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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Area of Science:

    • Affective Computing
    • Neuroscience
    • Machine Learning

    Background:

    • Emotion recognition is crucial in Affective Computing.
    • Electroencephalogram (EEG) signals are valuable for emotion detection.
    • Deep learning, particularly neural networks, is increasingly used for EEG-based emotion recognition.

    Purpose of the Study:

    • To propose an effective similarity learning network for EEG-based emotion recognition.
    • To improve the discriminative power of feature embeddings using pairwise constraints.
    • To enhance overall emotion classification performance.

    Main Methods:

    • Utilized a bidirectional long short-term memory (BLSTM) network.
    • Incorporated a pairwise constraint loss function combined with traditional supervised classification loss.
    • Evaluated the model on the benchmark SEED EEG emotion dataset.

    Main Results:

    • The pairwise constraint loss significantly improved emotion classification performance.
    • The proposed method achieved a mean accuracy of 94.62% on the SEED dataset.
    • Outperformed existing state-of-the-art emotion classification approaches.

    Conclusions:

    • The developed similarity learning network is effective for EEG-based emotion recognition.
    • Pairwise constraint loss is a valuable technique for learning discriminative features.
    • The model demonstrates superior performance in classifying emotions from EEG data.