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Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG

Miguel Arevalillo-Herráez1, Maximo Cobos2, Sandra Roger2

  • 1Departament d'Informàtica, Universitat de València, Avda. de la Universidad, s/n, 46100-Burjasot, Spain. miguel.arevalillo@uv.es.

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Summary
This summary is machine-generated.

Subject

Keywords:
EEGarousal detectiondata transformationnormalizationvalence detection

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Area of Science:

  • Neuroscience and Affective Computing

Background:

  • Electroencephalography (EEG) signals correlate with emotions, leading to various affect detection methods.
  • Current methods include intra-subject (user-specific) and inter-subject (generalized) approaches.
  • Intra-subject methods face data limitations, while inter-subject methods neglect individual differences.

Purpose of the Study:

  • To analyze the performance of intra-subject and inter-subject affect detection models.
  • To investigate the influence of individual subjects on EEG signals compared to emotional content.
  • To propose a novel method for integrating individual traits into inter-subject models.

Main Methods:

  • Analysis of three public EEG repositories.
  • Comparative study of intra-subject and inter-subject modeling techniques.
  • Development of a data transformation to incorporate subject-specific features.

Main Results:

  • Subject-specific influence on EEG signals is significantly greater than emotion-specific influence.
  • Inter-subject models alone are insufficient due to high individual variability.
  • The proposed data transformation enhances classification accuracy by accounting for individual traits.

Conclusions:

  • Subject-specific modeling is crucial for accurate EEG-based affect detection.
  • A data transformation method effectively integrates individual characteristics into generalized models.
  • This approach improves the robustness and applicability of inter-subject affect detection systems.