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Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism.

Chang Li1, Bin Wang2, Silin Zhang3

  • 1Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China; Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei, 230009, China.

Computers in Biology and Medicine
|February 26, 2022
PubMed
Summary

This study introduces a novel multi-task learning method using capsule networks and attention mechanisms for electroencephalography (EEG) emotion recognition. The approach significantly improves accuracy by learning arousal, valence, and dominance simultaneously.

Keywords:
Capsule networkDeep learningElectroencephalogramEmotion recognitionMulti-task learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning (DL) shows promise for electroencephalography (EEG) based emotion recognition.
  • Current DL methods often use single-task learning, potentially missing cross-task information and requiring extensive retraining for new tasks.

Purpose of the Study:

  • To develop an efficient and robust EEG-based emotion recognition system.
  • To overcome limitations of single-task learning in EEG emotion recognition.

Main Methods:

  • Proposed a novel multi-task learning framework integrating capsule networks (CapsNet) and an attention mechanism.
  • CapsNet effectively captures relationships between EEG channels.
  • Attention mechanism optimizes feature extraction by weighting channel importance.

Main Results:

  • Achieved high average accuracies: 97.25% (arousal), 97.41% (valence), and 98.35% (dominance) on the DEAP dataset.
  • Attained average accuracies of 94.96% (arousal), 95.54% (valence), and 95.52% (dominance) on the DREAMER dataset.
  • Demonstrated significant efficiency and improved generalization compared to single-task approaches.

Conclusions:

  • The proposed multi-task learning method with CapsNet and attention mechanism is highly effective for EEG emotion recognition.
  • This integrated approach enhances accuracy and robustness by leveraging complementary task information.
  • The method offers a more efficient and powerful solution for understanding emotions from EEG data.