[HTML][HTML] EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
Electroencephalographic (EEG) recordings generate an electrical map of the human brain
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
[HTML][HTML] Machine-learning-based diagnostics of EEG pathology
LAW Gemein, RT Schirrmeister, P Chrabąszcz… - NeuroImage, 2020 - Elsevier
Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
A pervasive approach to EEG‐based depression detection
H Cai, J Han, Y Chen, X Sha, Z Wang, B Hu… - …, 2018 - Wiley Online Library
Nowadays, depression is the world's major health concern and economic burden worldwide.
However, due to the limitations of current methods for depression diagnosis, a pervasive …
However, due to the limitations of current methods for depression diagnosis, a pervasive …
Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review
Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships
between measures of brain activity and of behavior or experimental design. In …
between measures of brain activity and of behavior or experimental design. In …
A deep convolutional neural network model for automated identification of abnormal EEG signals
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual
examination of these signals by experts is strenuous and time consuming. Hence, machine …
examination of these signals by experts is strenuous and time consuming. Hence, machine …
Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: recommendations of an expert panel
Abstract The Electrophysiology Professional Interest Area (EPIA) and Global Brain
Consortium endorsed recommendations on candidate electroencephalography (EEG) …
Consortium endorsed recommendations on candidate electroencephalography (EEG) …
[HTML][HTML] Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic …
J Maroco, D Silva, A Rodrigues, M Guerreiro… - BMC research …, 2011 - Springer
Background Dementia and cognitive impairment associated with aging are a major medical
and social concern. Neuropsychological testing is a key element in the diagnostic …
and social concern. Neuropsychological testing is a key element in the diagnostic …