Seizures

Raster plots machine learning to predict the seizure liability of drugs and to identify drugs.

In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable o...

Scalp EEG-Based Pain Detection Using Convolutional Neural Network.

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This pa...

Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals.

Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms ...

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.

Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as bi...

Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.

Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore...

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative sc...

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.

Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signal...

Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation.

Cognitive workload recognition is pivotal to maintain the operator's health and prevent accidents in the human-robot interaction condition. So far, th...

A simulation study to investigate the use of concentric tube robots for epilepsy surgery.

PURPOSE: Patients with pharmacoresistant refractory epilepsy may require epilepsy surgery to prevent future seizure occurrences. Conventional surgery ...

A gradient-based automatic optimization CNN framework for EEG state recognition.

. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, i...

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.

BACKGROUND: Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual k...

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus incr...

Artificial intelligence for medical image analysis in epilepsy.

Given improvements in computing power, artificial intelligence (AI) with deep learning has emerged as the state-of-the art method for the analysis of ...

WeDea: A New EEG-Based Framework for Emotion Recognition.

With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiolog...

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We ...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive sur...

Deep learning of early brain imaging to predict post-arrest electroencephalography.

INTRODUCTION: Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associa...

Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.

Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly used fine motor task and is included in the NIH Comm...

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a war...

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been use...