The learned class-specific graph-based representations can act as sub-dictionaries and be utilized for the task of emotion classification. Applying the proposed method on an electroencephalogram (EEG) emotion recognition dataset indicates the superiority of our framework over other state-of-the-art methods.
In this work, we bring to bear graph signal processing (GSP) tech- niques to tackle the problem of automatic emotion recognition using brain signals. GSP is an ...
Equivalently, for fixed low bandwidth we expect the signal power to be largest when projected onto the GFT basis constructed from Lc . EEG-based emotion ...
Jul 19, 2024 · EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy ...
Nov 29, 2024 · Three notable deep learning architectures for EEG-based emotion recognition are ShallowFBCSPNet, Deep4Net, and EEGNetv4. Each one offers ...
People also ask
What is emotion recognition using EEG signal?
What is graph signal processing?
How do you classify EEG signals?
Can you measure emotions with EEG?
Aug 13, 2024 · EEG-based emotion recognition leverages the close relationship between EEG signals and brain activity to accurately identify emotional states.
We propose graph signal representation of EEG signals corresponding to each task, where each channel corresponds to the nodes of the graph representing the ...
Jan 15, 2024 · EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion ...
This study introduces an advanced machine learning model aimed at improving EEG-based emotion classification by fully utilizing the spatial and temporal ...
In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel ...