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E2ENNet: An end-to-end neural network for emotional brain-computer interface.

Zhichao Han1, Hongli Chang2, Xiaoyan Zhou1

  • 1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China.

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|August 29, 2022
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Summary

This study introduces an end-to-end neural network (E2ENNet) for improved electroencephalography (EEG) emotion recognition. E2ENNet simplifies the process and achieves state-of-the-art accuracy, paving the way for practical brain-computer interfaces.

Keywords:
depthwise separable convolutionelectroencephalogram (EEG)emotional brain-computer interfacelong short-term memoryneurocognitive

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional electroencephalography (EEG) emotion recognition involves complex, multi-step feature engineering and classification.
  • This complexity limits the practical application of EEG-based emotion recognition systems.
  • Existing methods struggle with efficiency and direct feature extraction from raw EEG data.

Purpose of the Study:

  • To propose an end-to-end neural network, E2ENNet, for simplified and effective EEG emotion recognition.
  • To overcome the limitations of traditional step-by-step approaches in EEG emotion recognition.
  • To develop a methodology for a plug-and-play emotional brain-computer interface system.

Main Methods:

  • Preprocessing of raw EEG signals using baseline removal and sliding window slicing.
  • Feature extraction via convolution blocks.
  • Correlation analysis of features using a Long Short-Term Memory (LSTM) network.
  • Emotion classification using a softmax function.

Main Results:

  • E2ENNet achieved state-of-the-art accuracy on three public datasets.
  • Achieved 96.28% accuracy for 2-category emotion recognition on the DEAP dataset.
  • Achieved 98.1% accuracy for 2-category emotion recognition on the DREAMER dataset.
  • Achieved 41.73% accuracy for 7-category emotion recognition on the MPED dataset.

Conclusions:

  • E2ENNet effectively extracts more discriminative features directly from raw EEG signals.
  • The proposed network simplifies the EEG emotion recognition pipeline.
  • This research offers a viable methodology for implementing plug-and-play emotional brain-computer interface systems.