Seizures

F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation.

BACKGROUND: Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medically refractory epilepsy (MRE), especially when MRI is negative or equivocal. In this study, we developed a PET-guided diffusion tractography (PET/DTI) approach combining F-fluorodeoxyglucose PET (FDG-PET) and diffusion MRI to investigate white matter (...

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification.

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implement...

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network.

Emotion recognition plays an important role in the field of human-computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate...

Convolutional neural network for detection and classification of seizures in clinical data.

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate o...

Auditory attention tracking states in a cocktail party environment can be decoded by deep convolutional neural networks.

OBJECTIVE: A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features a...

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks.

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are benef...

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful mac...

Machine-learning-based diagnostics of EEG pathology.

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted featu...

Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach.

Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high vari...

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental...

Single-Trial EEG Responses Classified Using Latency Features.

Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are inc...

Emotion recognition with convolutional neural network and EEG-based EFDMs.

Electroencephalogram (EEG), as a direct response to brain activity, can be used to detect mental states and physical conditions. Among various EEG-bas...

Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.

Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features fo...

Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.

Prior research has shown that greater EEG alpha power (8-13 ​Hz) is characteristic of more creative individuals, and more creative task conditions. Th...

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learni...

EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia.

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sa...

Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?

PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is def...

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection a...

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong d...

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocia...