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 …
[HTML][HTML] A systematic survey on multimodal emotion recognition using learning algorithms
Emotion recognition is the process to detect, evaluate, interpret, and respond to people's
emotional states and emotions, ranging from happiness to fear to humiliation. The COVID-19 …
emotional states and emotions, ranging from happiness to fear to humiliation. The COVID-19 …
Brain emotion perception inspired EEG emotion recognition with deep reinforcement learning
Inspired by the well-known Papez circuit theory and neuroscience knowledge of
reinforcement learning, a double dueling deep network (DQN) is built incorporating the …
reinforcement learning, a double dueling deep network (DQN) is built incorporating the …
Machine-learning-based emotion recognition system using EEG signals
R Alhalaseh, S Alasasfeh - Computers, 2020 - mdpi.com
Many scientific studies have been concerned with building an automatic system to recognize
emotions, and building such systems usually relies on brain signals. These studies have …
emotions, and building such systems usually relies on brain signals. These studies have …
Prediction of biomedical signals using deep learning techniques
K Kalaivani, PR Kshirsagarr… - Journal of Intelligent …, 2023 - content.iospress.com
The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG)
are all very useful diagnostic techniques. The widespread availability of mobile devices plus …
are all very useful diagnostic techniques. The widespread availability of mobile devices plus …
An EEG data processing approach for emotion recognition
As the most direct way to measure the true emotional states of humans, EEG-based emotion
recognition has been widely used in affective computing applications. In this paper, we aim …
recognition has been widely used in affective computing applications. In this paper, we aim …
A fuzzy ensemble-based deep learning model for EEG-based emotion recognition
Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …
The major challenges involved in the task are extracting meaningful features from the …
EEG-based emotion recognition with deep convolutional neural networks
The emotional state of people plays a key role in physiological and behavioral human
interaction. Emotional state analysis entails many fields such as neuroscience, cognitive …
interaction. Emotional state analysis entails many fields such as neuroscience, cognitive …
Real-time emotion classification using eeg data stream in e-learning contexts
In face-to-face and online learning, emotions and emotional intelligence have an influence
and play an essential role. Learners' emotions are crucial for e-learning system because …
and play an essential role. Learners' emotions are crucial for e-learning system because …
Siam-GCAN: A Siamese graph convolutional attention network for EEG emotion recognition
H Zeng, Q Wu, Y Jin, H Zheng, M Li… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The graph convolutional network (GCN) shows effective performance in
electroencephalogram (EEG) emotion recognition owing to the ability to capture brain …
electroencephalogram (EEG) emotion recognition owing to the ability to capture brain …