...

EEG Processing to Discriminate Transitive-Intransitive Motor Imagery Tasks: Preliminary Evidences using Support Vector Machines.

It is known that brain dynamics significantly changes during motor imagery tasks of upper limb involving different kind of interactions with an object...

Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network.

Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild...