EEG based emotion recognition: A tutorial and review
Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …
concept in Artificial Intelligence and holds great potential in emotional health care, human …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
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 …
An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification
G Dai, J Zhou, J Huang, N Wang - Journal of neural engineering, 2020 - iopscience.iop.org
Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …
used in healthcare applications such as mobile assistive robots and post-stroke …
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): standardized processing software for developmental and high-artifact data
LJ Gabard-Durnam, AS Mendez Leal… - Frontiers in …, 2018 - frontiersin.org
Electroenchephalography (EEG) recordings collected with developmental populations
present particular challenges from a data processing perspective. These EEGs have a high …
present particular challenges from a data processing perspective. These EEGs have a high …
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension
The identification of seizure activities in non-stationary electroencephalography (EEG) is a
challenging task. The seizure detection by human inspection of EEG signals is prone to …
challenging task. The seizure detection by human inspection of EEG signals is prone to …
Emotion recognition from multi-channel EEG data through convolutional recurrent neural network
Automatic emotion recognition based on multi-channel neurophysiological signals, as a
challenging pattern recognition task, is becoming an important computer-aided method for …
challenging pattern recognition task, is becoming an important computer-aided method for …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
This paper describes a discrete wavelet transform-based feature extraction scheme for the
classification of EEG signals. In this scheme, the discrete wavelet transform is applied on …
classification of EEG signals. In this scheme, the discrete wavelet transform is applied on …