Latest AI and machine learning research in seizures for healthcare professionals.
Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the...
As the number of patients with Alzheimer's disease (AD) increases, the effort needed to care for the...
Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ ...
Absence epilepsy, characterized by transient loss of awareness and bilaterally synchronous 2-4 Hz sp...
Seizure detection is a routine process in epilepsy units requiring manual intervention of well-train...
The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium im...
Convolutional neural networks (CNN) have demonstrated state-of-the-art classification results in ima...
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could deco...
AIM: To explore the difference between robot assisted (RA) and stereotactic frame based (SF) stereoe...
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous effort...
Trial-by-trial texture classification analysis and identifying salient texture related EEG features ...
Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with lo...
To have an objective depression diagnosis, numerous studies based on machine learning and deep learn...
Deep learning has achieved great success in areas such as computer vision and natural language proce...
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in curr...
INTRODUCTION: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonli...
Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining po...
Clinical depression is a neurological disorder that can be identified by analyzing the Electroenceph...
To isolate brain activity that may reflect effective cognitive processes during the study phase of a...
OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) sign...
OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads ...