Seizures

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition.

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the ...

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natural language processing (NLP) algorithms to automatically extract key epilepsy outcome measures from clinic notes. In this study, we assessed the feasib...

TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.

Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy. We propose SeizFt, a robust sei...

Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.

Perception of social stimuli (faces and bodies) relies on "holistic" (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving in...

Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy.

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its pe...

MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.

Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based app...

Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis.

Robotic assistance has improved electrode implantation precision in stereoelectroencephalography (SEEG) for refractory epilepsy patients. We sought to...

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy.

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to locali...

Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.

Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented o...

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal...

Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model.

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically study how to explain EEG...

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation.

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep lea...

Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model.

The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used fo...

Self-Supervised EEG Emotion Recognition Models Based on CNN.

Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by research...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are esse...

Decoding study-independent mind-wandering from EEG using convolutional neural networks.

. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study...

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset.

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG ...

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures.

Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market pr...

Classification of coma/brain-death EEG dataset based on one-dimensional convolutional neural network.

Electroencephalography (EEG) evaluation is an important step in the clinical diagnosis of brain death during the standard clinical procedure. The proc...

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG.

Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. Howe...