Seizures

A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples. Building robust classifiers for the PU problem is very challenging, especially for...

Application of identity vectors for EEG classification.

BACKGROUND: Finding an optimal EEG subject verification algorithm is a long standing goal within the EEG community. For every advancement made, another feature set, classifier, or dataset is often introduced; tracking improvements in classification without a consistent benchmark, such as a classifier-feature pairing tested on a publicly available dataset, makes it difficult to understand how and w...

A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL).

Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to rec...

Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion Recognition.

The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applicatio...

Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.

OBJECTIVE: We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep lea...

Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification.

Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked s...

A Novel Method of Segmentation and Classification for Meditation in Health Care Systems.

Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequence...

A Wearable Multi-Modal Bio-Sensing System Towards Real-World Applications.

Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality amo...

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot...

Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine.

AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the ...

A hierarchical multimodal system for motion analysis in patients with epilepsy.

During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying...

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity.

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outc...

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures ...

Epilepsy classification using optimized artificial neural network.

OBJECTIVES: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the tim...

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution.

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this ...

Developing a Three- to Six-State EEG-Based Brain-Computer Interface for a Virtual Robotic Manipulator Control.

OBJECTIVE: We develop an electroencephalography (EEG)-based noninvasive brain-computer interface (BCI) system having short training time (15 min) that...

Robot-guided pediatric stereoelectroencephalography: single-institution experience.

OBJECTIVEStereoelectroencephalography (SEEG) has increased in popularity for localization of epileptogenic zones in drug-resistant epilepsy because sa...

Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery.

OBJECTIVE: We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure...

High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence.

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated ...

Modeling brain dynamic state changes with adaptive mixture independent component analysis.

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting t...