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Decoding Non-Invasive Electroencephalography Signal via a Two-Discriminator Adversarial Network.

Xuguang Liu1, Changyi Yu2, Ye Li1

  • 1Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.

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|February 13, 2026
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Summary
This summary is machine-generated.

This study introduces a novel Two-Discriminator Domain Adversarial Neural Network (TD-DANN) for accurate emotion decoding using electroencephalography (EEG) signals. The method enhances generalized and individualized emotion feature representations, improving non-invasive biosensing accuracy.

Keywords:
deep learningdomain adversarial neural networkelectroencephalographyemotion recognitionnon-invasive

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Biosignal Processing and Machine Learning

Background:

  • Electroencephalography (EEG) records brain activity for emotion decoding but faces challenges due to individual brain differences and complex channel interrelationships.
  • Existing non-invasive biosensing methods struggle with accuracy in emotion recognition because of inter-subject variability and intricate EEG signal patterns.

Purpose of the Study:

  • To propose a Two-Discriminator Domain Adversarial Neural Network (TD-DANN) for enhanced emotion decoding from EEG signals.
  • To achieve more generalized and individualized emotion feature representations through adversarial learning for improved accuracy in non-invasive biosensing.

Main Methods:

  • Utilized graph convolution to extract features from EEG signals, modeling channels as graph nodes with a dynamically learned adjacency matrix.
  • Implemented a two-discriminator approach: a domain discriminator for universal features and an individual discriminator for personalized emotion adaptability.
  • Employed adversarial learning to minimize feature distribution differences between source and target domains, enhancing feature universality and individual consistency.

Main Results:

  • Achieved high subject-dependent recognition accuracies of (98.45 ± 2.38)% on the SEED dataset and (84.40 ± 8.70)% on the SEED-IV dataset.
  • Demonstrated strong subject-independent recognition accuracies of (89.45 ± 5.87)% on the SEED dataset and (77.13 ± 7.97)% on the SEED-IV dataset.
  • The TD-DANN method significantly improved emotion decoding accuracy in both subject-dependent and subject-independent scenarios across different datasets.

Conclusions:

  • The proposed TD-DANN effectively addresses the challenges of individual differences and complex channel interrelationships in EEG-based emotion decoding.
  • The method's ability to learn generalized and individualized features validates its effectiveness for accurate and adaptive emotion recognition in non-invasive biosensing.
  • The experimental results confirm the TD-DANN's superior performance, highlighting its potential for practical applications in affective computing and brain-computer interfaces.