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Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition.

Danilo Avola1, Luigi Cinque1, Angelo Di Mambro1

  • 1Department of Computer Science, Sapienza University of Rome, Via Salaria 113, Rome 00198, Italy.

International Journal of Neural Systems
|March 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the Empátheia system for emotion recognition using ElectroEncephaloGraphy (EEG) data. It encodes EEG signals into compact images, significantly reducing dataset size while maintaining high classification performance.

Keywords:
CNNEEGEmotion recognitionGRULSTMPRISMIN frameworkViTimage encodingmulti-branch architecturespatio-temporal atlases

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition is crucial for human-human and human-computer interaction.
  • ElectroEncephaloGraphy (EEG) data analysis is effective for emotion discrimination but generates large datasets.
  • Managing, transmitting, and utilizing cumbersome EEG datasets poses challenges for advanced applications.

Purpose of the Study:

  • To develop a novel system, Empátheia, for efficient emotion recognition from EEG data.
  • To explore a new EEG data representation by encoding signals into compact images.
  • To design a classification architecture capable of capturing spatial and temporal aspects of emotions from image-based EEG representations.

Main Methods:

  • The Empátheia system utilizes the PRISMIN framework to extract spatio-temporal image encodings (atlases) from EEG signals.
  • These atlases provide a compact representation of the original EEG data.
  • The Empátheia architecture, featuring convolutional, recurrent, and transformer models, classifies these atlases to recognize emotions.

Main Results:

  • The proposed system achieved significant data size reduction for EEG signals.
  • High performance in emotion classification was retained despite the compression.
  • Experiments on the SEED dataset demonstrated the effectiveness of the image-based encoding approach.

Conclusions:

  • The Empátheia system offers an effective method for emotion recognition from EEG data.
  • Encoding EEG signals into images provides a compact and efficient data representation.
  • This approach opens new possibilities for data representation in EEG-based emotion recognition research.