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Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps.

Georgia Sovatzidi1, Dimitris K Iakovidis1

  • 1Dept of Computer Science and Biomedical Informatics, Univ. of Thessaly, Greece.

Studies in Health Technology and Informatics
|May 19, 2023
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Summary
This summary is machine-generated.

This study introduces an auto-constructed Fuzzy Cognitive Map (FCM) for interpretable emotion recognition using electroencephalography (EEG) signals. The novel model automatically identifies brain region and emotion relationships, offering trustworthy and understandable results.

Keywords:
Electroencephalography (EEG)Emotion RecognitionFuzzy Cognitive Map (FCM)Fuzzy LogicInterpretability

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • The human brain's complexity necessitates advanced methods for studying its functions.
  • Electroencephalography (EEG) records brain electrical activity via scalp electrodes.
  • Interpretable emotion recognition from EEG signals remains a challenge.

Purpose of the Study:

  • To develop an auto-constructed Fuzzy Cognitive Map (FCM) model for interpretable emotion recognition using EEG signals.
  • To automatically detect cause-and-effect relationships between brain regions and emotions.
  • To provide a trustworthy and easily implementable solution for EEG-based emotion analysis.

Main Methods:

  • Utilized an auto-constructed Fuzzy Cognitive Map (FCM) model.
  • Applied the model to Electroencephalography (EEG) signals recorded during movie watching.
  • Investigated cause-and-effect relationships among brain regions and induced emotions.
  • Employed a publicly available dataset for validation.

Main Results:

  • The developed FCM model successfully identified interdependencies between brain activity and emotions.
  • The model demonstrated interpretability, allowing users to understand the detected relationships.
  • The auto-construction feature simplified the model's implementation.
  • Effectiveness was validated against baseline and state-of-the-art methods.

Conclusions:

  • The auto-constructed FCM offers a novel and effective approach to interpretable emotion recognition from EEG.
  • This method enhances user trust through transparent and understandable results.
  • The model shows promise for advancing brain-computer interfaces and affective computing.