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EEG-based Emotion Detection Using Unsupervised Transfer Learning | IEEE Conference Publication | IEEE Xplore

EEG-based Emotion Detection Using Unsupervised Transfer Learning


Abstract:

Emotion classification using EEG signal processing has the potential of significantly improving the social integration of patients suffering from neurological disorders s...Show More

Abstract:

Emotion classification using EEG signal processing has the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) or the acute stages of Alzheimer's disease. One important challenge to the implementation of high-fidelity emotion recognition systems is the inadequacy of EEG data in terms of Signal-to-noise ratio (SNR), duration, and subject-to-subject variability. In this paper, we present a novel, integrated framework for semi-generic emotion detection using (1) independent component analysis for EEG preprocessing, (2) EEG subject clustering by unsupervised learning, and (3) a convolutional neural network (CNN) for EEG-based emotion recognition. The training and testing data was built using the combination of two publicly available repositories (DEAP and DREAMER), and a local dataset collected at Khalifa University using the standard International Affective Picture System (IAPS). The CNN classifier with the proposed transfer learning approach achieves an average accuracy of 70.26% for valence and 72.42% for arousal, which are superior to the reported accuracies of all generic (subject-independent) emotion classifiers.
Date of Conference: 23-27 July 2019
Date Added to IEEE Xplore: 07 October 2019
ISBN Information:

ISSN Information:

PubMed ID: 31945992
Conference Location: Berlin, Germany

References

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