Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals
Objective: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
[HTML][HTML] Bringing nature into hospital architecture: Machine learning-based EEG analysis of the biophilia effect in virtual reality
D Jung, DI Kim, N Kim - Journal of Environmental Psychology, 2023 - Elsevier
In recent years, there has been a growing interest in investigating the influence of biophilic
design on occupants' psychological comfort and well-being in the built environment …
design on occupants' psychological comfort and well-being in the built environment …
Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping 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 …
Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG
Abstract Major Depressive Disorder (MDD) is one of the leading causes of disability
worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) …
worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) …
A review of methods of diagnosis and complexity analysis of Alzheimer's disease using EEG signals
M Ouchani, S Gharibzadeh… - BioMed Research …, 2021 - Wiley Online Library
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis,
identifying and comparing key steps of EEG‐based Alzheimer's disease (AD) detection …
identifying and comparing key steps of EEG‐based Alzheimer's disease (AD) detection …
Advanced bioelectrical signal processing methods: Past, present and future approach—Part II: Brain signals
As it was mentioned in the previous part of this work (Part I)—the advanced signal
processing methods are one of the quickest and the most dynamically developing scientific …
processing methods are one of the quickest and the most dynamically developing scientific …
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …