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Automated Feature Extraction on AsMap for Emotion Classification Using EEG.

Md Zaved Iqubal Ahmed1, Nidul Sinha2, Souvik Phadikar2

  • 1Department of Computer Science & Engineering, National Institute of Technology, Silchar 788010, India.

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Summary

This study introduces a novel hybrid approach for emotion recognition using electroencephalography (EEG) signals. The method enhances classification accuracy by combining manual and deep learning-based feature extraction for affective computing.

Keywords:
arousalclassificationdeep learningelectroencephalogramemotionvalence

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition from electroencephalography (EEG) is crucial for affective computing.
  • Manual feature extraction from EEG signals often limits the performance of learning models.

Purpose of the Study:

  • To propose a hybrid feature extraction method combining manual and automatic techniques for improved EEG-based emotion recognition.
  • To evaluate the effectiveness of the proposed method against existing feature extraction techniques.

Main Methods:

  • A 2D vector, AsMap, is created from differential entropy features of EEG signals to capture brain region asymmetry.
  • A convolutional neural network (CNN) is employed for automatic feature extraction from AsMaps.
  • The proposed method is compared with differential entropy, relative asymmetry, differential asymmetry, and differential caudality.

Main Results:

  • The proposed hybrid feature extraction method significantly outperforms traditional methods in emotion classification.
  • The highest classification accuracy achieved was 97.10% on a three-class problem using the SJTU emotion EEG dataset.
  • The study also analyzed the influence of window size on classification performance.

Conclusions:

  • The hybrid AsMap-based feature extraction method offers a superior approach for EEG-based emotion recognition.
  • This technique holds promise for advancing the field of affective computing through more accurate emotion detection.