Emotion recognition from multichannel EEG signals using K-nearest neighbor classification
M Li, H Xu, X Liu, S Lu - Technology and health care, 2018 - content.iospress.com
BACKGROUND: Many studies have been done on the emotion recognition based on multi-
channel electroencephalogram (EEG) signals. OBJECTIVE: This paper explores the …
channel electroencephalogram (EEG) signals. OBJECTIVE: This paper explores the …
SparseDGCNN: Recognizing emotion from multichannel EEG signals
Emotion recognition from EEG signals has attracted much attention in affective computing.
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning
Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic
emotional state, resulting in more reliable, natural, and meaningful human-computer …
emotional state, resulting in more reliable, natural, and meaningful human-computer …
Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine
When dealing with patients with psychological or emotional symptoms, medical practitioners
are often faced with the problem of objectively recognizing their patients' emotional state. In …
are often faced with the problem of objectively recognizing their patients' emotional state. In …
Support matrix machines
In many classification problems such as electroencephalogram (EEG) classification and
image classification, the input features are naturally represented as matrices rather than …
image classification, the input features are naturally represented as matrices rather than …
Core-brain-network-based multilayer convolutional neural network for emotion recognition
Z Gao, R Li, C Ma, L Rui, X Sun - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, we propose a method for emotion classification based on multilayer
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …
[HTML][HTML] A comparison of emotion recognition system using electrocardiogram (ECG) and photoplethysmogram (PPG)
Abstract Electrocardiogram (ECG) and Photoplethysmogram (PPG) are derived from
electrical signals of the heart activities and frequently used to diagnose and monitor …
electrical signals of the heart activities and frequently used to diagnose and monitor …
Broad learning system for semi-supervised learning
Z Liu, S Huang, W Jin, Y Mu - Neurocomputing, 2021 - Elsevier
As an emerging technique for supervised learning, broad learning system (BLS) has been
proved to have many advantages such as fast learning speed, good generalization, etc …
proved to have many advantages such as fast learning speed, good generalization, etc …
Classification of EEG-based emotion for BCI applications
M Mohammadpour, SMR Hashemi… - 2017 Artificial …, 2017 - ieeexplore.ieee.org
Emotion plays an important role in human daily life and is a significant feature for interaction
among people. Due to having adaptive role, it motivate human to respond stimuli in their …
among people. Due to having adaptive role, it motivate human to respond stimuli in their …
Cross-subject and cross-gender emotion classification from EEG
This paper aims to explore whether different persons share similar patterns for EEG
changing with emotions and examine the performance of cross-subject and crossgender …
changing with emotions and examine the performance of cross-subject and crossgender …