Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

AA Ein Shoka, MH Alkinani, AS El-Sherbeny… - Brain Informatics, 2021 - Springer
Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure
using several methods such as neurological examination, blood tests, computerized …

A survey of brain network analysis by electroencephalographic signals

C Luo, F Li, P Li, C Yi, C Li, Q Tao, X Zhang, Y Si… - Cognitive …, 2022 - Springer
Brain network analysis is one efficient tool in exploring human brain diseases and can
differentiate the alterations from comparative networks. The alterations account for time …

An efficient deep learning paradigm for deceit identification test on EEG signals

DR Edla, S Dodia, A Bablani, V Kuppili - ACM Transactions on …, 2021 - dl.acm.org
Brain-Computer Interface is the collaboration of the human brain and a device that controls
the actions of a human using brain signals. Applications of brain-computer interface vary …

A Frequency-Domain Pattern Recognition Model for Motor Imagery-Based Brain-Computer Interface

ZT Al-Qaysi, MS Suzani… - … Data Science and …, 2024 - mesopotamian.press
Brain-computer interface (BCI) is an appropriate technique for totally paralyzed people with
a healthy brain. BCI based motor imagery (MI) is a common approach and widely used in …

Fast seizure detection from eeg using machine learning

AAE Shoka, MM Dessouky… - … Africa Conference on …, 2019 - ieeexplore.ieee.org
A seizure is a sudden, uncontrolled electrical disturbance in the brain. It can cause changes
in epileptic patient's behavior, developments or emotions, and in levels of consciousness …

EEG Feature Engineering Methods-A Comprehensive Review

RJ Martin - Multimedia Research, 2022 - publisher.resbee.org
Today, the primary topic of discussion in the signal processing domain is the analysis of non-
stationary and nonlinear signal data. The use of biomedical equipment generates enormous …

Energy-Efficient Electroencephalogram Signal Classification with Artificial Neural Networks and Fast Fourier Transform

G Keerthiga, S Rishika, P Sindhu - 2023 2nd International …, 2023 - ieeexplore.ieee.org
In the realm of brain-computer interfaces (BCIs), the demand for energy-efficient algorithms
to accurately classify electroencephalogram (EEG) signals has surged. This research …

A Study of Non-Gaussian Properties in Emotional EEG in Stroke Using Higher-Order Statistics

CW Yean, M Murugappan, MI Omar… - Advances in Electrical …, 2020 - Springer
The stroke patients often suffered from emotional disturbances, and this leads to perceive
emotions differently than normal control subjects; the emotional impairment of the stroke …

[PDF][PDF] Rapid Seizure Classification Using Feature Extraction and Channel Selection

AAE Shoka, MM Dessouky, AS El-Sherbeny… - algorithms - academia.edu
The Seizure is an abnormal electrical activity in the brain; it can be diagnosed by a
neurologist and could be classified using recorded data. Medical data, such as EEG signal …

Robustified principal component analysis for feature selection in EEG signal classification

RJ Martin - International Journal of Systematic Innovation, 2023 - ijosi.org
Feature engineering is an important step in data analysis especially for machine learning
applications. Wide range of feature selection methods are being used in EEG signal …