[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
Machine learning with applications in breast cancer diagnosis and prognosis
Breast cancer (BC) is one of the most common cancers among women worldwide,
representing the majority of new cancer cases and cancer-related deaths according to …
representing the majority of new cancer cases and cancer-related deaths according to …
Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism
Deep learning (DL) technologies have recently shown great potential in emotion recognition
based on electroencephalography (EEG). However, existing DL-based EEG emotion …
based on electroencephalography (EEG). However, existing DL-based EEG emotion …
Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time‐Frequency Domains
AS Al-Fahoum, AA Al-Fraihat - … Scholarly Research Notices, 2014 - Wiley Online Library
Technically, a feature represents a distinguishing property, a recognizable measurement,
and a functional component obtained from a section of a pattern. Extracted features are …
and a functional component obtained from a section of a pattern. Extracted features are …
Integrating metaheuristics and artificial neural networks for improved stock price prediction
Stock market price is one of the most important indicators of a country's economic growth.
That's why determining the exact movements of stock market price is considerably regarded …
That's why determining the exact movements of stock market price is considerably regarded …
Efficient deep neural networks for classification of Alzheimer's disease and mild cognitive impairment from scalp EEG recordings
The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
We introduced a multilayer perceptron neural network (MLPNN) based classification model
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
Wireless ear EEG to monitor drowsiness
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and
drivers. While existing in-cabin sensors may provide alerts, wearables can enable …
drivers. While existing in-cabin sensors may provide alerts, wearables can enable …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
MT Sadiq, X Yu, Z Yuan - Expert Systems with Applications, 2021 - Elsevier
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
EEG signal classification using wavelet feature extraction and a mixture of expert model
A Subasi - Expert Systems with Applications, 2007 - Elsevier
Mixture of experts (ME) is modular neural network architecture for supervised learning. A
double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME …
double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME …