AIMC Topic: Electroencephalography

Clear Filters Showing 821 to 830 of 2295 articles

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

PloS one
Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environ...

3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EE...

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

Epilepsy & behavior : E&B
OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox-Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection ...

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Computer methods in biomechanics and biomedical engineering
Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant r...

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.

PloS one
Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impac...

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea.

Computers in biology and medicine
Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep...

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

Applied neuropsychology. Adult
"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidi...

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.

Sensors (Basel, Switzerland)
Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activi...

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks.

Medical engineering & physics
Modern deep neural network training is based on mini-batch stochastic gradient optimization. While using extensive mini-batches improves the computational parallelism, the small batch training proved that it delivers improved generalization performan...

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.