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Related Concept Videos

Long-Term Memory01:18

Long-Term Memory

Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...

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Emotion recognition of CNN bidirectional long short-term memory with center and Softmax loss function (CNN-BiLSTM-CS)

Xiaodan Zhang1, Shuyi Wang1, Yige Li1

  • 1School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, China.

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|August 27, 2025
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Summary

This study introduces CNN-BiLSTM-CS for advanced emotion recognition using electroencephalogram (EEG) signals. The novel method enhances feature extraction, achieving higher accuracy in classifying emotions from brain activity.

Keywords:
CNN-BiLSTM-CSEmotion recognitionFeature extractionLoss function

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are crucial for emotion recognition.
  • Traditional methods struggle with extracting highly distinguishable features.
  • Existing models like unidirectional LSTM and Softmax have limitations in feature extraction.

Purpose of the Study:

  • To propose a novel deep learning model, CNN-BiLSTM-CS, for improved EEG-based emotion recognition.
  • To address the limitations of traditional unidirectional LSTM and Softmax models.
  • To enhance the extraction of discriminative features from EEG signals.

Main Methods:

  • Utilizing a Convolutional Neural Network (CNN) combined with a Bidirectional Long Short-Term Memory (BiLSTM) network.
  • Implementing a joint loss function with Center and Softmax (CS) to minimize intra-class distance and maximize inter-class distance.
  • Evaluating the model on the DEAP and SEED datasets.

Main Results:

  • Achieved average accuracies of 94.22% for valence and 92.16% for arousal on the DEAP dataset.
  • Demonstrated a significant improvement of nearly 6% compared to CNN-LSTM.
  • Obtained a triple categorization accuracy of 95.45% on the SEED dataset.

Conclusions:

  • The proposed CNN-BiLSTM-CS model significantly enhances the recognition performance of deep EEG features.
  • The improved network structure and combined loss function contribute to superior emotion recognition capabilities.
  • This approach offers a promising direction for more accurate and robust EEG-based emotion recognition systems.