Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
Complex Pearson correlation coefficient for EEG connectivity analysis
In the background of all human thinking—acting and reacting are sets of connections
between different neurons or groups of neurons. We studied and evaluated these …
between different neurons or groups of neurons. We studied and evaluated these …
Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic map** study
Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders
because it provides brain biomarkers. However, only highly trained doctors can interpret …
because it provides brain biomarkers. However, only highly trained doctors can interpret …
Group identity modulates bidding behavior in repeated lottery contest: neural signatures from event-related potentials and electroencephalography oscillations
S Hao, P Jiali, Z **aomin, W **aoqin, L Lina… - Frontiers in …, 2023 - frontiersin.org
A contest usually involves expenditures, termed “overbidding,” exceeding the theoretical
Nash equilibrium. A considerable number of studies have shown that group identity can …
Nash equilibrium. A considerable number of studies have shown that group identity can …
An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG
Objective. This study aims to design and implement the first deep learning (DL) model to
classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state …
classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state …
EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review
In recent decades, the automatic recognition and interpretation of brain waves acquired by
electroencephalographic (EEG) technologies have undergone remarkable growth, leading …
electroencephalographic (EEG) technologies have undergone remarkable growth, leading …
ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental behavioral disorder.
It is common in children, can be carried over into adulthood, and is associated with …
It is common in children, can be carried over into adulthood, and is associated with …
[HTML][HTML] HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings
Lower-density Electroencephalography (EEG) recordings (from 1 to approximately 32
electrodes) are widely-used in research and clinical practice and enable scalable brain …
electrodes) are widely-used in research and clinical practice and enable scalable brain …
Eeg signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review
EEG is a common and safe test that uses small electrodes to record electrical signals from
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …