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Labeling Emotion

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...

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TC-Net: A Transformer Capsule Network for EEG-based emotion recognition.

Yi Wei¹, Yu Liu², Chang Li¹

¹Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China.

Computers in Biology and Medicine

|December 26, 2022

Summary

A new Transformer Capsule Network (TC-Net) enhances Electroencephalogram (EEG) emotion recognition by capturing global context, achieving state-of-the-art results in subject-dependent scenarios.

Keywords:

Capsule network Electroencephalogram (EEG)Emotion recognition Transformer

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Area of Science:

Neuroscience and Artificial Intelligence
Brain-Computer Interfaces
Machine Learning for Affective Computing

Background:

Convolutional Neural Networks (CNNs) dominate Electroencephalogram (EEG) based emotion recognition but struggle with global contextual information.
Limitations of CNNs include difficulty in capturing temporal, frequency, intra-channel, and inter-channel dependencies.
There is a need for advanced deep learning models to overcome these limitations in EEG emotion recognition.

Purpose of the Study:

To propose a novel Transformer Capsule Network (TC-Net) for improved EEG-based emotion recognition.
To address the limitations of CNNs in capturing global contextual information from EEG signals.
To enhance the accuracy and robustness of emotion state classification using EEG data.

Main Methods:

Developed a TC-Net comprising an EEG Transformer module for feature extraction and an Emotion Capsule module for feature refinement and classification.
Utilized a Transformer block within the EEG Transformer module to capture global features across EEG windows.
Introduced a novel EEG-PatchMerging (EEG-PM) strategy for enhanced local feature extraction and employed capsule networks to model spatial relationships.

Main Results:

Achieved state-of-the-art performance in subject-dependent emotion recognition on DEAP and DREAMER datasets.
Attained average accuracies of 98.76% (valence), 98.81% (arousal), and 98.82% (dominance) on the DEAP dataset.
Demonstrated high effectiveness in multi-state emotion recognition tasks using VA and VAD models.

Conclusions:

The proposed TC-Net effectively captures global contextual information, outperforming traditional CNNs in EEG emotion recognition.
TC-Net shows significant promise for accurate emotion state classification in subject-dependent settings.
Further research is needed to address the model's limitations in cross-subject recognition tasks.