Abstract
The challenge of EEG-based emotion recognition had inspired researchers for years. However, lack of efficient technologies and methods of EEG signal analysis hindered the development of successful solutions in this domain. Recent advancements in deep convolutional neural networks (CNN), facilitating automatic signal feature extraction and classification, brought a hope for more efficient problem solving. Unfortunately, vague and subjective interpretation of emotional states limits effective training of deep models, especially when binary classification is performed basing on datasets with non-bimodal distribution of emotional state ratings. In this work we revisited the methodology of emotion recognition, proposing to use regression instead of classification, along with appropriate result evaluation measures based on mean absolute error (MAE) and mean squared error (MSE). The advantages of the proposed approach are clearly demonstrated on the example of the well-established and explored DEAP dataset.
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Opałka, S., Stasiak, B., Wosiak, A., Dura, A., Wojciechowski, A. (2021). EEG-Based Emotion Recognition – Evaluation Methodology Revisited. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_40
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