Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …
and mimics human emotions. Thanks to the continued advancement of portable non …
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …
made it possible to endow machines/computers with the ability of emotion understanding …
An automated system for epilepsy detection using EEG brain signals based on deep learning approach
Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a
large number of people all over the world. For its detection, encephalography (EEG) is a …
large number of people all over the world. For its detection, encephalography (EEG) is a …
A comparative analysis of signal processing and classification methods for different applications based on EEG signals
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …
currents that are generated due to the synchronized activity by a group of specialized …
Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection …
This study proposes a new model which is fully specified for automated seizure onset
detection and seizure onset prediction based on electroencephalography (EEG) …
detection and seizure onset prediction based on electroencephalography (EEG) …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
[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 …
Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional long short-term memory (Bi-LSTM)
Emotions are an essential part of daily human communication. The emotional states and
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
Application of entropies for automated diagnosis of epilepsy using EEG signals: A review
Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …