Emotion recognition from multiband EEG signals using CapsNet
H Chao, L Dong, Y Liu, B Lu - Sensors, 2019 - mdpi.com
Emotion recognition based on multi-channel electroencephalograph (EEG) signals is
becoming increasingly attractive. However, the conventional methods ignore the spatial …
becoming increasingly attractive. However, the conventional methods ignore the spatial …
Video affective content analysis: A survey of state-of-the-art methods
Video affective content analysis has been an active research area in recent decades, since
emotion is an important component in the classification and retrieval of videos. Video …
emotion is an important component in the classification and retrieval of videos. Video …
Accelerating 3D convolutional neural network with channel bottleneck module for EEG-based emotion recognition
Deep learning-based emotion recognition using EEG has received increasing attention in
recent years. The existing studies on emotion recognition show great variability in their …
recent years. The existing studies on emotion recognition show great variability in their …
Combining inter-subject modeling with a subject-based data transformation to improve affect recognition from EEG signals
Existing correlations between features extracted from Electroencephalography (EEG)
signals and emotional aspects have motivated the development of a diversity of EEG-based …
signals and emotional aspects have motivated the development of a diversity of EEG-based …
Improved Deep Feature Learning by Synchronization Measurements for Multi‐Channel EEG Emotion Recognition
H Chao, L Dong, Y Liu, B Lu - Complexity, 2020 - Wiley Online Library
Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key
research area in the field of affective computing. Traditional methods extract EEG features …
research area in the field of affective computing. Traditional methods extract EEG features …
A hybrid hand-crafted and deep neural spatio-temporal EEG features clustering framework for precise emotional status recognition
Human emotions are variant with time, non-stationary, complex in nature, and are invoked
as a result of human reactions during our daily lives. Continuously detecting human …
as a result of human reactions during our daily lives. Continuously detecting human …
FCAN–XGBoost: a novel hybrid model for EEG emotion recognition
J Zong, X Xiong, J Zhou, Y Ji, D Zhou, Q Zhang - Sensors, 2023 - mdpi.com
In recent years, artificial intelligence (AI) technology has promoted the development of
electroencephalogram (EEG) emotion recognition. However, existing methods often …
electroencephalogram (EEG) emotion recognition. However, existing methods often …
EEG Emotion Recognition Based on Federated Learning Framework
C Xu, H Liu, W Qi - Electronics, 2022 - mdpi.com
Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming
increasingly attractive. However, the lack of large datasets and privacy concerns lead to …
increasingly attractive. However, the lack of large datasets and privacy concerns lead to …
The secret language of our body: Affect and personality recognition using physiological signals
J Wache - Proceedings of the 16th International Conference on …, 2014 - dl.acm.org
We present a novel framework for decoding individuals? emotional state and personality
traits based on physiological responses to affective movie clips. During watching 36 video …
traits based on physiological responses to affective movie clips. During watching 36 video …
Discrete cosine transform for MEG signal decoding
In this study, we propose the discrete cosine transform coefficients as a new and effective set
of features for recognizing patterns of brain activity in MEG recording. We claim that …
of features for recognizing patterns of brain activity in MEG recording. We claim that …